What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Image quality evaluation of multi-sensitivity diffusion-weighted imaging for rectal cancer with small field of view based on deep learning

Key Points

FOCUS-MUSE images with deep learning demonstrate superior image quality and lesion visibility.
Signal-to-noise ratio and contrast-to-noise ratio significantly improved with deep learning enhancement (p < 0.01).
Evaluation involved 60 patients diagnosed with rectal adenocarcinoma through rectal MR examination.
Enhancing diffusion-weighted imaging with deep learning may improve colorectal cancer staging accuracy.

Abstract

Motivation: Diffusion-weighted magnetic resonance imaging (DW-MRI) is essential for rectal cancer imaging. Goal(s): This study evaluated the image quality, rectal contours, lesion visibility of low-field multiple sensitivity diffusion-weighted imaging enhanced with deep learning reconstruction , assessing their signal-to-noise ratio and contrast-to-noise ratio . Approach: 60 patients who underwent rectal MR examination at our hospital since January 2024 and were confirmed to have rectal adenocarcinoma by pathology were included. Results: The SNR , CNR , image quality, rectal contour, and lesion visibility of FOCUS-MUSE images with DL were superior to those without DL (p < 0.01 for SNR, CNR, image quality, rectal contour, p = 0.02 for lesion visibility). Impact: This study shows that DLR-based FOCUS-MUSE DWI enhances image quality, rectal contour, lesion visibility, SNR, and CNR, suggesting its potential to improve colorectal cancer staging accuracy.

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Image quality evaluation of multi-sensitivity diffusion-weighted imaging for rectal cancer with small field of view based on deep learning

Key Points

Abstract

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