Abstract #2442

Deep learning-based DWI Denoising method that suppressed the "instability" problem

Hayato Nozaki^1,2, Yasuhiko Tachibana³, Yujiro Otsuka⁴, Wataru Uchida^1,2, Yuya Saito¹, Koji Kamagata¹, and Shigeki Aoki¹

¹Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan, ²Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan, ³Department of Molecular Imaging and Theranostics National Institute of Radiological Sciences National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan, ⁴Miliman, Tokyo, Japan

Deep learning-based noise reduction technique for DWI contains a risk of outputting values that are greatly deviating from what it should be because of the instability problem of deep learning. The neural network model was designed in this study to suppress this risk which can fix the generated value for each pixel within the range of values of neighboring pixels in the original image. The results of the volunteer study suggested that the proposed method has potential to provide effective denoising beside suppressing the instability risk.

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