Accelerated multicontrast volumetric imaging with isotropic resolution for improved peri-infarct characterization using parallel imaging, low-rank and spatially varying edge-preserving sparse modeling

2017 ◽  
Vol 79 (6) ◽  
pp. 3018-3031 ◽  
Author(s):  
Li Zhang ◽  
Peng Lai ◽  
Mihaela Pop ◽  
Graham A. Wright
Keyword(s):  
Parallel Imaging ◽  
Low Rank ◽  
Edge Preserving ◽  
Sparse Modeling ◽  
Volumetric Imaging ◽  
Isotropic Resolution ◽  
Spatially Varying

scholarly journals High-Resolution Cardiovascular MRI by Integrating Parallel Imaging With Low-Rank and Sparse Modeling

2013 ◽  
Vol 60 (11) ◽  
pp. 3083-3092 ◽  
Author(s):  
Anthony G. Christodoulou ◽  
Haosen Zhang ◽  
Bo Zhao ◽  
T. Kevin Hitchens ◽  
Chien Ho ◽  
...  
Keyword(s):  
High Resolution ◽  
Parallel Imaging ◽  
Low Rank ◽  
Cardiovascular Mri ◽  
Sparse Modeling

scholarly journals Bayesian parallel imaging with edge-preserving priors

2006 ◽  
Vol 57 (1) ◽  
pp. 8-21 ◽  
Author(s):  
Ashish Raj ◽  
Gurmeet Singh ◽  
Ramin Zabih ◽  
Bryan Kressler ◽  
Yi Wang ◽  
...  
Keyword(s):  
Parallel Imaging ◽  
Edge Preserving

scholarly journals Whole-Brain High-Resolution Metabolite Mapping with 3D Compressed-Sensing-SENSE-LowRank 1H FID-MRSI

2020 ◽  
Author(s):  
Antoine Klauser ◽  
Paul Klauser ◽  
Frédéric Grouiller ◽  
Sebastien Courvoisier ◽  
Francois Lazeyras
Keyword(s):  
High Resolution ◽  
Brain Regions ◽  
Low Rank ◽  
Acquisition Time ◽  
Resonance Spectroscopy ◽  
Brain Anatomy ◽  
Reconstruction Technique ◽  
Whole Brain ◽  
Isotropic Resolution

There is a growing interest of the neuroscience community to map the distribution of brain metabolites in vivo. Magnetic resonance spectroscopy imaging (MRSI) is often limited by either a poor spatial resolution and/or a long acquisition time which severely limits its applications for clinical or research purposes. We developed a novel acquisition-reconstruction technique combining fast 1H-FID-MRSI sequence accelerated by random k-space undersampling and a low-rank and total-generalized variation (TGV) constrained model. This framework was applied to the brain of four healthy volunteers. Following 20 min acquisition, reconstruction and quantification, the resulting metabolic maps with a 5 mm isotropic resolution reflected the detailed neurochemical composition of all brain regions and revealed part of the underlying brain anatomy. Contrasts and features from the 3D metabolite distributions were in agreement with the literature and consistent across the four subjects. The successful combination of the 3D 1H-FID-MRSI with a constrained reconstruction enables the detailed mapping of metabolite concentrations at high-resolution in the whole brain and with an acquisition time that is compatible with clinical or research settings.

scholarly journals Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T

2021 ◽  
Author(s):  
Xi Chen ◽  
Wenchuan Wu ◽  
Mark Chiew
Keyword(s):  
Temporal Stability ◽  
Three Dimensional ◽  
Low Rank ◽  
Reconstruction Method ◽  
Isotropic Resolution ◽  
Resonance Effects ◽  
Temporal Variance ◽  
Phase Variations ◽  
Recovery Simulation

Three-dimensional (3D) encoding methods are increasingly being explored as alternatives to multi-slice two-dimensional (2D) acquisitions in fMRI, particularly in cases where high isotropic resolution is needed. 3D multi-shot EPI is the most popular 3D fMRI acquisition method, but is susceptible to physiological fluctuations which can induce inter-shot phase variations, and thus reducing the achievable tSNR, negating some of the benefit of 3D encoding. This issue can be particularly problematic at ultra-high fields like 7T, which have more severe off-resonance effects. In this work, we aim to improve the temporal stability of 3D multi-shot EPI at 7T by improving its robustness to inter-shot phase variations. We presented a 3D segmented CAIPI sampling trajectory ("seg-CAIPI") and an improved reconstruction method based on Hankel structured low-rank matrix recovery. Simulation and in-vivo results demonstrate that the combination of the seg-CAIPI sampling scheme and the proposed structured low-rank reconstruction is a promising way to effectively reduce the unwanted temporal variance induced by inter-shot physiological fluctuations, and thus improve the robustness of 3D multi-shot EPI for fMRI.

scholarly journals P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data

2015 ◽  
Vol 75 (4) ◽  
pp. 1499-1514 ◽  
Author(s):  
Justin P. Haldar ◽  
Jingwei Zhuo
Keyword(s):  
Parallel Imaging ◽  
Low Rank ◽  
Imaging Data

Accelerating T 2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling

2020 ◽  
Vol 85 (3) ◽  
pp. 1455-1467
Author(s):  
Ziyu Meng ◽  
Rong Guo ◽  
Yudu Li ◽  
Yue Guan ◽  
Tianyao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Rank ◽  
Sparse Modeling ◽  
The Brain
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