Deep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study

This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images.

Our retrospective study incorporated 34 patients diagnosed with biliary and pancreatic disorders. We obtained MRCP images using a single breath-hold MRCP on a 3T MRI system. We reconstructed MRCP images with ER-DLR (matrix = 768 × 960) and without ER-DLR (matrix = 256 × 320). Quantitative evaluation involved measuring the signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) between the common bile duct and periductal tissues, and slope. Two radiologists independently scored image noise, contrast, artifacts, sharpness, and overall image quality for the 2 image types using a 4-point scale. Results are expressed as median and interquartile range (IQR), and we compared quantitative and qualitative scores employing the Wilcoxon test.

In quantitative analyses, ER-DLR significantly improved SNR (21.08 [IQR: 14.85, 31.5] vs 15.07 [IQR: 9.57, 25.23], P  < 0.001), CNR (19.29 [IQR: 13.87, 24.98] vs 11.23 [IQR: 8.98, 15.74], P  < 0.001), contrast (0.96 [IQR: 0.94, 0.97] vs 0.9 [IQR: 0.87, 0.92], P  < 0.001), and slope of MRCP (0.62 [IQR: 0.56, 0.66] vs 0.49 [IQR: 0.45, 0.53], P  < 0.001). The qualitative evaluation demonstrated significant improvements in the perceived noise ( P  < 0.001), contrast ( P  = 0.013), sharpness ( P  < 0.001), and overall image quality ( P  < 0.001).

ER-DLR markedly increased the resolution, SNR, and CNR of breath-hold-MRCP compared to cases without ER-DLR.