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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