Deep learning-based velocity antialiasing of 4D-flow MRI.

ABSTRACT:

Purpose

To develop a convolutional neural network (CNN) for the robust and fast correction of velocity aliasing in 4D-flow MRI.

Methods

This study included 667 adult subjects with aortic 4D-flow MRI data with existing velocity aliasing (n = 362) and no velocity aliasing (n = 305). Additionally, 10 controls received back-to-back 4D-flow scans with systemically varied velocity-encoding sensitivity (vencs) at 60, 100, and 175 cm/s. The no-aliasing data sets were used to simulate velocity aliasing by reducing the venc to 40%-70% of the original, alongside a ground truth locating all aliased voxels (153 training, 152 testing). The 152 simulated and 362 existing aliasing data sets were used for testing and compared with a conventional velocity antialiasing algorithm. Dice scores w

SUBMITTER: Berhane H 

PROVIDER: S-EPMC9050855 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature