|
Download data/README.md from introvoyz045/flexus: direct link, hf CLI and curl.
- Browser
- Download file 3.98 kB
-
https://huggingface.co/datasets/introvoyz045/flexus/resolve/main/data/README.md
- Command line
-
hf download hf://datasets/introvoyz045/flexus/data/README.md
-
curl -L -o README.md https://huggingface.co/datasets/introvoyz045/flexus/resolve/main/data/README.md
3.98 kB
Image Reconstruction Scripts
The repository includes all necessary scripts to reconstruct the received ultrasound data for the in-vivo datasets presented in the paper as well as the raw data from those recordings. The various functions and scripts are:
- output_map.m - Generates the output data mapping from each coarse uBF such that the data stream can be mapped to the correct coarse uBF within the array
- grid_gen.m - Generates the imaging grid for DAS reconstruction. It uses a polar grid with radii starting from the virtual center of the array for maximum sector angle all the way to the maximum imaged depth, and then transforms this into a Cartesian grid
- delay_gen.m - Generates ASIC delays for transmit to utilize them in determining the TX delay for the array. It can generate delays for both XZ centric and YZ centric imaging
- file_rename.m - Splits the data input into per beam datsets, either as files or structs. It supports older data formats including CSVs and per-beam output files for backward compatibility
- channel_reorder.m - Rearranges the data input into per pixel data from top-left of the array to bottom-right by compressing the N_outxN_bypass input data into N_pixelxN_pixel columns per beam. This involves replicating the data by sqrt(N_bypass)xsqrt(N_bypass) to make up for reduced channel count. It also removes the data for unused elements and zero-pads by the T/R switch delay to ensure correct time indices. It supports older data formats including CSVs and per-beam output files for backward compatibility
- traumas_das.m - Performs the delay-and-sum beamforming for the imaged data onto the reconstruction grid using linear interpolation. It involves determining both TX and RX distance, converting them into a return time and then a time index and interpolating the echo from each point in the grid using the received data from all channels based on this time index
- traumas_das_3d.m - Same as traumas_das but extended to 3D
- image_recon_rt.m - Takes raw ultrasound real-time 2-D data (
*_rt_raw.zipin directory datasets_lung) and uses DAS to generate real-time image frames. The script handles unzipping and file handling if necessary and outputs a.matfile which contains the reconstruction grid coordinates and corresponding B-Mode values. It also optionally generates a video that contains the B-Mode slices - image_recon_3d.m - Takes raw ultrasound 3-D data (
*_3d_raw.zipin directory datasets_neck) and uses DAS to generate volumetric B-Mode data. The script handles unzipping and file handling if necessary. The parameterorthogonaldetermines if the reconstruction is done along the direction orthogonal to the acquisition plane. It outputs a.matfile which contains the reconstruction grid coordinates and corresponding B-Mode values. It also optionally generates a video that contains the B-Mode slices - bmode_image_plot.m - Plots 2-D and 3-D reconstructed B-mode data in
*_3d_recon.matfiles generated by image_recon_3d.m - multiframe_bmode_plot.m - Plots real-time 2-D reconstructed B-mode data in
*_rt_recon.matfiles generated by image_recon_rt.m - lung_seashore_plot.m - Generates M-Mode Seashore plots from real-time recordings of pleural region from files
*_rt_recon.matfor B-Mode data - lung_motion_brightness.m - Tracks brightness on real-time recordings of pleural region. This script uses files
*_rt_recon.matfor B-Mode data and*_motion.csvfor chest height information. Requires MATLAB Image Processing Toolbox. - lung_chest_position.py - Python script for extracting chest motion from video recording of the subject breathing. This script also adds markers to the video tracking the chest position. Requires the following packages: OpenCV (cv2), NumPy, SciPy Signal Processing (scipy.signal), PyPlot (matplotlib) and the module sys.
Raw data for image reconstruction is available on Zenodo -- DOI 10.5281/zenodo.20738351