A numerical study of a heart phantom model

Zhu, Ye; Luo, Xiaoyu; Gao, Hao; McComb, Christie; Berry, Colin

doi:10.1080/00207160.2013.854337

Cited by 9 publications

(11 citation statements)

References 37 publications

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“…These results were determined mathematically, by utilizing end-systolic wall stresses and wall thickness. However, Zhu et al (2014) commented on the lack of sufficient experimental data for myocardial tissue. As mentioned previously, the paucity of data creates issues regarding the reproducibly of results.…”

Section: Introductionmentioning

confidence: 99%

“…This holds true when developing a dynamic phantom that requires accurate viscoelastic properties to properly replicate bodily motions. As Zhu et al (2014) noted, the lack of experimental myocardial tissue data has presented challenges when developing dynamic heart phantoms with accurate pulsatile and contractile heart flow. In addition, the process of bio-mechanical testing standardization extends to tissue scaffold polymers, heart patches, and polymer glues, as these are commonly designed to match the properties of biological soft tissues (Kofidis et al 2002, Chen et al 2008, Lang et al 2014.…”

Section: Introductionmentioning

confidence: 99%

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Standardized static and dynamic evaluation of myocardial tissue properties

2017

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Standardized static and dynamic evaluation of myocardial tissue properties

2017

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“…It is also an essential step for the success of the clinical translation. Growing efforts are being made through comparisons to experimental benchmark data 41 , to clinical images 19 , 42 and to different computational models 21 , 22 . Directly measuring and can be extremely difficult in vivo , even in vitro .…”

Section: Discussionmentioning

confidence: 99%

Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Gao

Mangion

Carrick

et al. 2017

Sci Rep

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Biomechanical computational models have potential prognostic utility in patients after an acute ST-segment–elevation myocardial infarction (STEMI). In a proof-of-concept study, we defined two groups (1) an acute STEMI group (n = 6, 83% male, age 54 ± 12 years) complicated by left ventricular (LV) systolic dysfunction; (2) an age- and sex- matched hyper-control group (n = 6, 83% male, age 46 ± 14 years), no prior history of cardiovascular disease and normal systolic blood pressure (SBP < 130 mmHg). Cardiac MRI was performed in the patients (2 days & 6 months post-STEMI) and the volunteers, and biomechanical heart models were synthesized for each subject. The candidate parameters included normalized active tension (AT norm) and active tension at the resting sarcomere length (T req, reflecting required contractility). Myocardial contractility was inversely determined from personalized heart models by matching CMR-imaged LV dynamics. Compared with controls, patients with recent STEMI exhibited increased LV wall active tension when normalized by SBP. We observed a linear relationship between T req 2 days post-MI and global longitudinal strain 6 months later (r = 0.86; p = 0.03). T req may be associated with changes in LV function in the longer term in STEMI patients complicated by LV dysfunction. Further studies seem warranted.

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“…where F is the deformation gradient, C is the right CauchyGreen deformation tensor and W stands for the strain energy density function (SEDF). We adopt a neo-Hookean constitutive model with the shear modulus of 36.75 kPa from experiments [29], which guarantees the material incompressibility. The above equations can be solved using the finite element method (FEM) with appropriate boundary conditions.…”

Section: Biomechanical Simulationsmentioning

confidence: 99%

Biomechanics-Informed Neural Networks for Myocardial Motion Tracking in MRI

Chen

Wang

Chen

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations and to implicitly capture their underlying properties via reconstructing biomechanical simulations. The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data from two different datasets. The results show that it can achieve better performance against other competing methods in terms of motion tracking accuracy and has the ability to learn biomechanical properties such as incompressibility and strains. The method has also been shown to have better generalisability to unseen domains compared with commonly used L2 regularisation schemes.

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A numerical study of a heart phantom model

Cited by 9 publications

References 37 publications

Standardized static and dynamic evaluation of myocardial tissue properties

Standardized static and dynamic evaluation of myocardial tissue properties

Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models

Biomechanics-Informed Neural Networks for Myocardial Motion Tracking in MRI

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