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Abstract #0388

Self-supervised Cardiac MRI Denoising with Deep Learning

Junshen Xu1 and Elfar Adalsteinsson1
1Massachusetts Institute of Technology, Cambridge, MA, United States

Image denoising is of great importance for medical imaging system, since it can improve image quality for disease diagnosis and further image processing. In cardiac MRI, images are acquired at different time frames to capture the cardiac dynamic. The correlation among different time frames makes it possible to improve denoising results with information from other time frames. In this work, we propose a self-supervised deep learning framework for cardiac MRI denoising. Evaluation on in vivo data with different noise statistics shows that our method has comparable or even better performance than other state-of-the-art unsupervised or self-supervised denoising methods.

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