Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning.

ABSTRACT:

Purpose

To investigate the acceleration of 4D-flow MRI using a convolutional neural network (CNN) that produces three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase.

Methods

A fully 3D CNN using a U-net architecture was trained in a block-wise fashion to take complex images from three flow encodings and to produce three real-valued images for each velocity component. Using neurovascular 4D-flow scans (n = 144), the CNN was trained to predict velocities computed from four flow encodings by standard reconstruction including correction for residual background phase offsets. Methods to optimize loss functions were investigated, including magnitude, complex difference, and uniform velocity weightings. Subsequ

SUBMITTER: Kim D 

PROVIDER: S-EPMC9712238 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature