Dr. Chunrui Zou Profile

Dr. Chunrui Zou

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Publications (1)

Proceedings Article | 29 May 2024 Paper

Sat2Nu: a modular deep learning pipeline for converting fat-suppressed breast MRIs to nonfat-suppressed images with foreseeable applications in abbreviated breast MRI

Kalina Slavkova, Nehal Doiphode, Chunrui Zou, Alex Anh-Tu Nguyen, Arunima Sharma, Anne Marie McCarthy, Despina Kontos

Proceedings Volume 13174, 1317416 (2024) https://doi.org/10.1117/12.3027028

KEYWORDS: Education and training, Magnetic resonance imaging, Breast, Batch normalization, Deep learning, Spatial resolution, Image segmentation, Spatial learning, Reproducibility, Breast cancer

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While standard of care breast MRI primarily includes T1-weighted (T1w) fat-suppressed images, nonfat-suppressed images are not always included but may be needed to detect fat necrosis or fatty lesions. With the advent of abbreviated MRI to increase the accessibility of MRI for breast cancer screening, it is unlikely that imaging exams will contain both fat- and nonfat-suppressed images. Additionally, nonfat-suppressed images are integral for downstream quantitative analyses. Deep learning has seen increased use in medical imaging for contrast synthesis; however, there is limited work in the breast. This study aims to develop a reproducible, modular deep learning framework called Sat2Nu for generating nonfat-suppressed images from fat-suppressed inputs with limited training data. We retrospectively analyzed 2D slices from 643 bilateral sagittal T1w MRI screening exams with corresponding fat- and nonfat-suppressed scans from the University of Pennsylvania. One central slice was selected from each breast to yield 1,286 2D images. We trained a U-Net architecture on the entire dataset, where nonfat-suppressed images served as the ground truth. We randomly selected 20% of the data as an in-distribution validation set. The normalized root mean square error (NRMSE) and structural similarity index (SSIM) were used as performance metrics. We achieved a training NRMSE and SSIM of 0.143 and 0.855, respectively. Validation metrics on the in-distribution validation set were, respectively, 0.099 and 0.889. In conclusion, our preliminary results demonstrate representational capacity for the network to learn nonfat-suppressed contrast from fat-suppressed MRIs, which could develop into a promising solution for generating missing scans in the abbreviated setting and for downstream quantitative analyses dependent on nonfat-suppressed images. Current efforts include external validation and investigating other generative networks and loss functions for improving generalizability. Importantly, we are focusing on designing a reproducible pipeline that would allow future users to easily implement different architectures.

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