Update README.md

README.md

@@ -104,11 +104,16 @@ For a typical MR image data, the inference time on H100 is ~3-7s for SNRAware-sm
 
 ## Evaluation
 
+Both in-distribution and generalization tests were performed. For the in-distribution tests, the MR noise was generated and added to clean images to lower its SNR.
+Model was applied to restore the image quality. PSNR and SSIM were computed against the ground-truth. For the generalization tests, new data outside the training cohort
+were acquired for different imaging sequences, field strength, anatomies and resolution. Model outputs were compared to raw inputs and scored by clinicians to judge quality.
+
 Please refer to the publication for evaluation details.
 
 ### Results
 
-Please refer to the publication for results.
+Imaging transformer model outperformed competing model architectures in the in-distribution tests. 
+The SNRAware training also enabled imaging transformer models to generalize well to unseen applications without further training.
 
 #### Summary
 
@@ -159,6 +164,16 @@ Pytorch 2.8.0+cu128
     note ={PMID: 41123451},
     URL = {https://doi.org/10.1148/ryai.250227}
 }
+
+@misc{xue2024imagingtransformermridenoising,
+      title={Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy}, 
+      author={Hui Xue and Sarah Hooper and Azaan Rehman and Iain Pierce and Thomas Treibel and Rhodri Davies and W Patricia Bandettini and Rajiv Ramasawmy and Ahsan Javed and Zheren Zhu and Yang Yang and James Moon and Adrienne Campbell and Peter Kellman},
+      year={2024},
+      eprint={2404.02382},
+      archivePrefix={arXiv},
+      primaryClass={eess.IV},
+      url={https://arxiv.org/abs/2404.02382}, 
+}
 ```
 
 ## Model Card Contact