Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

ABSTRACT:

Objective

Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images.

Materials and methods

A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020. Likely clinically significant prostate cancer (csPCa) lesions (PI-RADS ≥ 4) were delineated by expert radiologists. T2-weighted scans were retrospectively undersampled, simulating accelerated protocols. DL reconstruction (DLRecon) and diagnostic DL detection (DLDetect) were developed. The effect on the partial area under (pAUC), the Free-Response Operating Char

SUBMITTER: van Lohuizen Q 

PROVIDER: S-EPMC11519109 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature