MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations

Xanthis, Christos G.; Venetis, Ioannis E.; Chalkias, Athanasios; Aletras, Anthony H.

doi:10.1109/tmi.2013.2292119

Cited by 51 publications

(62 citation statements)

References 20 publications

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“…The main bottleneck in the reconstructions is formed by the partial derivative computations needed to solve Equation . Further research is aimed at performing these computations on GPU architectures, reducing the computational effort through algorithmic improvements and through the use of surrogate models . Together with (cloud) computing resources becoming cheaper and more accessible over time, we believe it is possible to accelerate the computations to such an extent that MR‐STAT becomes applicable in clinical settings.…”

Section: Resultsmentioning

confidence: 99%

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

et al. 2020

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MR‐STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time‐domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large‐scale nonlinear optimisation problem. We propose a matrix‐free and parallelized inexact Gauss–Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high‐performance computing cluster and is demonstrated to be able to generate high‐resolution (1 mm × 1 mm in‐plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconstructed T1 and T2 values for the gel phantoms are in agreement with results from gold standard measurements and, for the in vivo experiments, the quantitative values show good agreement with literature values. In all experiments, short pulse sequences with robust Cartesian sampling are used, for which MR fingerprinting reconstructions are shown to fail.

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Section: Resultsmentioning

confidence: 99%

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

et al. 2020

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“…Future simulation studies using advanced MRI pulse‐sequence simulations 38 may provide improvements in this regard.…”

Section: Discussionmentioning

confidence: 99%

Validation of a new t2* algorithm and its uncertainty value for cardiac and liver iron load determination from MRI magnitude images

Bidhult

Xanthis

Liljekvist

et al. 2015

Magnetic Resonance in Med

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PurposeTo validate an automatic algorithm for offline T2* measurements, providing robust, vendor‐independent T2*, and uncertainty estimates for iron load quantification in the heart and liver using clinically available imaging sequences.MethodsA T2* region of interest (ROI)‐based algorithm was developed for robustness in an offline setting. Phantom imaging was performed on a 1.5 Tesla system, with clinically available multiecho gradient‐recalled‐echo (GRE) sequences for cardiac and liver imaging. A T2* single‐echo GRE sequence was used as reference. Simulations were performed to assess accuracy and precision from 2000 measurements. Inter‐ and intraobserver variability was obtained in a patient study (n = 23).ResultsSimulations: Accuracy, in terms of the mean differences between the proposed method and true T2* ranged from 0–0.73 ms. Precision, in terms of confidence intervals of repeated measurements, was 0.06–4.74 ms showing agreement between the proposed uncertainty estimate and simulations. Phantom study: Bias and variability were 0.26 ± 4.23 ms (cardiac sequence) and −0.23 ± 1.69 ms (liver sequence). Patient study: Intraobserver variability was similar for experienced and inexperienced observers (0.03 ± 1.44 ms versus 0.16 ± 2.33 ms). Interobserver variability was 1.0 ± 3.77 ms for the heart and −0.52 ± 2.75 ms for the liver.ConclusionThe proposed algorithm was shown to provide robust T2* measurements and uncertainty estimates over the range of clinically relevant T2* values. Magn Reson Med, 2015. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Magn Reson Med 75:1717–1729, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

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“…optimizing MR sequences, artifact detection, testing image reconstruction techniques, design of specialized RF pulses and educational purposes. Also, multiple utilities of numerical simulations have been combined to produce a few general purpose MRI simulators e.g., [5,6,7,8,9]. Accurate simulation of this initial-value problem is still challenging for the following reasons: very tiny time steps, sufficiently fine spatial resolution, and non-smooth data (e.g.…”

Section: Bloch Model For Magnetic Resonance Imagingmentioning

confidence: 99%

Numerical simulation of Bloch equations for dynamic magnetic resonance imaging

Hazra

Lube

Raumer

2018

Applied Numerical Mathematics

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Magnetic Resonance Imaging (MRI) is a widely applied non-invasive imaging modality based on non-ionizing radiation which gives excellent images and soft tissue contrast of living tissues. We consider the modified Bloch problem as a model of MRI for flowing spins in an incompressible flow field. After establishing the well-posedness of the corresponding evolution problem, we analyze its spatial semi-discretization using discontinuous Galerkin methods. The high frequency time evolution requires a proper explicit and adaptive temporal discretization. The applicability of the approach is shown for basic examples.

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MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations

Cited by 51 publications

References 20 publications

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

Validation of a new t2* algorithm and its uncertainty value for cardiac and liver iron load determination from MRI magnitude images

Numerical simulation of Bloch equations for dynamic magnetic resonance imaging

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