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Abstract #3137

Deep learning-based image quality and spatial resolution improvement for Diffusion Weighted Imaging in liver

Jihun Kwon1, Kohei Yuda2, Masami Yoneyama1, Yasutomo Katsumata3, and Marc Van Cauteren3
1Philips Japan, Tokyo, Japan, 2Tokyo Metropolitan Police Hospital, Nakano, Japan, 3Philips Healthcare, Best, Netherlands

Synopsis

Keywords: Liver, Diffusion/other diffusion imaging techniques

Diffusion-weighted imaging (DWI) in liver plays a significant role for lesion characterization and staging of fibrosis. Single-shot echo-planar imaging (ssh-EPI) readout is typically used; however, spatial resolution of ssh-EPI-DWI is limited by acquisition time. In this study, we investigated the use of prototype AI-based reconstruction technique (SmartSpeed Precise Image) to improve the image quality of liver ssh-EPI-DWI images. The image quality was compared between conventional Compressed-SENSE (C-SENSE), SmartSpeed AI, and SmartSpeed Precise Image. Volunteer data demonstrated a significant improvement of sharpness in DWI images and ADC map, and reduction of ringing artifact compared with C-SENSE and SmartSpeed AI reconstruction.

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Keywords