A spatially-distributed computational model to quantify behaviour of contrast agents in MR perfusion imaging

Cookson, Andrew; Lee, Jack; Michler, Christian; Chabiniok, Radomí­r; Hyde, Eoin; Nordsletten, David; Smith, Nicolas P.

doi:10.1016/j.media.2014.07.002

Cited by 29 publications

(80 citation statements)

References 47 publications

(58 reference statements)

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“…More complex models are providing insight into integrated coronary function such as the underlying mechanisms of cross talk in health and disease as well as its effects on wave propagation . In addition, a recent model has incorporated the physics governing the transport of a CA in a porous medium, which has identified key ranges of parameters for optimal image enhancement in perfusion MRI. The challenge of integrating this model with a contractile hyperporoelastic model of the heart will allow for in silico optimization of CA protocols under more physiological settings.…”

Section: Resultsmentioning

confidence: 99%

“…This poses the poroelastic model as a valuable approach to analyze cross‐talk phenomena, where currently the effects of whole‐heart contraction on the smaller scales of the vascular network (e.g., capillaries) would otherwise be computationally infeasible. In addition, recent developments in simulation of CA transport have been built on top of the porous modeling framework . This approach has been used to identify optimal ranges of several CA and imaging parameters with the capability of representing the signal produced by both blood‐bound and freely diffusive CAs as shown in Figure .…”

Section: Modeling Approaches In the Coronary Circulationmentioning

confidence: 99%

See 1 more Smart Citation

Measurement and modeling of coronary blood flow

Sinclair

Lee

Cookson

et al. 2015

WIREs Mechanisms of Disease

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Ischemic heart disease that comprises both coronary artery disease and microvascular disease is the single greatest cause of death globally. In this context, enhancing our understanding of the interaction of coronary structure and function is not only fundamental for advancing basic physiology but also crucial for identifying new targets for treating these diseases. A central challenge for understanding coronary blood flow is that coronary structure and function exhibit different behaviors across a range of spatial and temporal scales. While experimental studies have sought to understand this feature by isolating specific mechanisms, in tandem, computational modeling is increasingly also providing a unique framework to integrate mechanistic behaviors across different scales. In addition, clinical methods for assessing coronary disease severity are continuously being informed and updated by findings in basic physiology. Coupling these technologies, computational modeling of the coronary circulation is emerging as a bridge between the experimental and clinical domains, providing a framework to integrate imaging and measurements from multiple sources with mathematical descriptions of governing physical laws. State-of-the-art computational modeling is being used to combine mechanistic models with data to provide new insight into coronary physiology, optimization of medical technologies, and new applications to guide clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Modeling Approaches In the Coronary Circulationmentioning

confidence: 99%

Measurement and modeling of coronary blood flow

Sinclair

Lee

Cookson

et al. 2015

WIREs Mechanisms of Disease

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“…As the 2CX model does not assume any spatial distribution of CA concentration within each compartment, it is often referred to as a “lumped element” or “stirred tank” compartment model, while if given in an analytic Laplace domain representation, it is referred to as a two‐region, one‐barrier, distributed parameter model . Further extensions of compartment models assume explicit spatial distributions of CA within compartments including its passive transport (diffusion) …”

Section: Theorymentioning

confidence: 99%

Extended quantitative dynamic contrast‐enhanced cardiac perfusion imaging in mice using accelerated data acquisition and spatially distributed, two‐compartment exchange modeling

Kwiatkowski

Kozerke

2019

NMR in Biomedicine

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The objective of the present work was to improve data acquisition and quantification of dynamic contrast‐enhanced perfusion imaging in the in vivo murine heart. Four‐fold undersampled data were acquired in 14 mice and reconstructed using k‐t SPARSE. A two‐compartment exchange model was employed to provide additional characterization of myocardial tissue based on compartment volumes and the permeability surface area product. The feasibility of the proposed method was tested using compartment‐based analysis of contrast‐enhanced perfusion data acquired with intravascular and extracellular contrast agents. A significantly different permeability surface area product was measured for the intravascular versus extracellular contrast agent (0.13–0.15 ml/g/min vs 0.86–0.88 ml/g/min). The reduced extravasation also resulted in significantly smaller interstitial volumes of the intravascular versus extracellular agent (9.8–11% vs 45–47%). No difference was found for myocardial blood flow (6.5–7.2 ml/g/min vs 6.0–7.0 ml/g/min). The results presented here show that two‐compartment exchange modeling in the in vivo murine heart is feasible and gives access to tissue parameters beyond myocardial blood flow.

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“…Computational fluid dynamics (CFD) is a valuable tool for simulating fluid behavior. When applied to the myocardium, it can be used to predict blood supply to pathologic tissue . Early mathematical models trace back to a “well‐stirred” cylinder representing a single capillary tube.…”

Section: Introductionmentioning

confidence: 99%

“…A full simulation of a patient‐specific model may take several days or even weeks on a regular computer, and therefore is not suitable for a clinical or even medical research setting. An alternative approach is the Darcy's law for flow in porous media, allowing the biophysical modeling of contrast medium (CM) flow within the capillary bed of the myocardium with an efficient computational framework . There, the specific geometry of the capillary network is replaced by a permeability value, which gives only macroscopic information.…”

Section: Introductionmentioning

confidence: 99%

Porous medium 3D flow simulation of contrast media washout in cardiac MRI reflects myocardial injury

Riazy

Schaeffter

Olbrich

et al. 2019

Magnetic Resonance in Med

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Purpose Myocardial blood–flow simulation based on laws of fluid mechanics is a valuable tool for understanding tissue behavior. Our aim is to evaluate the ability of a porous‐media flow model approach to reflect disturbed washout of contrast media (CM) from the myocardium as observed by cardiovascular MR. Methods A coupled advection‐diffusion model is used to describe the CM flow in the vascular and extravascular space as separate compartments. Their exchange of CM is controlled by the exchange rate ExR, which in turn determines the washout behavior. We fitted simulations to CM concentration measurements, derived from T1 maps of the midventricular slice. The CM concentration was extracted from 18 patients with myocarditis in the acute phase and during follow‐up after 6 months. The results were compared with 18 sex‐ and age‐matched controls. For each subject, the measurements were acquired before and during the first 10 minutes at 5 time points after CM administration, representing CM washout. Image registration was applied to compensate for motion between different time points. Results Eight matched data sets had to be excluded due to low registration quality. Processing was successful in n = 10 matched data sets of acute and healed myocarditis as well as controls. Significant differences in ExR were observed when comparing patients with acute myocarditis to controls (P < .001), to their follow‐up (P < .05), and the follow‐up to controls (P < .05). Conclusion Our study suggests the feasibility of using the proposed porous‐medium flow framework for the simulation of pathologic myocardial tissue.

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A spatially-distributed computational model to quantify behaviour of contrast agents in MR perfusion imaging

Abstract: Graphical abstract

Cited by 29 publications

References 47 publications

Measurement and modeling of coronary blood flow

Measurement and modeling of coronary blood flow

Extended quantitative dynamic contrast‐enhanced cardiac perfusion imaging in mice using accelerated data acquisition and spatially distributed, two‐compartment exchange modeling

Porous medium 3D flow simulation of contrast media washout in cardiac MRI reflects myocardial injury

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