Flow MRI simulation in complex 3D geometries: Application to the cerebral venous network

Fortin, Alexandre; Salmon, Stéphanie; Baruthio, J.; Delbany, Maya; Durand, Emmanuel

doi:10.1002/mrm.27114

Cited by 22 publications

(34 citation statements)

References 26 publications

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“…Several specialized phantoms or simulators that complement the Bloch-Torrey equations, enabling simulation or calibration of advanced MR imaging techniques, have been proposed in the literature. For instance, Klepaczko et al 192 and Fortin et al 193 developed MRI simulators for magnetic resonance angiography (MRA) of the cerebral circulation. TOF MRA data simulated by Klepaczko et al 194 were used to generate ground-truth data for evaluating vascular segmentation algorithms.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

“…TOF MRA data simulated by Klepaczko et al 194 were used to generate ground-truth data for evaluating vascular segmentation algorithms. Fortin et al 193 extend the JEMRIS simulator to be able to produce flow-related MRA images for the main three techniques, viz., TOF MRA, phase contrast MRA, and contrast enhanced MRA. Pannetier et al 195 developed a simulator to predict dynamic contrast enhancement in MRI with bolus tracking.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

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Virtual clinical trials in medical imaging: a review

et al. 2020

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The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

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Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

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“…Last but not least, there is no simulation standard today within the MR field. A variety of MR simulators have been published in the literature to date, but they have mainly been developed and utilized for specific MR applications and they have not been validated extensively over a wider range of MR applications [17, 18].…”

Section: Introductionmentioning

confidence: 99%

coreMRI: A high-performance, publicly available MR simulation platform on the cloud

Xanthis

2019

PLoS ONE

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Introduction A C loud- OR iented E ngine for advanced MRI simulations (coreMRI) is presented in this study. The aim was to develop the first advanced MR simulation platform delivered as a web service through an on-demand, scalable cloud-based and GPU-based infrastructure. We hypothesized that such an online MR simulation platform could be utilized as a virtual MRI scanner but also as a cloud-based, high-performance engine for advanced MR simulations in simulation-based quantitative MR (qMR) methods. Methods and results The simulation framework of coreMRI was based on the solution of the Bloch equations and utilized a ground-up-approach design based on the principles already published in the literature. The development of a front-end environment allowed the connection of the end-users to the GPU-equipped instances on the cloud. The coreMRI simulation platform was based on a modular design where individual modules (such as the Gadgetron reconstruction framework and a newly developed Pulse Sequence Designer) could be inserted in the main simulation framework. Different types and sources of pulse sequences and anatomical models were utilized in this study revealing the flexibility that the coreMRI simulation platform offers to the users. The performance and scalability of coreMRI were also examined on multi-GPU configurations on the cloud, showing that a multi-GPU computer on the cloud equipped with a newer generation of GPU cards could significantly mitigate the prolonged execution times that accompany more realistic MRI and qMR simulations. Conclusions coreMRI is available to the entire MR community, whereas its high performance and scalability allow its users to configure advanced MRI experiments without the constraints imposed by experimentation in a true MRI scanner (such as time constraint and limited availability of MR scanners), without upfront investment for purchasing advanced computer systems and without any user expertise on computer programming or MR physics. coreMRI is available to the users through the webpage https://www.coreMRI.org .

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“…For technical reasons, the AIF cannot be quantified directly but is usually estimated in the left ventricle (LV) . Computational fluid dynamics (CFD) simulations—as they have been widely used for general interpretation of perfusion MRI data—have shown that the CA bolus experiences dispersion during its passage through the epicardial vasculature toward the myocardial region of interest . This widening is erroneously attributed to the microcirculation, and therefore leads to a systematic underestimation of the MBF and an overestimation of the MPR values.…”

Section: Introductionmentioning

confidence: 99%

Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

Martens

Panzer

Wijngaard

et al. 2019

Magnetic Resonance in Med

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Purpose Bolus‐based dynamic contrast agent (CA) perfusion measurements of the heart are subject to systematic errors due to CA bolus dispersion in the coronary arteries. To better understand these effects on quantification of myocardial blood flow and myocardial perfusion reserve (MPR), an in‐silico model of the coronary arteries down to the pre‐arteriolar vessels has been developed. Methods In this work, a computational fluid dynamics analysis is performed to investigate these errors on the basis of realistic 3D models of the left and right porcine coronary artery trees, including vessels at the pre‐arteriolar level. Using advanced boundary conditions, simulations of blood flow and CA transport are conducted at rest and under stress. These are evaluated with regard to dispersion (assessed by the width of CA concentration time curves and associated vascular transport functions) and errors of myocardial blood flow and myocardial perfusion reserve quantification. Results Contrast agent dispersion increases with traveled distance as well as vessel diameter, and decreases with higher flow velocities. Overall, the average myocardial blood flow errors are −28% ± 16% and −8.5% ± 3.3% at rest and stress, respectively, and the average myocardial perfusion reserve error is 26% ± 22%. The calculated values are different in the left and right coronary tree. Conclusion Contrast agent dispersion is dependent on a complex interplay of several different factors characterizing the cardiovascular bed, including vessel size and integrated vascular length. Quantification errors evoked by the observed CA dispersion show nonnegligible distortion in dynamic CA bolus‐based perfusion measurements. We expect future improvements of quantitative perfusion measurements to make the systematic errors described here more apparent.

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Flow MRI simulation in complex 3D geometries: Application to the cerebral venous network

Abstract: The framework provides a versatile and reusable tool for the simulation of any MRI experiment including physiological fluids and arbitrarily complex flow motion.

Cited by 22 publications

References 26 publications

Virtual clinical trials in medical imaging: a review

Virtual clinical trials in medical imaging: a review

coreMRI: A high-performance, publicly available MR simulation platform on the cloud

Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

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