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Abstract #3585

Untrained Modified Deep Decoder for Joint Denoising and Parallel Imaging Reconstruction

Sukrit Arora1, Volkert Roeloffs1, and Michael Lustig1
1UC Berkeley, Berkeley, CA, United States

An untrained deep learning model based on a Deep Decoder was used for image denoising and parallel imaging reconstruction. The flexibility of the modified Deep Decoder to output multiple images was exploited to jointly denoise images from adjacent slices and to reconstruct multi-coil data without pre-determed coil sensitivity profiles. Higher PSNR values were achieved compared to the traditional methods of denoising (BM3D) and image reconstruction (Compressed Sensing). This untrained method is particularly attractive in scenarios where access to training data is limited, and provides a possible alternative to conventional sparsity-based image priors.

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