Keywords: Lung, Machine Learning/Artificial Intelligence, Self-supervised DenoisingSelf-supervised learning denoising networks can be applied to noisy only datasets when the clean-noisy pairs are not available, which is suitable for dynamic contrast-enhanced (DCE) pulmonary imaging where SNR is low and no ground truth clean image can be acquired. Blind-spot network with asymmetric pixel-shuffle downsampling (AP-BSN) was trained to utilize the advantages of self-supervised BSN and improve the denoising performance for pixel-wise independent and dependent noise. AP-BSN denoised images showed improved image quality by human reader assessment. AP-BSN showed generalization ability from human subjects to swine and from 1.5T to 3T.
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