Deep Learning‐Based Dynamic Segmentation of the Left Atrium in 4D Flow MRI
ABSTRACT Purpose To design a deep learning pipeline for automated, time‐resolved segmentation of the left atrium (LA) from 4D flow MRI data. Methods We studied 100 individuals including 65 patients with atrial fibrillation (AF) and 35 healthy subjects (HS) resulting in 2530 4D flow MRI time‐volumes acquired in different centers and scanners.
Jonas Leite +16 more
wiley +1 more source
“Two‐For‐One”: 4D Cardiac and Pulmonary MR Imaging From a Single Acquisition Using bSTAR
ABSTRACT Purpose To generate 4D (3D+time) images of both the heart and lungs using a single volumetric radial free‐breathing bSSFP dual echo acquisition (bSTAR) with two image reconstructions within a single pipeline deployed inline at 0.55 and 1.5 T.
Pierre Daudé +9 more
wiley +1 more source
ABSTRACT Purpose To develop a unified image reconstruction framework that bridges real‐time and gated cardiac MRI, including quantitative MRI. Methods We introduce generative multitasking, which learns subject‐ and dataset‐specific implicit neural temporal bases from sequence timings and an interpretable latent space for cardiac and respiratory motion.
Xinguo Fang, Anthony G. Christodoulou
wiley +1 more source
Feasibility study of prospective gated tomosynthesis using a wide-angle carbon nanotube enabled, stationary digital chest tomosynthesis scanner: In-vivo evaluation in a porcine subject. [PDF]
Billingsley A +4 more
europepmc +1 more source
Flow 2.0: A Standardized Toolbox for Real‐Time Phase‐Contrast MRI of Neurofluid Dynamics
ABSTRACT Purpose Real‐time phase‐contrast MRI (RT‐PC) enables continuous quantification of physiological flow dynamics across multiple temporal frequencies, but its broader application is limited by complex and fragmented post‐processing workflows.
Pan Liu, Olivier Balédent
wiley +1 more source
Mapping cardiac and respiratory pulsations simultaneously with functional connectivity in the rat brain using zero echo time fMRI. [PDF]
Paasonen E +10 more
europepmc +1 more source
Training Deep Learning Based Dynamic MR Image Reconstruction Using Synthetic Fractals
ABSTRACT Purpose To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets.
Anirudh Raman +10 more
wiley +1 more source
Editorial: Unlocking the potential of prenatal MRI: advances in fetal brain, heart, and placenta imaging. [PDF]
Moghari MH +5 more
europepmc +1 more source
ABSTRACT Purpose To validate the performance of an open‐source cardiac diffusion sequence framework and to investigate hardware‐based performance differences. Methods Three second order motion compensated sequence files were compiled using Pulseq and applied across different MRI systems to image five volunteers.
Eric Arbes +7 more
wiley +1 more source
Fluorescence-lifetime optical electrophysiology in contracting cardiomyocytes. [PDF]
Millar E +10 more
europepmc +1 more source

