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Abstract #2674

HR-DWI with Super-Resolution DLR vs. Conventional DWI: Scan Time, Image Quality and Diagnostic Performance in Hepato-Pancreato-Biliary Tumors

Takeshi Yoshikawa1, Kaori Yamamoto2, Yuichiro Sano2, Masato Ikedo2, Masanori Ozaki2, Masao Yui2, Hiroyuki Nagata3, Takahiro Ueda1, Masahiko Nomura1, Daisuke Takenaka1, Yoshiyuki Ozawa1, and Yoshiharu Ohno1,3
1Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan, 2Canon Medical Systems Corporation, Otawara, Japan, 3Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan

Synopsis

Keywords: Cancer, AI/ML Image Reconstruction, Deep learning Reconstruction, Diffusion

Motivation: Super-resolution deep learning reconstruction (SR-DLR) improves image quality and differentiation capability of hepato-pancreato-biliary (HPB) tumors on high-resolution diffusion-weighted imaging (HR-DWIs) obtained with different NEXs as compared with conventional diffusion-weighted imaging (cDWI).

Goal(s): The goal was to compare capability of SR-DLR for improving image quality and HPB tumor differentiation among cDWI and HR-DWIs with different NEXs.

Approach: This clinical study obtained cDWI and HR-DWIs , and diagnostic performance was statistically compared among all DWIs.

Results: Specificities and accuracies of HR-DWIs with 1, 3 or 5 NEXs were significantly higher than those of cDWI (p<0.05).

Impact: SR-DLR had superior potential for acquisition time reduction with image quality and diagnostic capability improvements on HR-DWI, when compared with cDWI. When applied SR-DLR for HR-DWI, 3 NEX would be better to be applied in this setting.

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Keywords