Update README.md

README.md

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-Model summary 
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-Developer 
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-Microsoft Research, Health Futures 
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-Description 
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-SNRAware models improve the signal-noise-ratio of MR complex images. It is provided as research-only model for reproducibility of the corresponding research. 
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-Model architecture 
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-This model is an instantiation of the Imaging Transformer architectures, where local, global and inter-frame signal and noise characteristics are learned. 
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-Parameters 
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-SNRAware-small: 27.7million parameters 
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-SNRAware-medium: 55.1million parameters 
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-SNRAware-large: 109million parameters 
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-Inputs 
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-Input to model is 5D tensor [B, C, T/F, H, W] for batch, channel, time/frame, height and width. The last channel in input is the g-factor map. 
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-Context length 
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-Not applicable 
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-Outputs 
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-Output tensor is in the shape of [B, C-1, T/F, H, W]. 
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-GPUs 
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-16x B200 
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-Training time 
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-7 days 
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-Public data summary (or summaries) 
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-N/A 
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-Dates 
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-Nov 2025 
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-Status 
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-Model checkpoints can be downloaded from https://huggingface.co/microsoft/SNRAware 
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-Model may be subject to updates post-release. 
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-Release date 
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-Release date in the EU (if different) 
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-Dec 2025  
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-License 
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-MIT license 
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-Model dependencies: 
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-N/A 
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-List and link to any additional related assets 
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-https://github.com/microsoft/SNRAware/ 
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-Acceptable use policy 
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-N/A 
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+# Model summary 
+
+| Syntax      | Description |
+| ----------- | ----------- |
+| Developer      | Microsoft Research, Health Futures        |
+| Description   | SNRAware models improve the signal-noise-ratio of MR complex images. It is provided as research-only model for reproducibility of the corresponding research.        |
+| Model architecture       | This model is an instantiation of the Imaging Transformer architectures, where local, global and inter-frame signal and noise characteristics are learned.         |
+| Parameters      | SNRAware-small: 27.7million parameters;  SNRAware-medium: 55.1million parameters; SNRAware-large: 109million parameters         |
+| Inputs      | Input to model is 5D tensor [B, C, T/F, H, W] for batch, channel, time/frame, height and width. The last channel in input is the g-factor map.         |
+| Context length      | Not applicable        |
+| Outputs      | Output tensor is in the shape of [B, C-1, T/F, H, W].        |
+| GPUs      | 16x B200          |
+| Training time       | 7 days        |
+| Public data summary (or summaries)      | Not applicable        |
+| Dates      | Nov 2025        |
+| Status      |  Model checkpoints can be downloaded from https://huggingface.co/microsoft/SNRAware. Model may be subject to updates post-release.       |
+| Release date; Release date in the EU (if different) | Dec 2025 |
+| License | MIT license |
+| Model dependencies: | N/A |
+| List and link to any additional related assets | https://github.com/microsoft/SNRAware/ |
+| Acceptable use policy | N/A |
   
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 1. Model overview 
 
 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. It is provided as research-only model for reproducibility of the corresponding research. 
@@ -98,8 +40,6 @@ This model is only suited to denoise complex MR images with the unitary noise sc
 
 Any deployed use case of the model --- commercial or otherwise --- is out of scope. Although we evaluated the models using publicly available research benchmarks, the models and evaluations are intended for research use only and not intended for deployed use cases. 
 
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 2.3 Distribution channels 
 
 Model source code is available at https://github.com/microsoft/SNRAware/ 
@@ -136,11 +76,9 @@ Model was tested on the hold-out test sets for quality metrics of PSNR and SSIM.
 
 Please refer to the publication for more results. 
 
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 4.1 Long context 
 
 Not relevant 
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 4.2 Safety evaluation and red-teaming 
 
@@ -153,5 +91,4 @@ This model is not a frontier model.
 6. Requests for additional information may be directed to MSFTAIActRequest@microsoft.com.   
 
 7. Appendix 
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 None