Results 161 to 170 of about 13,553 (263)

Prospective Head Motion Correction in T1‐ and T2‐Weighted Long Echo Train Sequences Using Servo Navigation

open access: yesMagnetic Resonance in Medicine, Volume 96, Issue 4, Page 1741-1754, October 2026.
ABSTRACT Purpose To integrate MR‐based servo navigation in MPRAGE and 3D‐TSE sequences and demonstrate its potential for prospective head motion correction in structural imaging. Methods Repeated modules of servo navigators were inserted before each preparation pulse of MPRAGE and 3D‐TSE sequences (before‐prep) for rapid convergence of motion parameter
Matthias Serger   +7 more
wiley   +1 more source

DeepRelaxo: Fast Mono‐Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self‐Supervised Deep Learning

open access: yesMagnetic Resonance in Medicine, Volume 96, Issue 3, Page 1293-1302, September 2026.
ABSTRACT Purpose We introduce DeepRelaxo, a fast and generalizable deep learning method for estimating brain R2* maps from multi‐echo gradient echo (ME‐GRE) acquisitions with arbitrary echo configurations, including shortened echo trains for accelerated scans.
Samiha Prima   +3 more
wiley   +1 more source

Time‐Conditioned Zero‐Shot Self‐Supervised Reconstruction for Accelerated 3D Ultra‐Low‐Field MRI

open access: yesMagnetic Resonance in Medicine, Volume 96, Issue 3, Page 1303-1312, September 2026.
ABSTRACT Purpose Ultra‐low‐field (ULF) MRI provides a cost‐effective, portable imaging option but has relatively low SNR and long acquisition times compared to standard clinical scans. This study presents a time‐conditioned zero‐shot self‐supervised learning image reconstruction framework (ULF‐ZS‐SSL) to accelerate 3D‐acquired single‐coil ULF MRI ...
Mart W. J. van Straten   +6 more
wiley   +1 more source

Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI. [PDF]

open access: yesJ Imaging
Rios-Perez JD   +4 more
europepmc   +1 more source

Physics‐Guided Neural Network for Quantitative Parameter Mapping Using Balanced Steady State Free Precession MRI

open access: yesMagnetic Resonance in Medicine, Volume 96, Issue 3, Page 1313-1322, September 2026.
ABSTRACT Purpose To propose a new method using a physics‐guided neural network for quantitative parameter mapping in balanced steady‐state free precession (bSSFP) imaging. Theory and Methods We trained physics‐guided neural networks with a multilayer perceptron using simulated bSSFP signals generated from tissue parameters (T1, T2,Meffc, ∆f and φRF ...
Hye‐Ryeong Choi   +2 more
wiley   +1 more source

FMDNet: Spatial-frequency feature routing for low-dose CT denoising. [PDF]

open access: yesJ Appl Clin Med Phys
Yao Y   +6 more
europepmc   +1 more source

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