Realistic numerical simulations of diffusion tensor cardiovascular magnetic resonance: The effects of perfusion and membrane permeability

Alemany, Ignasi; Rose, Jan N; Ferreira, Pedro F.; Pennell, Dudley J.; Nielles‐Vallespin, Sonia; Scott, Andrew D.; Doorly, D. J.

doi:10.1002/mrm.29737

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…Furthermore, the diffusion encoding is assumed to only capture the Gaussian component, which holds true only for certain b-value ranges. 59,60 If detailed microstructure is of interest, Monte-Carlo simulations in realistic microstructures [61][62][63] are a suitable…”

Section: Discussionmentioning

confidence: 99%

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Weine,

McGrath,

Dirix

et al. 2024

Magnetic Resonance in Med

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PurposeTo present an open‐source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction.MethodsCMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase‐contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided.ResultsThe Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk‐motion phase sensitivity took approximately 10 s per diffusion‐weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain.ConclusionCMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open‐source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.

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Section: Discussionmentioning

confidence: 99%

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Weine,

McGrath,

Dirix

et al. 2024

Magnetic Resonance in Med

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Phase stabilization with motion compensated diffusion weighted imaging

Hannum,

Cork,

Setsompop

et al. 2024

Magnetic Resonance in Med

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PurposeDiffusion encoding gradient waveforms can impart intra‐voxel and inter‐voxel dephasing owing to bulk motion, limiting achievable signal‐to‐noise and complicating multishot acquisitions. In this study, we characterize improvements in phase consistency via gradient moment nulling of diffusion encoding waveforms.MethodsHealthy volunteers received neuro () and cardiac () MRI. Three gradient moment nulling levels were evaluated: compensation for position (), position + velocity (), and position + velocity + acceleration (). Three experiments were completed: (Exp‐1) Fixed Trigger Delay Neuro DWI; (Exp‐2) Mixed Trigger Delay Neuro DWI; and (Exp‐3) Fixed Trigger Delay Cardiac DWI. Significant differences () of the temporal phase SD between repeated acquisitions and the spatial phase gradient across a given image were assessed.Results moment nulling was a reference for all measures. In Exp‐1, temporal phase SD for diffusion encoding was significantly reduced with (35% of t‐tests) and (68% of t‐tests). The spatial phase gradient was reduced in 23% of t‐tests for and 2% of cases for . In Exp‐2, temporal phase SD significantly decreased with gradient moment nulling only for (83% of t‐tests), but spatial phase gradient significantly decreased with only (50% of t‐tests). In Exp‐3, gradient moment nulling significantly reduced temporal phase SD and spatial phase gradients (100% of t‐tests), resulting in less signal attenuation and more accurate ADCs.ConclusionWe characterized gradient moment nulling phase consistency for DWI. Using M1 for neuroimaging and M1 + M2 for cardiac imaging minimized temporal phase SDs and spatial phase gradients.

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The Effect of Temporal Variations in Myocardial Perfusion on Diffusion Tensor Measurements

Alemany

Ferreira

Nielles‐Vallespin

et al. 2023

Lecture Notes in Computer Science

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Realistic numerical simulations of diffusion tensor cardiovascular magnetic resonance: The effects of perfusion and membrane permeability

Cited by 5 publications

References 54 publications

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Phase stabilization with motion compensated diffusion weighted imaging

The Effect of Temporal Variations in Myocardial Perfusion on Diffusion Tensor Measurements

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