Abstract
Objectives
To determine whether AI-reconstructed prostate MRI at reduced acquisition times maintains prostate cancer (PCa) detection performance comparable to conventional scans.
Materials and methods
This multicenter, retrospective, consecutive-cohort study included 120 multi-coil T2-weighted prostate MRI scans from the University Medical Center Groningen (UMCG) and 312 publicly available scans from New York University (NYU). An AI model trained on the NYU data was tested on retrospectively undersampled UMCG scans at acceleration factors R = 3 and R = 6 (i.e., data reduction in k-space). Eight experienced radiologists participated in a multi-reader multi-case PCa detection study. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC). Histopathology and PI-RADS <= 2 findings served as reference standards. Multiple image quality metrics were subjectively evaluated using a 4-point Likert scale.
Results
No statistically significant reduction in PCa detection was observed at an MRI acceleration up to R = 6 (
p
= 0.08). AUROC values were 0.86 (95% CI: 0.74-0.90) for R = 1, 0.82 (0.72-0.88) for R = 3, and 0.80 (0.70-0.86) for R = 6. Compared to R = 1, R = 3 scans were rated by radiologists to have significantly improved sharpness (+0.2