Highly accelerated free-breathing real-time phase contrast cardiovascular MRI via complex-difference deep learning.

ABSTRACT:

Purpose

To develop and evaluate a real-time phase contrast (PC) MRI protocol via complex-difference deep learning (DL) framework.

Methods

DL used two 3D U-nets to separately filter aliasing artifact from radial real-time velocity-compensated and complex-difference images. U-nets were trained with synthetic real-time PC generated from electrocardiograph (ECG) -gated, breath-hold, segmented PC (ECG-gated segmented PC) acquired at the ascending aorta of 510 patients. In 21 patients, free-breathing, ungated real-time (acceleration rate = 28.8) and ECG-gated segmented (acceleration rate = 2) PC were prospectively acquired at the ascending aorta. Hemodynamic parameters (cardiac output [CO], stroke volume [SV], and mean velocity at peak systole [peak mean velocity]) were measured f

SUBMITTER: Haji-Valizadeh H 

PROVIDER: S-EPMC8145775 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature