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Abstract #4196

Deep learning reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas

Masahiro Tanabe1, Mayumi Higashi1, Masatoshi Yamane2, Takahiro Yamaguchi2, Hiroshi Imai3, Omar Darwish4, and Katsuyoshi Ito1
1Radiology, Yamaguchi University Graduate School of Medicine, Ube, Japan, 2Radiological Technology, Yamaguchi University Hospital, Ube, Japan, 3MR Research and Collaboration, Siemens Healthcare K.K., Tokyo, Japan, 4MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany

Synopsis

Keywords: Diffusion Reconstruction, Diffusion Reconstruction

Motivation: Deep Resolve techniques would improve DWI image quality.

Goal(s): To investigate the feasibility of DR-based full-size and reduced FOV DWI (fDWI and rDWI), compared with the fDWI and rDWI.

Approach: In 66 patients, fDWI, DR-fDWI, rDWI and DR-rDWI data sets were analyzed qualitatively using a 4-point scale. Contrast ratios between the pancreatic parenchyma and fat tissue or pancreatic lesion were also measured.

Results: DR-based fDWI and rDWI reduced noise and distortion, sharply delineate pancreas edges, and improve overall image quality. Especially, the use of DR in rDWI increased the contrast of the pancreas and clearly delineated PDACs and NEN.

Impact: Reduced FOV DWI technique decreases ghosting and susceptibility artifacts and increases spatial resolution, but decreases the signal of pancreas. Combination of reduced FOV DWI technique and DR decreased noise, improved CR pancreas-fat, and clearly delineated PDACs and NEN.

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Keywords