Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.

Purpose: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.

Materials And Methods: This was a retrospective study of 52 patients undergoing conventional prostate MRI with raw image data reconstructed using both conventional 2D Cartesian and DLR algorithms. Simulated shortened DWI acquisition times were performed by reconstructing images using DLR datasets harboring a reduced number of excitations (NEX). Two radiologists independently evaluated the image quality using a 4-point Likert scale. Signal-to-noise ratio (SNR) analysis was performed for the entire cohort and a subset of patients identified as having clinically significant prostate cancer identified at MRI, and later confirmed by pathology.

Results: Radiologists perceived less image noise for both restricted and large field of view (FOV) standard NEX dataset DLR diffusion images compared to conventionally reconstructed images with good interreader agreement. Diagnostic image quality was maintained for all DLR images using variably reduced NEX compared to conventionally reconstructed images employing the standard NEX. Improved signal to noise was observed for the restricted FOV DLR images compared to conventional reconstruction using standard NEX. DLR diffusion images derived from reduced NEX datasets translated to time reductions of up to 68 % and 39 % for the restricted and large FOV series acquisitions, respectively.

Conclusion: DLR of diffusion weighted images can reduce image noise at standard NEX and potentially reduce prostate MRI exam time when utilizing reduced NEX datasets without sacrificing diagnostic image quality.