Acute Microstructural Changes after ST-Segment Elevation Myocardial                    Infarction Assessed with Diffusion Tensor Imaging

Das, Arka; Kelly, Christopher; Teh, Irvin; Stoeck, Christian T.; Kozerke, Sebastian; Chowdhary, Amrit; Brown, Louise A.; Saunderson, Christopher; Craven, Thomas; Chew, Pei G.; Jex, Nicholas; Swoboda, Peter; Levelt, Eylem; Greenwood, John P; Schneider, Jürgen; Plein, Sven; Dall’Armellina, Erica

doi:10.1148/radiol.2021203208

Cited by 18 publications

(30 citation statements)

References 22 publications

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“…The computed mean ECV in Das et al [ 43 ] was 0.60, 0.29, and 0.29 in the infarct, border, and remote regions, respectively, compared to 0.49, 0.39, and 0.32 in the current study. The discrepancies in the reported ECV values could be due to the different methods used to subdivide the myocardium into remote, border, and infarct regions.…”

Section: Discussioncontrasting

confidence: 57%

“…Overall, the diffusion tensor invariants reported in this study are in good agreement with values reported in previous studies. Das et al [ 43 ] conducted a study on 30 patients with myocardial infarction. The mean MD and FA values computed in vivo by Das et al, compared to the values computed in current study (reported in parenthesis), were: 1.83 × 10 −3 (1.81 × 10 −3 ) mm 2 /s for MD and 0.22 (0.25) for FA in the infarct region; 1.53 × 10 −3 (1.69 × 10 −3 ) mm 2 /s for MD and 0.33 (0.28) for FA in the border zone, and 1.45 × 10 −3 (1.46 × 10 −3 ) mm 2 /s for MD and 0.35 (0.29) for FA in the remote myocardium.…”

Section: Discussionmentioning

confidence: 99%

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Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data

2022

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In vivo cardiac diffusion tensor imaging (cDTI) data were acquired in swine subjects six to ten weeks post-myocardial infarction (MI) to identify microstructural-based biomarkers of MI. Diffusion tensor invariants, diffusion tensor eigenvalues, and radial diffusivity (RD) are evaluated in the infarct, border, and remote myocardium, and compared with extracellular volume fraction (ECV) and native T1 values. Additionally, to aid the interpretation of the experimental results, the diffusion of water molecules was numerically simulated as a function of ECV. Finally, findings based on in vivo measures were confirmed using higher-resolution and higher signal-to-noise data acquired ex vivo in the same subjects. Mean diffusivity, diffusion tensor eigenvalues, and RD increased in the infarct and border regions compared to remote myocardium, while fractional anisotropy decreased. Secondary (e2) and tertiary (e3) eigenvalues increased more significantly than the primary eigenvalue in the infarct and border regions. These findings were confirmed by the diffusion simulations. Although ECV presented the largest increase in infarct and border regions, e2, e3, and RD increased the most among non-contrast-based biomarkers. RD is of special interest as it summarizes the changes occurring in the radial direction and may be more robust than e2 or e3 alone.

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Section: Discussioncontrasting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data

2022

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“… 2020 ; Das et al. 2021 ). This also allows us to focus on the material parameters in a slightly simplified system.…”

Section: Methodsmentioning

confidence: 99%

Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics

Lazarus

Dalton

Husmeier

et al. 2022

Biomech Model Mechanobiol

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Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to improve risk prediction in cardiac patients and assist in the development of new treatments. However, in order to use these models for clinical decision support, it is important that both the impact of model parameter perturbations on the predicted quantities of interest as well as the uncertainty of parameter estimation are properly quantified, where the first task is a priori in nature (meaning independent of any specific clinical data), while the second task is carried out a posteriori (meaning after specific clinical data have been obtained). The present study addresses these challenges for a widely used constitutive law of passive myocardium (the Holzapfel-Ogden model), using global sensitivity analysis (SA) to address the first challenge, and inverse-uncertainty quantification (I-UQ) for the second challenge. The SA is carried out on a range of different input parameters to a left ventricle (LV) model, making use of computationally efficient Gaussian process (GP) surrogate models in place of the numerical forward simulator. The results of the SA are then used to inform a low-order reparametrization of the constitutive law for passive myocardium under consideration. The quality of this parameterization in the context of an inverse problem having observed noisy experimental data is then quantified with an I-UQ study, which again makes use of GP surrogate models. The I-UQ is carried out in a Bayesian manner using Markov Chain Monte Carlo, which allows for full uncertainty quantification of the material parameter estimates. Our study reveals insights into the relation between SA and I-UQ, elucidates the dependence of parameter sensitivity and estimation uncertainty on external factors, like LV cavity pressure, and sheds new light on cardio-mechanic model formulation, with particular focus on the Holzapfel-Ogden myocardial model.

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“…Motion-compensated diffusion encoding overcomes this limitation [160][161][162][163]. Most of the cardiac dMRI studies are based on single diffusion encoding, which has already led to interesting insights in the healthy [28,90,96,161,162] and diseased heart, including myocardial infarction [29], hypertrophic and dilated cardiomyopathy [164], amyloidosis [165] and athlete's heart [166]. Isotropic diffusion encoding can be used to estimate mean diffusivity (MD) in a shorter time compared to conventional single diffusion encoding [71,75].…”

Section: Cardiac Imagingmentioning

confidence: 99%

“…Diffusion MRI has also been invaluable in the study of brain development [23], learning [24,25], and connectivity [26,27]. More recently, diffusion MRI of the heart has regained some significant interest, enabled by advances in MR scanner hardware and experimental design [28][29][30][31]. Diffusion MRI has been also used in the imaging of other Most diffusion MRI studies in the literature are based on conventional Stejskal-Tanner acquisitions [38], which has one pair of pulsed field gradients that encode diffusion along a single axis.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body

et al. 2022

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Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs.

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Acute Microstructural Changes after ST-Segment Elevation Myocardial Infarction Assessed with Diffusion Tensor Imaging

Cited by 18 publications

References 22 publications

Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data

Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data

Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics

Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body

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