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September 17, 2025

Deep Learning-Reconstructed Reduced-field-of-view DWI in Rectal Cancer：Enhanced Rectal Wall Layering and Improved T Staging Accuracy

Key Points

Enhanced T-staging accuracy was achieved with DLR-based reduced-field-of-view DWI for rectal cancer.
Spatial resolution improved significantly, particularly enhancing the mucosa-submucosa-muscularis layer discriminability.
Comparison with standard techniques showed that DLR-enhanced imaging provides superior image quality and diagnostic capabilities.
The study establishes the clinical feasibility of combining deep learning techniques with DWI for accurate rectal cancer assessment.

Abstract

Motivation: Early-stage rectal cancer staging remains challenging with conventional T2-weighted imaging (T2WI), highlighting the need for high-quality diffusion-weighted imaging (DWI) to improve diagnostic accuracy. Goal(s): To assess the performance of deep learning reconstruction (DLR) combined with reduced-field-of-view (rFOV) in achieving high-quality, high-spatial-resolution DWI for rectal cancer. Approach: This study prospectively compared three DWI sequences: DLR-based rFOV (rFOVDL), standard rFOV (rFOVSTA), and standard full-field-of-view (fFOVSTA) DWI, assessing the image quality and regional staging performance. Results: rFOVDL DWI significantly improved spatial resolution and image quality, particularly enhancing the discriminability of mucosa-submucosa-muscularis layers, which in turn significantly improved T-staging accuracy, especially for early-stage tumors. Impact: This study demonstrated the clinical feasibility of DLR-enhanced rFOV DWI for rectal cancer. It improved the spatial resolution and the discriminability of mucosa-submucosa-muscularis layers, which facilitated higher T-staging accuracy, especially in early-stage tumors.

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