Update README.md

README.md

@@ -8,87 +8,76 @@ license: mit
 
 A deep learning imaging AI model with imaging transformer, for MR denoising.
 
-```latex
-@article{
-    doi:10.1148/ryai.250227,
-    author = {Xue, Hui and Hooper, Sarah M. and Pierce, Iain and Davies, Rhodri H. and Stairs, John and Naegele, Joseph and Campbell-Washburn, Adrienne E. and Manisty, Charlotte and Moon, James C. and Treibel, Thomas A. and Hansen, Michael S. and Kellman, Peter},
-    title = {SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation},
-    journal = {Radiology: Artificial Intelligence},
-    volume = {0},
-    number = {ja},
-    pages = {e250227},
-    year = {0},
-    doi = {10.1148/ryai.250227},
-    note ={PMID: 41123451},
-    URL = {https://doi.org/10.1148/ryai.250227}
-}
-```
+![image](https://cdn-uploads.huggingface.co/production/uploads/690cf6928b7eacea549fd405/ohWF8O1xe0rOJWvhXmxLf.png)
 
 ## Model Details
 
+SNRAware is an imaging transformer model trained to denoise complex MR image data. 
+Imaging transformers use attention modules to capture local, global, and inter-frame 
+signal and noise characteristics. Denoising training used the SNRAware method, 
+generating MR-realistic noises on the fly to create low SNR samples with unitary noise scaling. 
+Model received low SNR complex images and g-factor maps as input, producing high SNR complex images as output. 
 
+Please refer to the publication for technical details.
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
+The published model was instantiated with a high-res net (HRnet) backbone and consists of multiple imaging attention modules. 
+Two models were published: 
+  . SNRAware-small: a 27.7 million parameter model
+  . SNRAware-medium: a 55.1 million parameter model
 
+Input to model is 5D tensor [B, C, T/F, H, W] for batch, channel, time/frame, height and width. Output tensor is in the shape of 
+[B, C-1, T/F, H, W]. The last channel in input is the g-factor map.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Microsoft Research, Health Futures
+- **Model type:** Imaging Transformer
+- **License:** MIT
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/microsoft/SNRAware
+- **Paper [optional]:** https://pubs.rsna.org/doi/10.1148/ryai.250227
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
+The model should accept the reconstructed MR complex images and g-factor maps and produce the denoising images. 
+The model expects the unitary noise level in the input images. This can be achieved by reconstruction images with 
+Gadgetron framework.
+
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+Denoise the complex MR images.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+No out-of-scope use should be attempted. Input to the model should be SNRUnit reconstructed.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+No bias and risks are associated with this model. Only limitation is input data should have unitary noise scaling.
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+None
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Please refer to the documentation in https://github.com/microsoft/SNRAware to get started.
 
 ## Training Details
 
@@ -96,121 +85,82 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+Training data is not shared.
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+Please refer to the publication for training details.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** fp32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+For a typical MR image data, the inference time on H100 is ~3-7s for SNRAware-small and ~5-11s for SNRAware-medium.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Please refer to the publication for evaluation details.
 
 ### Results
 
-[More Information Needed]
+Please refer to the publication for results.
 
 #### Summary
 
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
 ## Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
+- **Hardware Type:** B200
+- **Hours used:** 100
+- **Cloud Provider:** Azure
+- **Compute Region:** westus2
+- **Carbon Emitted:** 7.5
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Imaging Transformer with a high-res net backbone
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Azure GPU VMs
 
 #### Hardware
 
-[More Information Needed]
+NVIDIA B200 x16
 
 #### Software
 
-[More Information Needed]
+Pytorch 2.8.0+cu128
 
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```latex
+@article{
+    doi:10.1148/ryai.250227,
+    author = {Xue, Hui and Hooper, Sarah M. and Pierce, Iain and Davies, Rhodri H. and Stairs, John and Naegele, Joseph and Campbell-Washburn, Adrienne E. and Manisty, Charlotte and Moon, James C. and Treibel, Thomas A. and Hansen, Michael S. and Kellman, Peter},
+    title = {SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation},
+    journal = {Radiology: Artificial Intelligence},
+    volume = {0},
+    number = {ja},
+    pages = {e250227},
+    year = {0},
+    doi = {10.1148/ryai.250227},
+    note ={PMID: 41123451},
+    URL = {https://doi.org/10.1148/ryai.250227}
+}
+```
 
 ## Model Card Contact
 
-[More Information Needed]
+Hui Xue, xueh@microsoft.com