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Abstract #3809

The usefulness of 4D convolution in deep-learning-based noise reduction for low-SNR body DWI

Yasuhiko Tachibana1, Hiroki Tsuchiya1, Riwa Kishimoto1, Tokuhiko Omatsu1, Shinichiro Mori1, Takayuki Obata1, and Tatsuya Higashi1
1National institutes for Quantum science and Technology, Chiba, Japan


Deep-learning-based slice-by-slice noise reduction may not be suitable for low-SNR body DWI that contains insufficient information in the original single slice. Moreover, averaging multiple acquisitions after denoising to avoid this problem is insufficient because it causes blurring owing to a mismatch between acquisitions. Herein, we designed a neural network that utilises 4D convolution to incorporate adjacent slices and multiple acquisitions simultaneously for a slice to achieve adequate denoising. The results support the utility of the proposed method in comparison with the usual slice-by-slice method and averaging.

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