Item
Aquilion Lightning (TSX‐036A/7) V10.2
Aquilion Lightning (TSX‐036A/1) V8.4

Anatomical Region
AIDR 3D (Whole Body)
AIDR 3D (Whole Body)

AIDR 3D
AIDR 3D Enhanced
Quantum Denoising Smoothing (QDS)

Console CKCN‐020C
Reconstruction processing system
(N/A)

‐ Better Low‐contrast Detectability than

Operating System
Microsoft Windows 10
Microsoft Windows 10

subject and the predicate device is included below.


3930 Freedom Cir., Ste 101
Santa Clara, CA 95054


Proprietary Name
WRDensity by Whiterabbit.ai

Automated Radiological Image Processing Software


Classification Name
System, Image Processing, Radiological


Automated Radiological Image
Processing Software

Picture archiving and
communication system


WRDensity is a software
application intended for use with
compatible full field digital
mammography and digital breast
tomosynthesis systems.
WRDensity provides an ACR
BI-RADS Atlas 5th Edition breast
density category to aid
interpreting physicians in the
assessment of breast tissue
composition. WRDensity
produces adjunctive information.
It is not a diagnostic aid.

Densitas densityai™ is a software
application intended for use with
compatible full field digital
mammography and digital breast
tomosynthesis systems. Densitas
densityai™ provides an ACR
BI-RADS Atlas 5th Edition breast
density category to aid interpreting
physicians in the assessment of
breast tissue composition. Densitas
densityai™ produces adjunctive
information. It is not a diagnostic
aid.

Symptomatic and
asymptomatic women
undergoing
mammography

End Users
Interpreting Physicians
Interpreting Physicians

FFDM
Hologic Selenia Dimensions
Hologic Lorad Selenia
Synthetic 2D
Hologic C-View

FFDM
Hologic Selenia Dimensions
Hologic Lorad Selenia
GE Senographe Essential
GE Senographe Pristina
Siemens MAMMOMAT
Inspiration
Siemens MAMMOMAT Novation
DR
Siemens MAMMOMAT Fusion
Siemens MAMMOMAT
Inspiration Prime
Siemens MAMMOMAT
Revelation

Table 5.2 Indications and Technological Characteristics Comparison


DICOM digital mammography
images – For Presentation; RCC,
LCC, RMLO, LMLO

BIRADS 5th Ed.
For each patient:
Whiterabbit.ai WRDensity Breast
Density Level, and Breast Density
Level Probability

BIRADS 5th Ed.
For each patient:
Densitas densityai™ breast density
grade

4-category breast density scale
from 5th Ed. ACR BI-RADS
Atlas 2013

DICOM Structured
Report and Secondary
Capture
Text labels presented in a
radiologist’s PACS
and RIS patient worklist.

Deployment
Virtual Machine Software
Standalone computer


Legal Manufacturer
Perspectum Ltd.
Perspectum Diagnostics Ltd.

NDeuvmicbee Nr ame
LiverMultiScan (LMSv4)
LiverMultiScan (LMSv3)

System, Nuclear Magnetic
Resonance Imaging


Comparison of Subject and Predicate Device

Characteristic
LMSv4 (Subject device)
LMSv3 (Predicate device)

“LiverMultiScan (LMSv4) is indicated for use as
a magnetic resonance diagnostic device
software application for non-invasive liver
evaluation that enables the generation, display
and review of 2D magnetic resonance medical
image data and pixel maps for MR relaxation
times.
LiverMultiScan (LMSv4) is designed to utilize
DICOM 3.0 compliant magnetic resonance
image datasets, acquired from compatible MR
systems, to display the internal structure of the
abdomen including the liver. Other physical
parameters derived from the images may also
be produced.
LiverMultiScan (LMSv4) provides a number of
tools, such as automated liver segmentation
and region of interest (ROI) placements, to be
used for the assessment of selected regions of
an image. Quantitative assessments of
selected regions include the determination of
triglyceride fat fraction in the liver (PDFF), T2*
and iron-corrected T1 (cT1) measurements.
T2* may be optionally computed using the
DIXON or LMS MOST methods.
These images and the physical parameters
derived from the images, when interpreted by
a trained clinician, yield information that may
assist in diagnosis.”

“LiverMultiScan (LMSv3) is indicated for use as
a magnetic resonance diagnostic device
software application for non-invasive liver
evaluation that enables the generation, display
and review of 2D magnetic resonance medical
image data and pixel maps for MR relaxation
times.
LiverMultiScan (LMSv3) is designed to utilize
DICOM 3.0 compliant magnetic resonance
image datasets, acquired from compatible MR
systems, to display the internal structure of
the abdomen including the liver. Other
physical parameters derived from the images
may also be produced.
LiverMultiScan (LMSv3) provides a number of
tools, such as automated liver segmentation
and region of interest (ROI) placements, to be
used for the assessment of selected regions of
an image. Quantitative assessments of
selected regions include the determination of
triglyceride fat fraction in the liver (PDFF), T2*
and iron-corrected T1 (cT1) measurements.
PDFF may optionally be computed using the
LMS IDEAL or three-point DIXON
methodology.
These images and the physical parameters
derived from the images, when interpreted by
a trained clinician, yield information that may
assist in diagnosis.”

Patients suitable to undergo an MRI scan and

Population
not contra-indicated for MRI.
not contra-indicated for MRI.

Device User
Device User
Trained Perspectum operator
Trained Perspectum operator
Trained Perspectum operator
Trained Perspectum operator

An interpreting clinician or healthcare
practitioner.

Installation of LMSv4 is controlled and installed
on general purpose workstations at
Perspectum’s image analysis centre, by
specialist members of staff.

Installation of LMSv3 is controlled and
installed on general purpose workstations at
Perspectum’s image analysis centre by
specialist members of staff.


LMSv4 is a standalone software device that is

LMSv3 is a standalone software device that is

intended to be installed on general use
intended to be installed on general use

workstations at Perspectum’s image analysis

centre. The intended device users will log on to

the workstations, access the device, and use

to the workstations, access the device, and

use the device on general-use HD monitors.

LMSv4 is a post-processing software, the

LMSv3 is a post-processing software, the

intended device users are trained internal

Perspectum operators. Operators use LMS to

conduct quantitative analysis of liver tissue

characteristics to produce a report.
characteristics to produce a report.

The end-users for the output from the device,

the pdf report, are clinicians who receive and

interpret LMSv4 reports through the QAS.

interpret LMSv3 reports through the QAS.

Anatomical
Abdomen, Liver
Abdomen, Liver
Abdomen, Liver
Abdomen, Liver

Software only application. The device, a

standalone software application, does not

deliver, monitor or depend on energy
deliver, monitor or depend on energy

delivered to or from patients.
delivered to or from patients.

LMS is a standalone software application that
imports MR data sets encompassing the
abdomen, including the liver. Visualisation and
display of 2D multi-slice, spin-echo MR data
can be analysed, and quantitative metrics of
tissue characteristics are then reported.
Datasets imported into LMS are DICOM 3.0
compliant, reported metrics are independent
of the MRI equipment vendor.

imports MR data sets encompassing the
imports MR data sets encompassing the


Comparison of Subject and Predicate Device

Characteristic
LMSv4 (Subject device)
LMSv3 (Predicate device)

• ROI placed method on the cT1 map with
IQR and median metrics from the placed
ROI’s potentially across multiple acquired
slices.
T2*
• ROI placed method on the T2* map with
IQR and median metrics from the placed
ROI’s potentially across multiple acquired
slices.
• T2* parametric maps are calculated from
the MOST method or the three-point
DIXON method.
PDFF
• Full liver segmentation of the PDFF
parametric map where IQR and median
metrics are reported from the
segmentation.
• ROI placed method on the PDFF map with
IQR and median metrics from the placed
ROI’s potentially across multiple acquired
slices.
• PDFF parametric maps are calculated
using the LMS IDEAL method 1

• ROI placed method on the cT1 map with
IQR and median metrics from the placed
ROI’s potentially across multiple acquired
slices.
T2*
• ROI placed method on the T2* map with
IQR and median metrics from the placed
ROI’s potentially across multiple acquired
slices.
• T2* parametric maps are calculated from
the three-point DIXON method.
PDFF
• Full liver segmentation of the PDFF
parametric map where IQR and median
metrics are reported from the
segmentation.
• ROI placed method on the PDFF map with
IQR and median metrics from the placed
ROI’s potentially across multiple acquired
slices.
• PDFF parametric maps can be calculated
using either the LMS IDEAL method 1 or
the three-point DIXON method 2

T1, iron-corrected T1 (cT1) and T2* mapping.

Utilizes MR images that exploit the difference
in resonance frequencies between hydrogen
nuclei in water and triglyceride fat using the
LMS IDEAL method.

Utilizes MR images that exploit the difference
in resonance frequencies between hydrogen
nuclei in water and triglyceride fat using either
the LMS IDEAL method or three-point DIXON
method.

LMSv4 supports automatic multi-slice full liver
segmentation of the cT1 and PDFF parametric
map, use of this functionality is at the
discretion of the operator instead or in
combination with the ROI based method.
The cT1 segmented liver is presented in colour
level window, while the rest of the cT1 image
is presented in greyscale level window with
ducts and liver vasculature excluded from the
segmented volume.

LMSv3 supports automatic multi-slice full liver
segmentation of the cT1 and PDFF parametric
map, use of this functionality is at the
discretion of the operator instead or in
combination with the ROI based method.
The cT1 segmented liver is presented in colour
level window, while the rest of the cT1 image
is presented in greyscale level window with
ducts and liver vasculature excluded from the
segmented volume.


Median and interquartile range measurements
created from a cross sectional slice of liver
tissue. For each parametric map, statistics
from multiple Regions of Interest (ROIs) –
potentially placed across multiple slices – are
summarised.

Iron corrected T1 (cT1), T2* and Proton
Density Fat Fraction (PDFF) parametric maps
can be created from all supported scanners.
PDFF is quantified using the LMS IDEAL
method. Parametric maps of T2* may be
optionally be computed using either the
three-point DIXON method or the LMS MOST
method.

Iron corrected T1 (cT1), T2* and Proton
Density Fat Fraction (PDFF) parametric maps
can be created from all supported scanners.
PDFF is quantified using the LMS IDEAL
method or the three-point DIXON method.

Numerous views within the LMSv4 interface
can be used to assist in analysis, Iron-
corrected T1 (cT1), T2* and triglyceride fat
(also known as Proton Density Fat Fraction
(PDFF)) parametric maps can be created from
all supported scanners. R2 maps can also be
utilised to assess the quality of the map fitting.
Iron- corrected T1 (cT1) displayed using LMSv4
colourmap, designed to have maximum
contrast on liver parenchymal tissue.

Numerous views within the LMSv4 interface
can be used to assist in analysis, iron
corrected T1 (cT1), T2* and triglyceride fat
(also known as Proton Density Fat Fraction
(PDFF)) parametric maps can be created from
all supported scanners.
Iron corrected T1 (cT1) displayed using the
LMSv3 colourmap, designed to have maximum
contrast on liver parenchymal tissue.

DICOM 3.0 compliant MR data from supported
MRI scanners.

Quantified metrics and images derived from

the analysis conducted of liver tissue
the analysis conducted of liver tissue

characteristic on parametric maps are collated

into a report for evaluation and interpretation

collated into a report for evaluation and

by a clinician.
interpretation by a clinician.

Installation of LMSv4 is controlled and is

Installation of LMSv3 is controlled and is

installed on general purpose workstations that

meet the minimum technical requirements at

requirements at PD’s image analysis centre by

specialist members of staff.
specialist members of staff.

Device performance was assessed with
purpose-built phantoms and in-vivo acquired
data from volunteers covering a range of
physiological values for cT1, T2* and PDFF.

Validated across all listed supported
manufacturers and field strengths.


Comparison of Subject and Predicate Device

Characteristic
LMSv4 (Subject device)
LMSv3 (Predicate device)

Materials
Not applicable, standalone software.
Not applicable, standalone software.

Biocompatibility
Not applicable, standalone software.
Not applicable, standalone software.

Sterility
Not applicable, standalone software.
Not applicable, standalone software.

Electrical Safety
Not applicable, standalone software.
Not applicable, standalone software.

Chemical Safety
Not applicable, standalone software.
Not applicable, standalone software.

Thermal Safety
Not applicable, standalone software.
Not applicable, standalone software.

Radiation Safety
Not applicable, standalone software.
Not applicable, standalone software.


IDEAL PDFF
(lowe-r3 t.h8a0n t og r6o.u08n%d truth
lower-1 t.h3a9n t ogr 5o.u5n8d% t ruth


IDEAL PDFF (Segmentation)
-1.31 to 1.34 %
-2.94 to 2.53%


Length between two points,
for both curved lines (splines)
and straight lines, including
the diameter (including min,
max, average) resulting from
closed splines and depth of the
LAA

Diameter & depth of
LAA landing zone
(LAA module);
distance between
points of interest

The perimeter of a contour
(closed spline)

Perimeter of LAA
landing zone (LAA
module); perimeter of
other contours of
interest

Area of LAA landing
zone (LAA module);
area of other contours
of interest

The angle of an
object/structure of interest

measurement application for which it is used.


• Segmentation of
cardiovascular structures
• Visualization and image
reconstruction techniques: 2D
review, Volume Rendering,
MPR
• Simulation of TEE views, ICE
views, and fluoroscopic
rendering
• Measurement and annotation
tools
• Reporting tools
TruPlan’s intended patient
population is comprised of adult
patients.

(calcium scoring) in the
coronary arteries
To facilitate the above, 3mensio
provides general functionality such
as:
• Segmentation of cardiovascular
structures
• Visualization and image
reconstruction techniques: 2D
review, Volume Rendering,
MPR, Curved MPR, Stretched
CMPR, slabbing, MIP, AIP,
MinIP
• Measurement and annotation
tools
• Reporting tools
• Automatic and manual
centerline detection

CT data in DICOM format (vendor
independent)

• Study/series previewing
• Exporting
• Deleting
• Anonymizing
• Search

• Fluoroscopy (grayscale
3D rendering), to visualize
relationship among LAAC
procedure relevant
anatomical structures
• TEE, to provide similar
views to intraprocedural
TEE
• ICE, to provide similar
views to intraprocedural
ICE

• Grayscale 3D rendering, to
visualize relationship
among LAAC procedure
relevant anatomical
structures
• TEE, to provide similar
views to intraprocedural
TEE


Image assessment –
other visualization
functionality

• 2D
• 3D (with manual & semi-
automatic segmentation)
• 4D (cine)
• MPR
• Annotations
• Curved MPR
• Stretch CMPR
• Slabbing
• MIP
• AIP
• MinIP
• Centreline extraction
• Calcium scoring

Image assessment –
measurement functionality

• Distance (length, diameter,
perimeter)
• Area
• Angle
• Signal intensity
• Coordinates
• Volume

• Patient/study information
• Screenshots
• Measurements
• Free text
• Device sizing table (for
reference only) for LAA
procedure

• Patient/study information
• Screenshots
• Measurements
• Free text
• Device-specific reports for
procedures covered in
intended use

Operating system
Microsoft Windows
Microsoft Windows


AI Metrics LLC
432 Renaissance Dr. Hoover, AL 35226
Phone: 205-573-3332
Website: www.aimetrics.com

Dr. Andrew Smith, Founder
Email: andrew@aimetrics.com
Phone: (769) 610-6235

Bob Jacobus, COO
Email: bob@aimetrics.com
Phone: (205) 639-8618


Picture Archiving and Communications System per 21 CFR 892.2050


5.3 Predicate Device to which Equivalence is claimed


Intended Use
Subject (AI Metrics)
Predicate (K142647)

Thin client medical image viewer.
Yes.
Yes.

Software only medical device deployed within
a customer’s IT infrastructure or on virtualized
server technology.

Application supports anatomical datasets,
such as CT and MR.

To be used for viewing, manipulation,
communication, storage, 3D-visualization and
comparison of medical images from multiple
imaging modalities and/or multiple time points.

Provides tools to help the user assess and
document the extent of a disease and/or the
response to therapy in accordance with user
selected standards.

Supports the interpretation and evaluation of
examinations and follow up documentation of
findings within healthcare institutions, for
example, in Radiology, Oncology, and other
Medical Imaging environments.

Yes. Includes
Nuclear Medicine
environments.

The medical professional retains the ultimate
responsibility for making the pertinent
diagnosis based on their standard practices.
The software is a complement to these
standard procedures.


Technological Characteristic
Subject (AI Metrics)
Mint Lesion (K142647)

Software-based Picture Archiving and
Communication System (PACS) used with
general purpose computing hardware.

Windows 64 / native or
virtualized Microsoft
Windows platform.

Linux / native or
virtualized Linux
platform.

Image navigation tools (pan, zoom, scroll,
window/level)

Measurement tools (linear, ROI, HU)
Yes.
Yes.

Automatic long and short axis calculations

User controls functions with a system of
interactive menus and tools

Semi-automatic lesion segmentation tools

Anatomical location labelling tools
Yes.
Yes.

Display output of measurements and
anatomical location information

Tabulation and summation of
measurements, lesion categorization and
standard evaluation in accordance with
selected criteria


Image co-registration for viewing images
from different time points


GE Healthcare (GE Healthcare (Tianjin) Company Limited)
No. 266 Jingsan Road,Tianjin Airport Economic Area
Tianjin, China 300308

Qiang Ding
Regulatory Affairs Leader
Phone: +86 13311385163
Email: Ding.Qiang@ge.com

Glen Sabin
Regulatory Affairs Director
Phone: 262-894-4968
Email: Glen.Sabin@ge.com

Common/Usual Name:
Magnetic Resonance Diagnostic Device

Classification Names:
Magnetic Resonance Diagnostic Device

Predicate Device:
SIGNA Artist (K163331)

The SIGNA Artist system is a whole body magnetic resonance scanner
designed to support high resolution, high signal-to-noise ratio, and short scan
times. The system features a superconducting magnet. The data acquisition
system accommodates up to 128 independent receive channels in various
increments and multiple independent coil elements per channel during a
single acquisition series. The system uses a combination of time varying
magnetic fields (gradients) and RF transmissions to obtain information
regarding the density and position of elements exhibiting magnetic
resonance. The system can image in the sagittal, coronal, axial, oblique, and
double oblique planes, using various pulse sequences and reconstruction
algorithms.
This 510(k) submission is for the SIGNA Artist 1.5T MR system, and has
been triggered by the addition of the AIR Recon DL software feature.

In accordance with 21 CFR 807.92 the following summary of information is provided:


The AIR Recon DL feature has been previously cleared for use on the
SIGNA Premier 3T system through K193282, which is used as a reference
device for this submission.

The Indications for Use statement for the proposed device is identical to
that of the predicate device:
The SIGNA Artist system is a whole body magnetic resonance
scanner designed to support high resolution, high signal-to-noise
ratio, and short scan times. It is indicated for use as a diagnostic
imaging device to produce axial, sagittal, coronal, and oblique
images, spectroscopic images, parametric maps, and/or spectra,
dynamic images of the structures and/or functions of the entire
body, including, but not limited to, head, neck, TMJ, spine, breast,
heart, abdomen, pelvis, joints, prostate, blood vessels, and
musculoskeletal regions of the body. Depending on the region of
interest being imaged, contrast agents may be used.
The images produced by the SIGNA Artist system reflect the
spatial distribution or molecular environment of nuclei exhibiting
magnetic resonance. These images and/or spectra when
interpreted by a trained physician yield information that may assist
in diagnosis.
The addition of the AIR Recon DL feature does not impact the intended
use of the SIGNA Artist system.

Many of the technological characteristics of the proposed SIGNA Artist
system are unchanged from the predicate device. The SIGNA Artist
system has been introduced an additional configuration with IPM magnet.
There are no changes to gradient, and RF subsystems compared to the
predicate K163331. Key performance specifications (such as magnet
stability, maximum gradient strength and slew rate, etc.) for the system
are also unchanged.
The software used on the proposed SIGNA Artist system has been
modified to include the AIR Recon DL feature. The User interface
provides operators of the system with new options for selecting AIR
Recon DL and adjusting the associated level of image noise reduction.
The resulting images can have higher SNR and improved sharpness
compared to images reconstructed without AIR Recon DL.
AIR Recon DL has been previously cleared for use with GE Healthcare’s
3T SIGNA Premier system through K193282. Due to the technical
similarities, SIGNA Premier (K193282) is used as a reference device for
this submission.


Determination of
Substantial
Equivalence:

Summary of Non-Clinical Tests:
The AIR Recon DL feature has undergone the performance testing.
These tests were designed to evaluate the AIR Recon DL feature and its
impact on image quality, including SNR, sharpness, low contrast
detectability, and noise spectral content. Analysis was performed to
confirm that the feature does not introduce significant bias that might
impact quantitative measurements based on signal intensity. The
influence of motion during image acquisition on the performance of AIR
Recon DL was also evaluated.
The nonclinical testing demonstrated that AIR Recon DL does improve
SNR and image sharpness while maintaining low contrast detectability
and having minimal impacts to noise spectral content, average signal
intensity, or the appearance of motion artifacts. AIR Recon DL was also
able to maintain image SNR and did not sacrifice sharpness for images
acquired with a reduced scan time. The nonclinical testing passed the
defined acceptance criteria, and did not identify any adverse impacts to
image quality or other concerns related to safety and performance.
Summary of Clinical Tests:
Objective measures of in vivo images were analyzed to confirm that AIR
Recon DL improves SNR and image sharpness for typical clinical use
cases.
Additionally, sample images from clinically indicated scans were
evaluated both with and without the AIR Recon DL feature. These
samples included images using exogenous contrast and images involving
pathology spanning a variety of anatomies and pulse sequences.
Radiologists were asked to rate the images, and to comment on any
notable aspects related to image quality. This study showed that the AIR
Recon DL feature provides images with equivalent or better image
quality, lesion conspicuity is maintained, and that the radiologists
preferred the AIR Recon DL images for clinical use.

Conclusion Drawn from
Performance Testing:

The nonclinical and clinical testing demonstrated that AIR Recon DL
satisfies the product claims of improved SNR and image sharpness, and
can enable shorter scan times while maintaining SNR and image
sharpness.
The proposed SIGNA Artist system with AIR Recon DL has been
developed under GE Healthcare’s quality system and is at least as safe
and effective as the legally marketed predicate. The performance testing
did not identify any new hazards, adverse effects, or safety and


101 Greenwich St, Suite 11C
New York, NY 10006


Common Name
System, Image Processing, Radiological

21 CFR 892.2050 Picture Archiving And Communications
System


Feature
Cleerly Labs v2.0 Device
Cleerly Labs Device

Image Input
DICOM 3.0 Compliant (or higher)
DICOM 3.0 Compliant (or higher)

Study Analysis Tools –
Editing/ Visualization


Cleerly Labs v2.0 Device
Cleerly Labs Device


Digital Imaging And
Communications In Medicine
(DICOM) Set

Medical Device Software - Software
Life Cycle Processes [Including
Amendment 1 (2016)]

Medical Devices - Application Of
Risk Management To Medical
Devices

Medical Devices - Part 1: Application
Of Usability Engineering To Medical
Devices [Including
CORRIGENDUM 1 (2016)]

Principles for medical device security
– Risk management

Table 5: Standards applied to device development


using its scripting interface to facilitate streamlined transmission of DICOM files and plan parameters.


planning workflow. Out of all the plans tested, only one plan took more iterations (one more) using


Reader
Category
Years of relevant experience


Table 2: Patient characteristics for the Validation Set.


represented, and a variety of slice spacings up to 2.5mm.


Dose Information
N
Median Range
N
Median Range
N Total


the attributes suggested in FDA’s website guidance for this comparison.


SubtleMR is an image
processing software
that can be used for
image enhancement in
MRI images. It can be
used to reduce image
noise for head, spine,
neck, and knee MRI,
or increase image
sharpness for non-
contrast-enhanced
head MRI.

BoneMRI is an image
processing software
that can be used for
image enhancement in
MR images. It can be
used to visualize the
bone structures in MR
images with enhanced
contrast with respect
to the surrounding soft
tissue

Similar –
Intended uses are
the same for Image
enhancements for
MRI. But the
intended use
differences does
not affect the
safety and
effectiveness of the
device when used
as labeled and is
similar to the
predicate use.

Target Population
Adults
Adults
The same


Device Nature
Software package
Software package
The same

SubtleMR software
implements an image
enhancement
algorithm using
convolutional neural
network based
filtering. Original
images are enhanced
by running through a
cascade of filter

MRIguidance
software implements
an image
enhancement
algorithm using
convolutional neural
network. Original
images are enhanced
by running them
through a cascade of

Different –
The algorithm while
using similar
methodology, uses
different filters and
outputs to enhance
the image. The
difference does not
affect the safety and


banks, where
thresholding and
scaling operations are
applied. Separate
neural network based
filters are obtained
for noise reduction
and sharpness
increase. The
parameters of the
filters were obtained
through an image
guided optimization
process.

filter banks, where
thresholding and
scaling operations
are applied. Separate
neural network-
based filters are
obtained to assign a
Hounsfield Unit
(HU) value to a
single volume
element, based on
intensity and
contextual
information. The
parameters of the
model were obtained
through an algorithm
development
pipeline.

effectiveness of the
device when used as
labeled and is similar
to the predicate use.

None – enhanced
images are viewed on
existing PACS
workstations

The software
operates on DICOM
files on the file
system, enhances the
images, and stores
the enhanced images
on the file system.
The receipt of
original DICOM
image files and
delivery of enhanced
images as DICOM
files depends on other
software systems.
Enhanced images co-
exist with the original
images.

The software
operates on DICOM
files on the file
system, enhances the
images, and stores
the enhanced images
on the file system.
The receipt of
original DICOM
image files and
delivery of enhanced
images as DICOM
files depends on
other software
systems. Enhanced
images co-exist with
the original images.


Classification
Class II, LLZ, 21 CFR 892.2050
Class II, LLZ, 21 CFR 892.2050

Automatic labeling, spatial
measurement, and volumetric
quantification of brain structures
from a set of low-field MR
images and returns annotated
and segmented images, color
overlays, and reports.

Automatic labeling, visualization
and volumetric quantification of
segmentable brain structures and
lesions from a set of MR images.
Volumetric data may be
compared to reference percentile
data

▪ Automated measurement of
brain tissue volumes and
structures
▪ Automatic segmentation and
quantification of brain structures
using machine learning

• Automated measurement of
brain tissue volumes and
structures and lesions
• Automatic segmentation and
quantification of brain structures
using a dynamic probabilistic
neuroanatomical atlas, with age
and gender specificity, based on
the MR image intensity

MR images are automatically
sent to BrainInsight and
processed images are
automatically returned in
approximately 7 minutes.

User manually sends MR images
to NeuroQuant and processed
images are automatically
returned in approximately 7
minutes.


User Interface / Physical
Characteristics

• No software required
• Operates in a serverless cloud
environment
• User interface through PACS
(multiple vendors)

• Software package installed on
User hardware
• Operates on off-the-shelf
hardware (multiple vendors)
• User interface through the
software package

Automated internal pipeline that
performs:
- segmentation
- volume calculation
- distance measurement
- numerical information display

Automated internal pipeline that
performs:
- artifact correction
- segmentation
- lesion quantification
- volume calculation
- report generation

▪ MRI scanner: Hyperfine FSE
MRI scans acquired with
specified protocols
▪ Supports DICOM format as
input

▪ MRI scanner: 3D T1 MRI
scans acquired with specified
protocols
▪ NeuroQuant Supports DICOM
format as input

Provides volumetric
measurements of brain structures
▪ Includes segmented color
overlays and morphometric
reports
▪ Supports DICOM format as
output of results that can be
displayed on DICOM
workstations and Picture
Archive and Communications
Systems

Provides volumetric
measurements of brain structures
and lesions
▪ Includes segmented color
overlays and morphometric
reports
▪ Automatically compares results
to reference percentile data and
to prior scans when available
▪ Supports DICOM format as
output of results that can be
displayed on DICOM
workstations and Picture
Archive and Communications
Systems

Automated quality control
functions
▪ Tissue contrast check
▪ Scan protocol verification
▪ Atlas alignment check
▪ Results must be reviewed by a
trained physician


Proprietary Name
Efficient Care 3D Planning

Picture Archiving and Communication System

The HealthJOINT device is substantially equivalent to the following device:


Predicate Device:
Efficient Care 3D Planning
(K183544)

The Zebra HealthJOINT device is
a software tool for 3D
reconstruction of bones from a set
of 2D radiographs. The device is
intended for assisting clinicians
in the preoperative planning of
knee orthopedic surgical
procedures. Zebra’s
HealthJOINT analyzes cases
using an artificial intelligence
algorithm for the 3D model
reconstruction. In addition to the
model, the software provides a list
of anatomical landmarks with
their position on the 3D model.
The result is made available via a
3rd parties’ software interface for
further display and analysis of the
3D bone model.
Clinical judgement and
experience are required to
properly use the models produced
by this software.

Efficient Care 3D Planning is
software indicated for assisting
orthopedic surgeons in preoperative
planning of knee orthopedic
surgeries. The software allows for
the overlaying of 3D/2D implant
models and for the visualization of
the radiological images and 3D
reconstruction of bones, and
includes tools for performing
measurements on the images or 3D
model of bones, and for selecting
and positioning the implant model.
Clinical judgments and experience
are required to properly use the
software.
Efficient Care 3D Planning is to be
used with the following fixed
bearing knee replacement systems
in accordance with their indications
and contraindications: NexGen®
CR, NexGen CR-Flex, NexGen
CR-Flex Gender, NexGen LPS,
NexGen LPS-Flex, NexGen
LPSFlex Gender, Persona® CR,

Similar. In
addition to the 3D
reconstruction and
3D/2D overlay for
the visualization
of radiological
images, the
predicate device
also provide tools
for performing
measurements and
for selecting the
implant model.

A comparison of the technological characteristics with the predicate device is summarized below.


Persona PS, Vanguard® CR, and
Vanguard PS.

Prespecified clinical users
(clinicians)

Output
3D bone reconstruction
3D bone reconstruction
Same

Different, but do
not raise
questions of
safety and
effectiveness.


User can inspect and edit prostate
segmentation.

User can enter PSA value and compute
PSA density

User can create and update regions of
interest (ROIs)

User can add and update annotations (text)
to analysis

User can manually set a PI-RADS score
using a PI-RADS interactive worksheet

Semi-automatic prostate segmentation,
Multi-parametric image review,
Biparametric combination image viewer,
Basic image manipulation tools,
Thresholding tool,
User interface to create/update user-
generated ROIs.

Semi-automatic prostate segmentation,
Multi-parametric image review,
Basic image manipulation tools,
Thresholding tool
User interface to create/update user-
generated ROIs.

Non-clinical
performance
Partially
equivalent

Bench testing performed to test the
functionality of the system.
This included characterization of the stand-
alone performance of the prostate
segmentation algorithm.

Clinical
performance
Predicate
device has no
data to
assess
equivalency

Prostate segmentation algorithm was tested
in a clinical use context, i.e. as a semi-
automatic algorithm after correction by
trained clinicians.

• IS0 14971 – Medical devices -Application
of risk management to medical devices
• IEC 62304 – Medical device software –
Software life cycle processes
• IEC 62366 – Medical devices -
Application of usability engineering to
medical devices

SW
verification
and validation
Equivalent

Tested in accordance with verification and
validation processes and planning. The
testing results support that all the system
requirements have met their acceptance
criteria and are adequate for its intended

6 COMPARISON OF TECHNOLOGICAL CHARACTERISTICS


Compatibility with
the environment
and other devices
Equivalent

Prostate gland volume, PSA density, ROIs
within the prostate, with for each ROI:
• Volume
• Average ADC value
• PI-RADS score
• Location

DICOM compatible data, type of MRI
scans: T2-weighted, DWI, ADC, DCE


Regulation Device Device Product Classification


Radiological
image processing
software for
radiation therapy

As advised by the FDA the new
product code, QKB, has been
created in-lieu of LLZ which uses
AI algorithms and is intended for
radiation therapy, and is the
proposed product code for ART-
Plan.

Any patient type for
whom relevant
modality scan data
is available.

The proposed device has
identical target populations to the
secondary predicates.

The proposed device and
predicates have identical
target environments

Intended Use
ART-Plan is a
software designed

Workflow Box is a
system designed to
allow users to route

RTx is intended to
be used by trained
medical

Intended Use
MIM 4.1 (SEASTAR) software is
intended for trained medical

The intended use and
indications for use of the
proposed device, ART-Plan


to assist the
contouring
process of the
target
anatomical
regions on 3D-
images of cancer
patients for whom
radiotherapy
treatment has
been planned.
The SmartFuse
module allows the
user to register
combinations of
anatomical and
functional images
and display them
with fused and
non-fused displays
to facilitate the
comparison and
delineation of
image data by the
user.
The images
created with rigid
or elastic
registration require
verifications,
potential
modifications, and
then the validation
of a trained user
with professional
qualifications in
anatomy and
radiotherapy.
With the Annotate
module, users can

department to
register
multimodality
images and
segment (non-
contrast) CT
images, to
generate needed
information for
treatment
planning,
treatment
evaluation and
treatment
adaptation.

DICOM-compliant
data to and from
automated
processing
components.
Workflow Box
includes processing
components for
automatically
contouring imaging
data using
deformable image
registration and
machine learning
based algorithms.
Workflow Box must
be used in
conjunction with
appropriate
software to review
and edit results
generated
automatically by
Workflow Box
components, for
example image
visualization
software must be
used to facilitate the
review and edit of
contours generated
by Workflow Box
component
applications.
Workflow Box is not
intended to
automatically detect
lesions.

professionals
including, but not
limited to,
radiologists, nuclear
medicine
physicians,
radiation
oncologists,
dosimetrists and
physicists.
RTx is a software
application intended
to display and
visualize 2D & 3D
multi-modal medical
image data. The
user may process,
render, review,
store, print and
distribute DICOM
3.0
compliant datasets
within the system
and/or across
computer networks.
Supported
modalities
include static and
gated a, PET, MR,
SPECT and planar
NM. The user may
also create, display,
print, store and
distribute reports
resulting from
interpretation of the
datasets.
RTx allows the user
to register
combinations of

professionals including, but not
limited to, radiologists,
oncologists, physicians, medical
technologists, dosimetrists and
physicists.
MIM 4.1 (SEASTAR) is a
medical image and information
management system that is
intended to receive, transmit,
store, retrieve, display, print and
process digital medical images,
as well as create, display and
print reports from those images.
The medical modalities of these
medical imaging systems
include, but are not limited to,
CT, MRI, CR, DX, MG, US,
SPECT, PET and XA as
supported by ACR/NEMA
DICOM 3.0.
MIM 4.1 (SEASTAR) provides
the user with the means to
display, register and fuse
medical images from multiple
modalities. Additionally, it
evaluates cardiac left ventricular
function and perfusion, including
left ventricular end-diastolic
volume, end-systolic volume,
and ejection fraction. The
Region of Interest (ROI) feature
reduces the time necessary for
the user to define objects in
medical image volumes by
providing an initial definition of
object contours. The objects
include, but are not limited to,
tumors and normal tissues.

and the primary predicate
AccuContour are the same in
that they are software
applications intended for
professional use to
display and visualize multi-
modal medical image data.
Supported modalities include
CT, PET, and MR.
The proposed device is also
identical to the primary
predicate in that it offers the
same two key features of the
planning process in
radiotherapy: image
registration and
segmentation.
In both devices, segmentation
can only be performed on CT
modality; registration can be
done from every supported
modality toward a CT with
deformable registration.
The proposed device may
differ from the primary
predicate in a) the list of
structures included in
automatic segmentation
algorithm b) the presence or
not of a rigid registration
algorithm, which is a
reduction compared to the
deformable registration that is
provided by ART-Plan.
However, not enough
information on these two
aspects is provided on the


edit manually and
semi-automatically
the contours for
the regions of
interest. It
also allows to
generate
automatically, and
based on medical
practices, the
contours for the
organs at risk and
healthy lymph
nodes on CT
images.
The contours
created
automatically,
semi-automatically
or manually
require
verifications,
potential
modifications, and
then the validation
of a trained user
with professional
qualifications in
anatomy and
radiotherapy.
The device is
intended to be
used in a clinical
setting, by trained
professionals only.
.

anatomical and
functional images
and display
them with fused and
non-fused displays
to facilitate the
comparison of
image data by the
user. The result of
the registration
operation can assist
the user in
assessing changes
in image
data; either within or
between
examinations and
aims to help the
user obtain a better
understanding of
the combined
information that
would otherwise
have to be visually
compared
disjointedly.
RTx provides a
number of tools
such as rulers and
region of interests,
which are intended
to
be used for the
assessment of
regions of an image
to support a clinical
workflow. Examples
of
such workflows
include, but are not

MIM 4.1 (SEASTAR) provides
tools to quickly create,
transform, and modify
contours for applications
including, but not limited to,
quantitative analysis, aiding
adaptive therapy, transferring
contours to radiation therapy
treatment planning systems and
archiving contours for patient
follow-up and management.
MIM 4.1 (SEASTAR) also aids
in the assessment of
PET/SPECT brain scans. It
provides automated quantitative
and statistical analysis by
automatically registering
PET/SPECT brain scans to a
standard template and
comparing intensity values to a
reference database or to other
PET/SPECT scans on a voxel
by voxel basis, within
stereotactic surface projections
or standardized regions of
interest.
Indications for Use
MIM 4.1 (SEASTAR) software is
used by trained medical
professionals as a tool to aid in
evaluation and information
management of digital medical
images. The
medical image modalities
include, but are not limited to,
CT, MRI, CR, DX, MG,


limited to, the
evaluation of the
presence or
absence of
lesions,
determination of
treatment response
and follow-up.
RTx supports the
loading and saving
of DICOM RT
objects and allows
the user to define,
import, display,
transform, store and
export such objects
including regions of
interest
structures and dose
volumes to radiation
therapy planning
systems. RTx
allows the user to
transform regions of
interest associated
with a particular
imaging dataset to
another,
supporting atlas-
based contouring
and rapid re-
contouring of the
same patient.

US, SPECT, PET and XA as
supported by ACR/NEMA
DICOM 3.0. MIM 4.1
(SEASTAR) assists in the
following indications:
* Receive, transmit, store,
retrieve, display, print, and
process medical images
and DICOM objects.
* Create, display and print
reports from medical images.
* Registration, fusion display,
and review of medical images
for diagnosis,
treatment evaluation, and
treatment planning.
* Evaluation of cardiac left
ventricular function and
perfusion, including left
ventricular end-diastolic volume,
end-systolic volume, and
ejection fraction.
* Localization and definition of
objects such as tumors and
normal tissues in
medical images.
* Creation, transformation, and
modification of contours for
applications
including, but not limited to,
quantitative analysis, aiding
adaptive therapy,
transferring contours to radiation
therapy treatment planning
systems, and
archiving contours for patient
follow-up and management.
* Quantitative and statistical
analysis of PET/SPECT brain
scans by comparing to other


registered PET/SPECT brain
scans
Lossy compressed
mammographic images and
digitized film screen images
must not be reviewed for
primary image interpretations.
Images that are printed to film
must be printed using a FDA-
approved printer for the
diagnosis of digital
mammography images.
Mammographic images must be
viewed on a display
system that has been cleared by
the FDA for the diagnosis of
digital mammography images.
The software is not to be used
for mammography CAD.


Standalone
software
application
accessed via a
compliant
browser (Chrome
or Mozilla
Firefox) on a
personal
computer, tablet
or phone (In
case of
connection to the
platform with a
screen of a
phone or a
tablet, the user
must choose the
option for the
desktop site of
his
communication
device. The
platform is
optimally used
with 17 inches
and up screen.
Facilitates
display and
visualization of
data by user.

The proposed device and
predicates have identical
methods of use

Full web platform
Launch from
Google Chrome
or Mozilla Firefox

Workstation and
Server based
application
supporting Windows
Server 2008 R2,

Server based
application
supporting Microsoft
Windows 10 (64-bit)
and Microsoft

The proposed devices and
predicates are compatible with
identical operating systems


Data
Visualization /
Graphical
Interface

None – the
proposed device
has no data
visualization
functionality. All data
processing is
automated and does
not require user
interaction. A control
interface is provided
for system
administration and
configuration only.

The proposed device is identical
to the primary predicate, and
secondary predicate, MIM 4.1, in
that it has a graphical interface.
Workflow Box is AI based but
has no interface. RTx is not AI
based but has an interface and
thus most of the tools of
Annotate for contour
edition/display

Registration:
Static and gated
CT, MR, PET
(via the
registration of
the CT of said
PET)
Segmentation:
CT (injected or
not), DICOM
RTSTRUCT

Registration:
Multimodality
DICOM fixed and
moving images
including CT,
MR, PET
Segmentation:
Non-contrast CT

Static and gated CT
and PET, MR,
SPECT, NM,
DICOM RT

CT, MR, DICOM
RTSTRUCT for
image
processing
Any valid DICOM
data for data routing

Medical image modalities include,
but are not limited to, CT, MRI,
CR, DX, MG, US, SPECT, PET
and XA as supported by
ACR/NEMA DICOM 3.0.

The proposed device is
compatible with the same
modalities as the primary
predicate on the registration
feature, which are CT, MR and
PET images in a DICOM format.
For both devices, supported
images can be fixed (static) or
moving (gated).
The primary predicate device
and ART-Plan are both
compatible only with CT images
on the segmentation feature.
The predicate device claims to
handle only non-injected CTs
while ART-Plan can be used
with injected CT images as well.
The predicate device does not


explicitly mention the format
RTStruct in supported
modalities which correspond to
the format used to store
segmentation masks in
radiotherapy. The terms
“medical images and DICOM
data” are used by the primary
predicate device, which can
imply the management of
DICOM RTStruct.
Compared to secondary
predicates, ART-Plan claims
less supported modalities.

Distribution of
DICOM
compliant
Images into
other DICOM
compliant
systems.

Allows export of
medical images
and DICOM data

Supports routing
and distribution of
images to other
DICOM nodes
including to custom
executables
determined by the
user.

The system has the ability to send
data to DICOM-ready devices for
image storage, retrieval and
transmission.

The proposed device and
primary predicates have
identical data export
capabilities.

No Limitation on
scanner model,
DICOM 3.0
compliance
required.
Compatible with
Microsoft
Windows.

Compatible with
data from any
DICOM compliant
scanners for the
applicable
modalities.
Integration with
Mirada DBx
application launcher

The software can receive,
transmit, store, retrieve, display,
print, and process DICOM objects
and medical image modalities
including, but not limited to, CT,
MRI, CR, DX, MG, US, SPECT,
PET and XA as supported by
ACR/NEMA DICOM 3.0.

The proposed device and
primary predicates have
identical compatibilities


No Limitation on
treatment
planning system
(TPS) model,
DICOM 3.0
compliance
required.


The proposed device, primary
predicate and secondary
predicate Workflow Box share
an AI delineation method.

Multi-modal
and mono-
modal.
Rigid and
deformable
Automatic and
manual
initialization
(landmarks,
fusion box,
alignment).
Registration for
the purposes of
replanning/
recontouring
and AI-based
automatic
contouring.

Automatic
registration.
Multi-modal and
mono-modal.
Deformable
registration.
Intensity based
algorithm
Registration for
the purpose of
treatment
planning,
treatment
evaluation and
treatment
adaptation.

Manual and Landmark
Rigid. Automatic multi-
modal rigid. Mono-
modal and multi-modal
deformable registration.
Motion correction in
hybrid scans and gated
scans.
Registration for the
purposes of
replanning/recontouring
and atlas-based
contouring.

Registration for the
purposes of
replanning/
re-contouring and
AI based
contouring. The
algorithms used
for image
registration are the
same for both RTx
and Workflow Box
devices.

Registration, fusion display, and
review of medical images for
diagnosis, treatment
evaluation, and treatment
planning.

Both the predicate device and
ART-Plan offer mono-modal (CT-
CT) and multi-modal (CT/MR,
CT/PET) deformable registration.
The SmartFuse module of ART-
Plan also offers rigid
registration, which is a
reduction of the deformable
registration since the degree of
freedom of the transformation
are constrained.
Both devices offer an automatic
solution for registration. ART-
Plan also offers semi-automatic
registration by including manual
initialization tools in addition to
automatic initialization.
Secondary devices offer the
same options as the proposed
device: rigid and deformable
transformation. No information
is given on the possible
automatization of this process.

Automatically
delineates
OARs and
healthy lymph
nodes (on any

Automatically
delineates OAR
(on non-contrast
CT images)

Automatically
delineates any structure
(OAR or lymph node)
included in the atlas

The software automatically
generates contours using a
deformable registration technique
which registers pre-contoured
patients to target patients.

The proposed device and
primary predicate are capable of
automatically contouring the
organ-at-risk (OAR).


CT images)
Deep learning
algorithm.
Automatic
segmentation
includes the
following
localizations:
* head and
neck
* thorax/breast
(for
male/female)
* abdomen
* pelvis (for
male only)
* brain.

Deep learning
algorithm.
It can
automatically
contour the
organ-at-risk,
including head
and neck,
thorax, abdomen
and pelvis (for
both male and
female),

images.
Atlas algorithm (contour
registration)
Automatic
segmentation supports
any anatomy included
in atlas images.

Registrations are either
between a serial pair of intra-
patient volumes or between a pre-
existing atlas of contoured patients
and a patient volume. This process
facilitates contour creation or re-
contouring for adaptive therapy.

They differ in that ART-Plan can
also delineate healthy lymph
nodes.
Plus, there is a difference in
intended anatomies. The
common localizations are the
head and neck, thorax, abdomen
and male pelvis. ART-Plan does
not claim to offer automatic
segmentation on female pelvis.
However, in addition to the 4
upper common localizations,
ART-Plan can also be used for
brain localizations.
Secondary predicate using
atlas-based algorithms can be
applied on any localization and
for any type of structure
contained in atlas images used
by the center.

Window and
level, pan,
zoom, cross-
hairs, slice
navigation.
Maximum,
average and
minimum
intensity
projection (MIP,
AVG, MinIP),
color rendering,
multi-planar
reconstruction
(MPR), fused
views,
gallery views.

Window and level, pan,
zoom, cross-hairs, slice
navigation.
Maximum or minimum
intensity projection
(MIP),
volume rendering, color
rendering, surface
rendering,
multi-planar
reconstruction (MPR),
fused views,
gallery views.

No information has been found
regarding view manipulation and
volume rendering concerning
the primary predicate. The
predicate device claims to offer
a feature to review the
processed image and perform
manual contouring, which
implies at least the display of
DICOM images with slice
navigation.
The proposed device has the
majority of the same tools as the
secondary RTx predicate, apart
from volume and surface


rendering
The Workflow Box secondary
predicate does not offer a
graphical interface.

AI Based
autocontouring,
Registration
based contour
projection (re-
contouring),
Manual ROI
manipulation
and
transformation
(margins,
booleans
operators,
interpolation).

Automatic target
volume
delineation
system.
Automatically
delineates
organs-at-risk
(OAR)
Manual Contour

2D and 3D ROIs, semi-
automatic ROI
definition, isocontour
ROIs using threshold
and percentage of
maximum, one-click
seed-pointing
contouring, manual ROI
manipulation, ROI
transformation, Atlas-
based contouring.

Atlas Based
contouring,
registration based
recontouring,
machine learning
based contouring

Atlas based contouring, tools to
quickly create, transform, and
modify contours.

Both the proposed device and
the primary predicate allow AI
automatic contouring and
manual contouring

Region/volume
of
interest
measurements
and
size
measurements

Intensity,
Hounsfield
units and SUV
measurements
Size
measurements
include 2D and
3D
measurements
(number of
slices, volume
of a structure,
static ruler)

Intensity, Hounsfield
units, activity and SUV
measurements
including min, max,
mean, peak, standard
deviation, total
glycolytic activity,
median, histogram, max
and mean ratio to
reference region.
Gray for RT Dose.
Size measurements
include 2D and 3D
measurements
including rulers and
volume, line profile.

ART-Plan offers the same
Intensity, Hounsfield units and
SUV
measurements and size
measurements including 2D and
3D measurements (volume and
ruler) as secondary predicate
RTx. However, this is only a
fraction of the tools claimed by
secondary predicate RTx.


Regions table with
charting supports
analysis of
measurement over
multiple studies using
standard protocols such
as RECIST, PERCIST
and WHO

The secondary predicate RTx
offers a range of tools for
region/volume quantification.
ART-Plan does not offer these
kinds of tools. It is a reduction in
claim or equivalent to the
primary predicate for which no
information was found.


The ART-Plan was assessed with
regards to usability for compliance
with IEC 62366

The study gathers the information on
the 3 tests performed on the
automatic segmentation performances
on European data.

The ART-Plan was evaluated for its safety and effectiveness based on the following testing:


Autosegmentation
performances according to
AAPM requirements

The testing demonstrated that the
auto-segmentation algorithm of the
module Annotate provides acceptable
contours for the concerned structures
on an image of a patient.

Autosegmentation
performances against MIM

Qualitative validation of
autosegmentation
performances

External Contour
performances according to
AAPM requirements

The testing demonstrated that the
External Contour Automatic
Segmentation algorithm of the module
Annotate provides acceptable
contours for the patient’s body on an
image of a patient.

Fusion performances
according to AAPM
recommendations

The testing evaluated the quality of
the rigid and deformable registration
tools of the SmartFuse module on
retrospective intra-patient images and
inter-patient images of different
modalities, to ensure the safety of the
device for clinical use.

The testing evaluated the quality of
the deformable registration tools of the
SmartFuse module on intra-patient CT
images.
Testing was conducted according to
POPI-model protocol on
corresponding public data.


Autosegmentation
performances on US data

The testing demonstrated that the
autosegmentation algorithm of the
Annotate module provides clinically
acceptable contours for the concerned
structures when applied to US
patients.

Pilot study for sample size
estimation - literature
review

The testing was a pilot study
estimating a consistent sample size of
dataset for our performance testing’s
considering the state-of-art studies in
image registration and segmentation.
The literature review was completed
on the most cited articles in the field of
medical vision.

System Verification and
Validation Testing

The system verification and validation
testing was performed to verify the
software of the ART-Plan.


Mint Labs, Inc., d/b/a. QMENTA - QMENTA Imaging S.L.

Common Name:
QMENTA CARE PLATFORM FAMILY

Picture archiving and communication systems to
medical image management and processing system

None established under Food Drug and Cosmetic Act


Mint Labs, Inc., d/b/a.
QMENTA -QMENTAImaging
S.L.

Blackford Analysis
Registration Product Family

QMENTA Care Platform
Family is asoftwaremedical
imaging system used to
receive DICOM images and
textual reports, organize
and store them in an
internal format, and to
make that information
available across a network
via web and customized
user interfaces. QMENTA
Care Platform Family
consists of two
components:
1. A Storage/archiving
server for the
retrieval of DICOM
images from CT and
MR modality data,
and also PET/CT
data from PACS

Product Family consists of
highly similar in

1. A Workflow Server that
for use since they

use, when reading studies
DICOM objects.

to aid navigation through,
Blackford includes

interpretation of, a target
help the user


and/or OS-based
file storage.
2. A workflow server
that allows to
integrate legally
marketed
applications for
clinical use into the
QMENTA Care
PlatformFamily.The
applications are
geared toward
specific types of
image analysis and
are marketed with
corresponding
names.
The results may be saved to
a DICOMimagefileandmay
be further visualized on an
imaging workstation.
QMENTA Care Platform
Family is designed to aid
suitablyqualifiedphysicians,
who will base their
diagnoses on training and
protocols that do not
necessarily rely on image
maps or image
quantifications.

navigate through
studies, or series
in the same study.
Similarly, QMENTA
Care provides a
storage and
archiving server
where the user
can navigate
through the
studies or series
within a study.

Registration-aided
interpretation of images
should be carried out by a
suitablyqualifiedphysician,
who will base their
diagnoses on training and

Not a clinically
significant
difference.


protocols that do not
necessarily rely on
registration for navigation.

necessarily rely on image
maps or image
quantifications.

Multiple applications
Hosts and manages a
portfolio of Blackford® and
Platform Partner products,
configured and managed
centrally, supporting
automated or triggered
processing of imaging
applications and AI
algorithms.

Multiple Applications
Additional legally marketed
applications for clinical use
can be "plugged in"intothe
QMENTA Care Platform
Family. The applications are
gearedtowardspecifictypes
of image analysis and are
marketed with
corresponding names.

Flexible hosting and cloud
deployment
Flexible hosting allows
clinical applications to be
locally hosted on VM or
Docker containers. Cloud
deployment enables
studies to be routed to
separate resource in the
cloud, minimizing dataflow

Flexible hosting and cloud
deployment
The "plugged in" clinical
applications are hosted on
VM or Docker containers.
Studies are routed to
separate resources in the
cloud, minimizing dataflow.

Not a clinically
significant
difference –
hosted using
resources in the
cloud.

Fully DICOM compliant
DICOM compliant and
integrated with DICOM
modality worklist to
pre-fetch prior studies for
analysis.

Fully DICOM compliant
DICOM image studies
retrieved from PACS and/or
OS-based file storage. From
a workflow perspective,
QMENTA Care can operate
as a computing appliance
thatiscapableofsupporting
DICOMfiletransferforinput
and output of results.

Configurable dataflow
Configurable dataflow
management offers
multiple adaptable SCPs
that maximize data
ingestion speed,
customizable AE titles for
manual triggering, and

Configurable dataflow
QMENTA Care offers
configurable dataflow
management, where image
studies can be transferred
from multiple SCPs into the
Storage/Archiving server.
Then images can be


comprehensive patient
consolidation from
multiple sources.

automatically processed by
the configured image
processing applications, and
results can be automatically
transferred back to a third
party DICOM workstationor
PACS.
QMENTA PACS Nexus is a
small application that runs
on alocalworkstationinside
the hospital network and
automates the task of
exporting images to the
QMENTA Care Platform and
retrieving the results.

Relevancy Engine
Relevancy engine
determines which studies
to send each clinical
application and collates
descriptor knowledge from
all deployments. It
automates the process of
matching incoming study
information with the
appropriate application,
processes the data, and
ensures results are quickly
sent back to the right
system.

Protocol Adherence Engine
The protocol adherence
engine determinesifstudies
follow the configured
requirements for a clinical
application, taking into
account description
knowledge and image
parameters available in
DICOM objects,e.g.number
of timepoints, consistent
spacing, etc.

Marketplace™ provides a
Development Kit provides

vettedcuratedmarketplace
the possibility of integrating

of regulatory approved
curated legally marketed

medical imaging analysis
third-party applications for

applications and AI
clinical use accessed via

algorithms accessed via
QMENTA Care Platform


QMENTA Care Platform Family (QMENTA Care) is a software medical imaging system that runs on

standard computer hardware that may compute legally marketed applications for clinical use based

on DICOM images captured via MR, CT and PET modalities.

● Retrieval of MR, CT and PET DICOM image studies fromPACS and/or OS-based file storage

● Computation of legally marketed applications for clinicaluse

● Generation of reports summarizing the computationsperformed


Classification Name:
Magnetic Resonance Diagnostic Device

Regulation Number:
90-LNH (Per 21 CFR § 892.1000)

Vantage Orian 1.5T, MRT-1550, V6.0 with AiCE Reconstruction
Processing Unit for MR


Predicate Device (AiCE): Vantage
Orian 1.5T, MRT-1550, V6.0

Marketed By
Canon Medical Systems USA, Inc.
Canon Medical Systems USA, Inc.


Subject Device
Predicate Device (K193097)

Shoulder, Hip, Elbow, Wrist/Hand,
Foot/Ankle

Pelvis
Female, Pelvis (soft tissue), Prostate
New

Addition of the following anatomical regions;


Item
Subject Device:
Predicate Device:
Notes

Operational Modes
Normal and 1st Operating Mode
Normal and 1st Operating Mode
Same

Allows screen access to 1st level
operating mode


4W/kg for whole body (1st
operating mode specified in IEC
60601-2-33:
2010+A1:2013+A2:2015)

Potential emergency
condition and means
provided for shutdown

Shutdown by Emergency Ramp
Down Unit for collision hazard
for ferromagnetic objects


Device Common Name:
Radiological Image Proces

Classification Name:
Picture archiving and com


Workflow
BoxTM
(including
DLCExpert™,
Embrace:CT™,
Embrace:MR™,
Re:Contour™)

Picture
Archiving and
Communicatio
ns System

Table 1. Identification of Predicate Device.


Brachial plexus, Brain Stem, Constrictor naris, Ear Left, Ear Right, Eye Left,
Eye Right, Hypophysis, Larynx, Lens Left, Lens Right, Mandible, Optic
chiasm, Optic nerve Left, Optic nerve Right, Oral cavity, Parotid Left,
Parotid Right, Sublingual gland, Submandibular gland Left,
Submandibular gland Right, Spinal Cord, Temporal Lobe Left, Temporal
Lobe Right, Temporomandibular joint Left, Temporomandibular joint
Right, Thyroid, Trachea

Esophagus, Heart, Lung Left, Lung Right, Spinal Cord, Trachea

Bladder, Duodenum, Gallbladder, Femur Left, Femur Right, Kidney Left,
Kidney Right, Large Bowel, Liver, Pancreas, Rectum, Small Bowel, Spleen,
Spinal Cord, Stomach

Table 2. List of OARs delineated by DV.Target across three anatomic sites.


• DV.Target is a software application
that enables the routing of image
data (CT Images) to automatic image
processing workflows, using machine
learning learning-based algorithms to
automatically delineate OARs
(Organs-at-risk). Contours generated
by DV.Target may be used as an
input to clinical workflows for
treatment planning in radiation
therapy.
• DV.Target is intended to be used by
trained medical professionals
including radiologists, radiation
oncologists, dosimetrists, and
physicists.

• Workflow Box is a software system
designed to allow users to route DICOM-
compliant data to and from automated
processing components. Supported
modalities include CT, MR, RTSTRUCT.
• Workflow Box includes processing
components for automatically
contouring imaging data using
deformable image registration to
support atlas-based contouring, re-
contouring of the same patient
and machine learning based contouring.
• Workflow Box is a data routing and
image processing tool which
automatically applies contours to
data which is sent to one or more of the
included image processing workflows.

Table 3. Comparison of Indications for Use with Predicate Device.


Classification Panel
Radiology
Radiology

Description
Image Processing
Image Processing

Indications for Use
Indications for Use
NinesMeasure is a semi-
The Lung Nodule Assessment

automatic tool indicated for use
and Comparison Option is

by trained radiologiststo aid in
intended for use as a diagnostic

the analysis and review of adult
patient-imaging tool. It is

thoracic CT images.
intended for the review and

NinesMeasure provides
analysis of thoracic CT images,

quantitative information about
providing quantitative and

pulmonary nodule size on a
characterizing information about

A table comparing the key features of the subject and predicate device is provided below.


nodules in the lung in a single
study, or over the time course of
several thoracic studies.
Characterizations include
diameter, volume and volume
over time. The system
automatically performs the
measurements, allowing lung
nodules and measurements to
be displayed.

User Population
Radiologists
Radiologists and Technologist

Image processing algorithms
computing pulmonary nodule
measurements and returning
computed measurements to the
workstation.

computing pulmonary nodule
computing pulmonary nodule

measurements and returning
measurements and returning

computed measurements to the
computed measurements to the

Image processing algorithms for
nodule measurement

nodule measurement
-Display, comparison, and risk

Anatomical region of interest
Chest
Chest

-long axis measurement
-short axis measurement
(perpendicular to long axis)

-short axis measurement
-short axis measurement

(perpendicular to long axis)
(perpendicular to long axis)


Normalized error on long axis diameter
[95% CI]

Normalized error on short axis diameter
[95% CI]

The primary endpoints of the algorithm are listed below:


Nodule size
Number of nodules
Normalized error on
Normalized error on

3-6 mm
100
0.104 [Upper Bound:
0.123 [Upper Bound:

6-8 mm
63
0.119 [Upper Bound:
0.143 [Upper Bound:

8-10 mm
46
0.13 [Upper Bound
0.133 [Upper Bound

The primary endpoint stratified by nodule size is listed below:


Primary:
Aquilion Prime SP
(TSX‐303B/8) V10.2
with AiCE‐i

JAK:
System, X‐ray,
Tomography,
Computed

Reference Device:
Aquilion LB, TSX‐
201A/3, V6.0


Aquilion Prime SP (TSX‐303B/8)
V10.2 with AiCE‐i

Model Number
(TSX‐202A/3) V10.6 with AiCE‐i

View rate
Max. 2400 views/s (0.5 s)
Max. 2572 views/s (0.35 s)

Extended Field of View
Available
Available

±30°
Axial and helical scanning
Gantry and remote controlled

Two types
Small: FOV M
Large: FOV L and XL

Detector
PURE ViSION detector
PURE ViSION detector

Data acquisition
1136 channels  80 rows
896 channels  80 rows

X‐ray generation
 Channel‐direction (fan) angle
 Slide‐direction (cone) angle
 Rated output
 X‐ray tube voltage
 X‐ray tube current
 X‐ray tube heat capacity
 X‐ray tube cooling rate

Single Energy Metal Artifact
Reduction (SEMAR)

 Quantum Denoising Software
(QDS)
 Adaptive Integrative Dose
Reduction 3D (AIDR 3D)
 AIDR 3D Enhanced
 AiCE (Body, Lung, Bone, Brain,
Inner Ear)

 Quantum Denoising Software
(QDS)
 Adaptive Integrative Dose
Reduction 3D (AIDR 3D)
 AIDR 3D Enhanced
 AiCE (Body, Lung, Cardiac,
Bone, Brain, Inner Ear)

Respiratory‐gating system
Available
Available


Annotations -
Standardized and Free
Text

MPR
・ orthogonal / oblique /
curved Multi-Planar
Reconstructions (MPR),
・ Sector and rectangular
shape MPR image
viewing
・ MPR for dental images
・ Multiple MPR images
along an object (Slicer)

Table 1 Device Features and Technical Characteristics Comparison Matrix


General image data
management and
administration tools

Yes
(Some segmentation
applications are
implemented using a deep
learning method called as
“Fully Convolutional
Network”)


Product Availability
Software Product
Software Product


GE Healthcare Japan Corporation
7-127, Asahigaoka, 4-chome
Hino-shi, Tokyo, 191-8503, Japan

Tomohiro Ito
Sr. Regulatory Affairs Leader
Phone +81-42-585-5383 or +81-90-8346-6807
Email: tomohiro.ito@ge.com

Helen Peng
Sr Regulatory Affairs Director
GE Healthcare
Phone Number: 262-4248222
e-mail: hong.peng@ge.com

John Jaeckle
Chief Regulatory Affairs Strategist
GE Healthcare
Phone Number: 262-424-9547
e-mail: John.Jaeckle.com

Product Identification
Revolution Ascend

Computed Tomography X-ray System, 21 CFR 892.1750
/ Class II

In accordance with 21 CFR 807.92 the following summary of information is provided:


510 (K) number
K192686 cleared on October, 2019

Computed Tomography X-ray System, 21 CFR 892.1750 /
Class II

510(k) Premarket Notification Submission for Revolution Ascend


Revolution Maxima gantry
- Bore: 70cm
- Digital Tilt ( 30)

Revolution Ascend Gantry
- Bore: 75cm
- Physical Tilt ( 30)

Patient Table
VT1700v, Lite
VT1700v, VT2000, VT2000x

Available
*3D Guidance was cleared in
K153429 and ported to proposed
device

Available
*Smart Plan was cleared in K171013
and ported to proposed device

Available
* Auto Prescription was cleared in
K133705 and ported to proposed
device

Available
* Auto Gating was cleared in
K133705 and ported to proposed
device

Intelligent Protocoling by
Machine Learning


883 Santa Cruz Ave, Suite 205
Menlo Park, CA 94025

Trade name: SubtleMR
Manufacturer: Subtle Medical, Inc.
Regulation Number: 21 CFR 892.2050
Regulation Name: System, Image Processing,
Radiological
Device Class: Class II
Product Code: LLZ
510(k) Number: K191688
510(k) Clearance Date: September 16, 2019


Software package that operates on
off-the-shelf hardware

The software processes DICOM
compliant image data

Table 2. Summary of Technological Characteristics Comparison.


SubtleMR software implements
an image enhancement algorithm
using convolutional neural
network based filtering. Original
images are enhanced by running
through a cascade of filter banks,
where thresholding and scaling
operations are applied. Separate
neural network based filters are
obtained for noise reduction and
sharpness enhancement. The
parameters of the filters were
obtained through an image-guided
optimization process.

The software operates on DICOM
files on the file system, enhances
the images, and stores the
enhanced images on the file
system. The receipt of original
DICOM image files and delivery
of enhanced images as DICOM
files depends on other software
systems. Enhanced images co-
exist with the original images.

Head, spine, neck, abdomen,
pelvis, prostate, breast and
musculoskeletal MRI


Aquilion ONE (TSX‐
306A/3) V10.0 with
Spectral Imaging
System

JAK:
System, X‐ray,
Tomography,
Computed


S
ubject Device
P
redicate Device
C
omment

Model Number
TSX‐306A/3) V10.4 with
TSX‐306A/3) V10.0 with

pectral Imaging System
pectral Imaging System

5
10(k) Number
T
his submission
K
192828

Spectral Imaging System
(CSDE‐004A)
 Scan Type
 Scan Regions
 Spectral Reconstruction
Images
 Metal Artifact
Reduction (MAR)
 ECG‐gated scan
 Gantry tilt
 Rotation time

Available
‐Rapid kV Switching
‐Abdomen and pelvis,
Chest, Extremities
and Cardiac
‐Basis material image
‐Monochromatic
image
‐Iodine Map
‐VNC (virtual non‐
contrast) image
Available
Available
Available
0.275, 0.5, 0.75 and
1.0 s

Available
‐Rapid kV Switching
‐Abdomen and pelvis,
Chest and Extremities
‐Basis material image
‐Monochromatic
image
‐Iodine Map
‐VNC (virtual non‐
contrast) image
N/A
N/A
N/A
0.5, 0.75 and 1.0 s

Addition of Cardiac
scan region and image
quality improvements
to existing regions


Jen-Tang Lu, PhD (Chief Executive Officer)

Date Summary Prepared:
February 09, 2021


Radiological Image Processing Software for
Radiation Therapy

Regulation Number / Product Code:
21 CFR 892.2050 / QKB


Device Name
VBrain
AccuContour™
MIM - MRT Dosimetry

VBrain is a software
device intended to assist
trained medical
professionals, during
their clinical workflows
of radiation therapy
treatment planning, by
providing initial object
contours of known
(diagnosed) brain tumors
(i.e., the region of
interest, ROI) on axial
T1 contrast-enhanced
brain MRI images.

It is used by radiation
oncology department to
register multimodality
images and segment
(non-contrast) CT
images, to generate
needed information for
treatment planning,
treatment evaluation and
treatment adaptation.
The product has two
image process functions:
(1)Deep learning
contouring: it can
automatically contour

MIM software is used by
trained medical
professionals as a tool to
aid in evaluation and
information management
of digital medical
images. The medical
image modalities
include, but are not
limited to, CT, MRI, CR,
DX, MG, US, SPECT,
PET and XA as
supported by
ACR/NEMA DICOM
3.0.

Table 5-1. Comparison with the Predicate Devices.


•Planning and
evaluation of permanent
implant brachytherapy
procedures (not
including
radioactive
microspheres).
•Calculating absorbed
radiation dose as a result
of administering a
radionuclide.
When using device
clinically, the user
should only use FDA
approved
radiopharmaceuticals. If
using with unapproved
ones, this device should
only be used for research
purposes.
Lossy compressed
mammographic images
and digitized film screen
images must not be
reviewed for
primary image
interpretations.
Images that are printed
to film must be printed
using an FDA-approved
printer for the diagnosis
of digital mammography
images.
Mammographic images
must be viewed on a
display system that has
been cleared by the FDA
for the diagnosis of
digital mammography
images. The software is
not to be used for
mammography CAD.

Atlas-based algorithm
and propagation tools
(requiring user’s input to


Microsoft Windows and
Apple macOS operating
systems

Trained medical
professionals including,
but not limited to,
radiologists, oncologists,
physicians, medical
technologists,
dosimetrists, and
physicists.

It is used by radiation
oncology department.

Segmentation Features:
Non-Contrast CT
Registration Features:
CT, MRI, PET

CT, MRI, CR, DX, MG,
US, SPECT, PET and
XA as supported by
ACR/NEMA DICOM
3.0.

Qualified brain tumors -
brain metastases,
meningiomas, and
acoustic neuromas

Organ-at-risk, including
head and neck, thorax,
abdomen and pelvis (for
both male and female)

To support the intended
use of the VBrain AI
software for brain tumor
contouring
(segmentation)
performance, Vysioneer
conducted a
retrospective, blinded,
multicenter,
multinational study with
the VBrain software. The
test data sets consisted of
116 cases acquired from
4 different institutions (3
US and 1 non-US). Five
metrics are evaluated: (1)
lesion-wise sensitivity,
(2) false-positive rate, (3)
lesion-wise Dice
coefficient, (4) average
Hausdorff distance, and
(5) average centroid
distance between

Segmentation
performance test
The segmentation
performance test was
performed on proposed
device and predicate
device to evaluate the
automated segmentation
accuracy. Two separate
tests were performed.
One test involved
images generated in
healthcare institutions in
China using scanner
models available in
China covering three
major vendors. The other
involved images
generated in healthcare
institutions in US using
scanner models available
in US covering three
major vendors. The three
major vendors were GE,

MIM Software Inc. has
conducted performance
and integration testing
on MIM - MRT
Dosimetry software with
a comparison to a
commercially available
solution for internal
radionuclide dosimetry.
Standard quality control
phantoms, simulated
phantoms based on the
NEMA IEC Body
Phantom, simulated
phantoms based on
patient data, and clinical
patient data were used
for verification testing.
All tests were performed
using standard clinical
acquisition and
reconstruction protocols.
The accuracy of planar
corrections for


Current Predicate Device
syngo.CT Lung CAD (VC30) (K193216)

Type of Change and Impact to
Safety & Effectiveness

Solid pulmonary nodules in diagnostic
chest CT acquisitions

Solid and subsolid (part-solid and
ground-glass) pulmonary nodules in
screening and diagnostic chest CT acqui-
sitions

[Updated]
Reflecting parameters validated in the
reader study.

The LungCAD software device is a Com-
puter-Aided Detection (CAD) tool de-
signed to assist
radiologists in the detection of solid pul-
monary nodules during review of multi-
detector
computed tomographic (MDCT) thoracic
examinations. The software is an adjunc-
tive tool
that alerts the radiologist to regions of in-
terest (ROI) that may be initially over-
looked. The
LungCAD software device use is in-
tended to be used as a second reader
after the radiologist has completed
his/her initial read.

syngo.CT Lung CAD software device is a
Computer-Aided Detection (CAD) tool de-
signed to assist radiologists in the detec-
tion of pulmonary nodules during review
of multi-detector computed tomography
(MDCT) thoracic examinations. The soft-
ware is an adjunctive tool to alert the ra-
diologist to regions of interest (ROI) that
may otherwise be overlooked.

[Updated]
Reflecting key aspects for the intended
use. Details that explicitly relate to the
indication for use have been removed
and are included in the indications for
use (see below).


Size
• Solid ≥ 3mm and < 20mm
Locations
• full range: central, peripheral
Contours:
• round, irregular

Diameter
• Solid ≥ 3mm and < 30mm
• Subsolid (part-solid and ground
glass) ≥ 5mm and < 30mm
Locations
• full range: central, peripheral
Contours:
• round, irregular

[Extended]
- Solid nodules to 30mm
- Included Subsolid (part-solid and
ground glass) nodules
- Validated by reader study

concurrent first reader OR
second reader workflow

Scanners
Siemens multi-detector CT (MDCT)
scanners.

Scanners
Multi-vendor and multi-detector CT
(MDCT) scanners (Siemens, GE, Philips,
and Toshiba)

[Updated]
Recommendation to use 16 or more
detector rows included, as recom-
mended by FDA,
Reflecting parameters validated in the
reader study.

Scan area
The scan area needs to comprise the
entire thorax covering the lung apices to
the bases

Scan area
The scan area needs to comprise the en-
tire thorax covering the lung apices to the
bases (single breath hold recommended)

[Updated]
Clarification added; no Impact

Scan direction
Cranio-caudal or caudal-cranial


[Updated]
Reflecting parameters validated in the
reader study.

[Removed]
Aspect captured by the Dose recom-
mendation

Slice Thickness
Up to and including 2.5mm, it is recom-
mended that <= 1.25 mm be used for the
detection of smaller nodules (e.g.
3.0mm)

Slice overlap
0–25%
Note: Reconstruction overlap is allowed,
but gaps are not permitted

Slice Overlap
0–50%
Note: Reconstruction overlap is allowed,
but gaps are not permitted

Number of images
Up to 1000 images per series.

[Removed]
No longer a technological limitation

Kernel
Consistent with thoracic CT protocols and
in line with patient safety
guidelines. Kernels were grouped as to
their profile. Typical kernels
validated by the reader study were:
Smooth: B, B30f, Standard, FC10.
Medium: C B45f, B50f, Lung, FC50,
FC51, Bv49d_2, I50f_2, B60f.
Sharp: D, B70f, Bone, FC52 .


Contrast
Intravenous contrast enhancement is op-
tional

[Removed]
Insufficient data in the reader study to
substantiate this characteristic.

Dose
Consistent with thoracic CT protocols and
in line with patient safety guidelines.
Typical values are: CTDIvol < 8.0 mGy
(milligray) in diagnostic protocols and
CTDIvol of = 3.0 mGy in screening proto-
cols. These values are defined for stand-
ard sized patient—5 ft 7 in., 154 lb (170
cm, 70 kg)—based on a 32-cm reference
phantom with appropriate reductions in
CTDIvol for smaller patients and appro-
priate increases in CTDIvol for larger pa-
tients.

[Extended]
Dose characteristic has been updated,
as discussed with FDA, to reflect the
data for diagnostic and screening pro-
tocols included and validated as part of
the reader study.

Hosting Platform
syngo.via (VB50)
syngo.via (VB60)
[Unchanged] and no Impact

Hosting Application
syngo MM Oncology
syngo MM Oncology
[Unchanged] and no Impact


Preprocessing
Standardization of
the input images and
lung segmentation

(a) isotropic volume resampling
(b) lung segmentation is accomplished using a CCN. Initially
a coarse estimation of the lung is performed using a V-net
process. Using two predefined bounding boxes left and right
lungs are initialized. Another V-net is used to segment left
and right lungs. This is followed by up-sampling to the original
image resolution.

(a) isotropic volume resampling
(b) lung segmentation is accomplished using a CCN. Initially a
coarse estimation of the lung is performed using a V-net process.
Using two predefined bounding boxes left and right lungs are ini-
tialized. Another V-net is used to segment left and right lungs.
This is followed by up-sampling to the original image resolution.

Candidate Genera-
tion
The partitioned vol-
ume is processed us-
ing a CNN and fil-
tered to yield a list of
candidates for each
subvolume.

(a) isotropic volume is partitioned into subvolumes
(b) Each subvolume is fed to a CNN to compute features (“re-
sponse volume”). Filtering and non-maximum suppression
yield a list of candidates for each subvolume
(c) candidates above a certain threshold score are passed to
the next step.

(a) isotropic volume is partitioned into subvolumes
(b) Each subvolume is fed to a CNN to compute
features (“response volume”). Filtering and non-maximum sup-
pression yield a list of candidates for each subvolume
(c) candidates above a certain threshold score are passed to the
next step.

Candidate Classifi-
cation utilizes a
CNN-based classifier
to process each can-
didate and estimate
the
likelihood of its type
as either “nodule” or
“non-nodule”.

CNN is used for feature computation for each candidate.
(a) The input image patch is firstly processed by batch nor-
malization.
(b) Three blocks of operations are computed. In each block, a
convolution, with stride 2, is used for down-sampling instead
of max-pooling.
(c) Semantic features from image features are computed us-
ing two fully connected layers.
(d) A soft-max function, applied to each candidate, assigns 2
values corresponding to the probability of being a nodule or
being a false positive.
(e) A weighted-sum of the scores from this phase and
the results of the prior step is computed. Candidates above a
certain threshold score are labeled as nodule candidates.

CNN is used for feature computation for each candidate.
(a) The input image patch is firstly processed by batch normaliza-
tion.
(b) Three blocks of operations are computed. In each block, a
convolution, with stride 2, is used for down-sampling instead of
max-pooling.
(c) Semantic features from image features are computed using
two fully connected layers.
(d) A soft-max function, applied to each candidate, assigns 2 val-
ues corresponding to the probability of being a nodule or being a
false positive.
(e) A weighted-sum of the scores from this phase and the results
of the prior step is computed. Candidates above a certain thresh-
old score are labeled as nodule candidates.


Postfiltering This step includes the application of two cascaded fil-
ters. The first one aims at removing false positives originating (a)
from the colon and a second one from (b) calcified protrusions
(for example, areas where the sternum meets the manubrium,
spine malformations, and osteophytes, and so on).
The first filter is a CNN-based classifier that has a similar struc-
ture to that of the classifier in step 3.
The second filter uses three orthogonal slices at the candidate lo-
cation as input to three CCN-based classifiers (one per slice).
The results from the three classifiers is then combined by a max-
voting mechanism. Any candidate deemed a false positive by ei-
ther filter is thus removed

The location information of all the nodule candidates are col-
lecte a final candidate list passed to the Hosting Application

The location information of all the nodule candidates are collected
into a final candidate list passed to Hosting Application.


syngo.CT Brain Hemorrhage
MaxQ-AI AccipioIx

Notification-only, parallel
workflow tool

Hospital networks and qualified
clinicians

Identify patients with a
prespecified clinical
condition

Clinical condition
Intracranial hemorrhage
Intracranial hemorrhage
Same

Alert to finding
Yes; flagged for review
Yes; flagged for review
Same

Independent of standard
of care workflow

Modality
Non-contrast CT
Non-contrast CT
Same


Suspected hemorrhage / No suspected
hemorrhage


Digital Imaging and Communications in
Medicine (DICOM) Set; PS 3.1 – 3.20

Medical Device Software –Software Life Cycle
Processes; 62304:2006 (1st Edition)

Medical devices – Application of risk
management to medical devices; 14971 Second
Edition 2007-03-01

Medical devices - Part 1: Application of
usability engineering to medical devices
IEC 62366-1:2015

covering electrical and mechanical safety listed below, prior to introduction into interstate commerce:


Legal Manufacturer
Perspectum Ltd.
Perspectum Ltd.

NDeuvmicbee Nr ame
Hepatica (Hepatica v1)
LiverMultiScan (LMSv4)

System, Nuclear Magnetic
Resonance Imaging


Comparison of Subject and Predicate Device

Characteristic
Hepatica v1 (Subject device)
LMSv4 (Predicate device)

“Hepatica (Hepatica v1) is a post-processing
medical device software that presents
quantified metrics which may contribute to
the assessment of a patient’s liver health.
Hepatica (Hepatica v1) uses image
visualisation and analysis tools to process
DICOM 3.0 compliant magnetic resonance
image datasets to produce semi-automatic
segmented 3D models of the liver based on
the work of Couinaud and the Brisbane 2000
terminology. For each identified Couinaud
segment, volumetric data is determined and
reported.
Hepatica (Hepatica v1) may also report iron
corrected-T1 (cT1) and PDFF calculated using
the IDEAL method from multi-slice
acquisitions, on a per segment basis, over
the whole liver. Both metrics present
numerical values of different fundamental
liver tissue characteristics that can be used
as measures of liver tissue health.
Hepatica (Hepatica v1) provides trained
clinicians with additional information to
evaluate the volume and health of a patient’s
liver on a segmental basis. It is not intended
to replace the established procedures for the
assessment of a patient’s liver health.
However, information gathered through
existing diagnostic tests, clinical evaluation of
the patient, as well Hepatica (Hepatica v1),
may support surgical decision making.”

“LiverMultiScan (LMSv4) is indicated for use
as a magnetic resonance diagnostic device
software application for non-invasive liver
evaluation that enables the generation,
display and review of 2D magnetic resonance
medical image data and pixel maps for MR
relaxation times.
LiverMultiScan (LMSv4) is designed to utilize
DICOM 3.0 compliant magnetic resonance
image datasets, acquired from compatible
MR systems, to display the internal structure
of the abdomen including the liver. Other
physical parameters derived from the images
may also be produced.
LiverMultiScan (LMSv4) provides a number of
tools, such as automated liver segmentation
and region of interest (ROI) placements, to
be used for the assessment of selected
regions of an image. Quantitative
assessments of selected regions include the
determination of triglyceride fat fraction in
the liver (PDFF), T2* and iron-corrected T1
(cT1) measurements. T2* may be optionally
computed using the DIXON or LMS MOST
methods.
These images and the physical parameters
derived from the images, when interpreted
by a trained clinician, yield information that
may assist in diagnosis.”

Patients who are suitable to undergo an MRI
scan and not contra-indicated for MRI.
Hepatica may benefit the clinical
management of patients who are being
considered for liver resection(s).

Patients suitable to undergo an MRI scan and
not contra-indicated for MRI


Characteristic
Hepatica v1 (Subject device)
Hepatica v1 (Subject device)
LMSv4 (Predicate device)

Device User
Trained Perspectum internal operator
Trained Perspectum internal operator
Trained Perspectum internal operator

An interpreting clinician or healthcare
practitioner

Installation of Hepatica v1 is controlled and is

Installation of LMSv4 is controlled and is

installed on general purpose workstations

that meet the minimum technical
that meet the minimum technical

requirements at Perspectum’s image analysis

centre by specialist members of staff
centre by specialist members of staff.

Hepatica v1 is a standalone software device

LMSv4 is a standalone software device that is

that is intended to be installed on general

workstations at Perspectum’s image analysis

analysis centres. The intended device users

centres. The intended device users will log

will log on to the workstations, access the

on to the workstations, access the device,

device, and use the device on general-use

Hepatica v1 is a post-processing software

LMSv4 is a post-processing software and the

and the intended device users are trained

Perspectum internal operators.
Perspectum internal operators.

Operators use LMS to conduct quantitative

analysis of liver tissue characteristics to

characteristics to produce a report.
produce a report.

The end-users for the output from the
The end-users for the output from the

device, the report, are clinicians who receive

and interpret Hepatica v1 reports.
and interpret LMSv4 reports.

Anatomical
Abdomen, including the Liver
Abdomen, including the Liver
Abdomen, including the liver
Abdomen, including the liver

LMS is a standalone software application. It
does not deliver, monitor or depend on
energy delivered to or from patients.

Hepatica is a standalone software application
that imports MR datasets encompassing the
abdomen, including the liver. Visualisation
and display of T1-weighted MR data which
can be analysed, and quantitative metrics of
tissue characteristics and liver volume are
then reported.
Datasets imported into Hepatica are DICOM
3.0 compliant and reported metrics are
independent of the MRI equipment vendor.

LMS is a standalone software application that
imports MR data sets encompassing the
abdomen, including the liver. Visualisation
and display of 2D multi-slice, spin-echo MR
data can be analysed, and quantitative
metrics of tissue characteristics are then
reported.
Datasets imported into LMS are DICOM 3.0
compliant, reported metrics are independent
of the MRI equipment vendor.

Allows for the 3D visualisation of the liver
and quantification of metrics (cT1, PDFF and
volumetry) from liver tissue and exportation
of results and images to a deliverable report.

Allows for the visualisation via parametric
maps and quantification of metrics (cT1, T2*
and PDFF) from liver tissue and exportation
of results and images to a deliverable report.


Comparison of Subject and Predicate Device

Characteristic
Hepatica v1 (Subject device)
LMSv4 (Predicate device)

Hepatica v1 allows for:
Volumetry
• Reporting of whole liver and segmental
volume
• Semi-automatic segmentation of the
outer contour of the liver
• Semi-automatic segmentation of the
liver into Couinaud segments via placed
landmarks of anatomical areas of
interest
cT1 and PDFF metrics may be quantified and
loaded from LMS when analysis has been
conducted on the same patient. Datasets
imported from LMS contain all image analysis
warnings and cautions associated with the
individual analysis and are included in the
hepatica report.

LMSv4 allows for:
cT1
• Full segmentation of the outer liver
contour and liver vasculature of the cT1
parametric map. IQR and median metrics
are reported from the segmentation.
• ROI placed method on the cT1 map with
IQR and median metrics from the placed
ROI’s potentially across multiple
acquired slices.
T2*
• ROI placed method on the T2* map with
IQR and median metrics from the placed
ROI’s potentially across multiple
acquired slices.
• T2* parametric maps are calculated from
the MOST method or the three-point
DIXON method (1)
PDFF
• Full liver segmentation of the PDFF
parametric map where IQR and median
metrics are reported from the
segmentation.
• ROI placed method on the PDFF map
with IQR and median metrics from the
placed ROI’s potentially across multiple
acquired slices
• PDFF parametric maps are calculated
using the LMS IDEAL method (2)

Hepatica v1 does not support quantification
of metrics from MR relaxometry. If cT1
results are imported from LMS, cT1 may be
reported for all individual Couinaud
segments and for whole-liver analysis.
Median T2* values are given from the cT1
quantification.

T1, iron- and fat- corrected T1 (cT1) and T2*
mapping

Liver fat quantification data is imported from
the predicate device (LMSv4), where
available. If PDFF results are imported from
LMS, PDFF may be reported for all individual
Couinaud segments as well as Whole-liver
PDFF based on the segmented outer-

Utilizes MR images that exploit the
difference in resonance frequencies between
hydrogen nuclei in water and triglyceride fat
using the LMS IDEAL method (2).


contour. PDFF parametric maps are
calculated using the LMS IDEAL method (2).

Hepatica v1 supports semi-automatic liver
segmentation of T1-weighted volumetric
data.
Liver segmentation in Hepatica v1 requires
the placement of anatomical landmarks to
define the outer contours of the liver and
can be adjusted by the operator, where
necessary.
Where available, whole liver and segmental
cT1 and PDFF quantitative metrics derived
from the predicate device may be presented
in the final report.

LMSv4 supports automatic multi-slice full
liver segmentation of the cT1 and PDFF
parametric maps. Use of this functionality is
at the discretion of the operator, instead or
in combination, with the ROI based method.
The cT1 segmented liver is presented in
colour level window, while the rest of the
cT1 image is presented in greyscale level
window with liver vasculature excluded from
the segmented volume.

Median and interquartile range
measurements created from a cross
sectional slice of liver tissue. For each
parametric map, statistics from multiple
Regions of Interest (ROIs) – potentially
placed across multiple slices – are
summarised.
Also supports the display of ‘Live’ ROI
statistics when moving the ROI across the
parametric map.

Hepatica uses volumetric datasets to create
2D anatomical views from all supported
scanners.
Where available, cT1 and PDFF parametric
maps are derived from the predicate device.

Iron corrected T1 (cT1), T2* and Proton
Density Fat Fraction (PDFF) parametric maps
can be created from all supported scanners.
It is possible to use the T2* and PDFF maps
and knowledge of the T2* and PDFF
measurements and the scanner field
strength to correct for signal changes related
to iron deposits, producing a cT1 map. The
cT1 map eliminates the effects of elevated
iron from the T1 measurement (3) and
standardizes for the fat signal across scanner
manufacturers.
PDFF is quantified using the LMS IDEAL
method. Parametric maps of T2* may be
optionally be computed using either the
three-point DIXON method or the LMS MOST
method.


Numerous views in the Hepatica v1 interface
can be used to assist analysis.
Operators are able to see live views of
crosshair placements during landmarking
across multiple image planes simultaneously
and adjust contrast where required.
Lesion segmentation will be performed by
navigating through the axial slices using the
crosshair tool and “painting in” the segment
on each slice.
Operators will be required to navigate
through the axial, sagittal and coronal planes
using the crosshair tool to confirm that the
delineation carried out by the device is
accurate. The borders can be adjusted
accordingly.
Views of the segmented liver and lesions are
updated in real-time and can be rotated in a
3D space. Paint, eraser and zooming
functionalities are also available to the
operator.
Where available, operators can review the
position of cT1 and PDFF slices (derived from
LMSv4) within the 3D view of the liver.

Numerous views within the LMSv4 interface
can be used to assist in analysis, Iron-
corrected T1 (cT1), T2* and triglyceride fat
(also known as Proton Density Fat Fraction
(PDFF)) parametric maps can be created
from all supported scanners. R2 maps can
also be utilised to assess the quality of the
map fitting.
Iron- corrected T1 (cT1) displayed using
LMSv4 colourmap, designed to have
maximum contrast on liver parenchymal
tissue.

DICOM 3.0 compliant MR data from
supported MRI scanners.

Quantified metrics and images derived from
the analysis of liver volume and tissue
characteristics are collated into a report for
evaluation and interpretation by a clinician.
Images
Images of the whole liver divided into
Couinaud segments are presented in
greyscale in the report.
Where available, cT1 and PDFF slices are also
presented from the imported LMSv4
analysis.
Values
Whole and segmental liver volume metrics
calculated during analysis are provided in the
report.

Quantified metrics and images derived from
the analysis of liver tissue characteristic on
parametric maps are collated into a report
for evaluation and interpretation by a
clinician.
When segmentation analysis is used a
representative pie- chart is provided based
on the confirmed segmentation contour
from the PDFF map. The voxels within the
segmentation are separated into 5
categories (<5% PDFF, 5-10% PDFF, 10-33%
PDFF, 33-66% PDFF and >66%) to give
proportions based on PDFF. These categories
were chosen based on the work of Kleiner et
al (3) and Satkunasingham et al (4) on the
grading of histological features presented in
Non-Alcoholic Fatty Liver Disease.