A Comprehensive Cardiac Motion Estimation Framework Using Both Untagged and 3-D Tagged MR Images Based on Nonrigid Registration

Shi, Wenzhe; Zhuang, Xiahai; Wang, Haiyan; Duckett, Simon; Luong, Duy V. N.; Tobon-Gómez, Catalina; Tung, Kai-Pin; Edwards, Philip; Rhode, Kawal; Razavi, Reza; Ourselin, Sébastien; Rueckert, Daniel

doi:10.1109/tmi.2012.2188104

Cited by 79 publications

(30 citation statements)

References 38 publications

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“…The proposed energy CF relies on strain fields derived directly from the images, and thus on the availability of high quality data and robust image registration tools (Shi et al. 2012). A similar approach can be followed by exploiting the fact that the cavity pressure that inflates the myocardium to a given volume scales linearly with the parameter .…”

Section: Sensitivity Analysismentioning

confidence: 99%

Improved identifiability of myocardial material parameters by an energy-based cost function

Nasopoulou

Shetty

Lee

et al. 2017

Biomech Model Mechanobiol

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Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were and (, , ) for the HF cases and and (, , ) for the healthy case.

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Section: Sensitivity Analysismentioning

confidence: 99%

Improved identifiability of myocardial material parameters by an energy-based cost function

Nasopoulou

Shetty

Lee

et al. 2017

Biomech Model Mechanobiol

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“…However, for real in vivo cardiac data, that requirement can hardly be satisfied. To address this “validation without ground truths” problem, several categories of effective solutions have been investigated in past decades, including (1) manufacturing simulated data with the known ground truths [29–31], (2) artificially marking out ground truths [32], (3) using gold-standard methods to generate ground truths [29,31], and (4) other particular approaches [33]. Nevertheless, these solutions all have limitations.…”

Section: Methodsmentioning

confidence: 99%

A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters

Liu

Wang

et al. 2014

Magnetic Resonance Imaging

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A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod.

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“…In brief, the cine MRI sequence is first processed using an motion tracking algorithm (Shi et al 2012) to extract the myocardial displacements, based on which a sequence of cubic-Hermite meshes are then constructed and aligned to the motion observed in each frame of MRI sequence (Lamata et al 2011). These meshes are compared to the simulation results generated using our finite deformation-based mechanical model, with the previously defined pressure surrogate (illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Understanding the need of ventricular pressure for the estimation of diastolic biomarkers

Shi

Rueckert

et al. 2013

Biomech Model Mechanobiol

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The diastolic function (i.e., blood filling) of the left ventricle (LV) is determined by its capacity for relaxation, or the decay in residual active tension (AT) generated during systole, and its constitutive material properties, or myocardial stiffness. The clinical determination of these two factors (diastolic residual AT and stiffness) is thus essential for assessing LV diastolic function. To quantify these two factors, in our previous work, a novel model-based parameter estimation approach was proposed and successfully applied to multiple cases using clinically acquired motion and invasively measured ventricular pressure data. However, the need to invasively acquire LV pressure limits the wide application of this approach. In this study, we address this issue by analyzing the feasibility of using two kinds of non-invasively available pressure measurements for the purpose of inverse mechanical parameter estimation. The prescription of pressure based on a generic pressure–volume (P–V) relationship reported in literature is first evaluated in a set of 18 clinical cases (10 healthy and 8 diseased), finding reasonable results for stiffness but not for residual active tension. We then investigate the use of non-invasive pressure measures, now available through imaging techniques and limited by unknown or biased offset values. Specifically, three sets of physiologically realistic synthetic data with three levels of diastolic residual active tension (i.e., impaired relaxation capability) are designed to quantify the percentage error in the parameter estimation against the possible pressure offsets within the physiological limits. Maximum errors are quantified as 11 % for the magnitude of stiffness and 22 % for AT, with averaged 0.17 kPa error in pressure measurement offset using the state-of-the-art non-invasive pressure estimation method. The main cause for these errors is the limited temporal resolution of clinical imaging data currently available. These results demonstrate the potential feasibility of the estimation diastolic biomarkers with non-invasive assessment of pressure through medical imaging data.

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A Comprehensive Cardiac Motion Estimation Framework Using Both Untagged and 3-D Tagged MR Images Based on Nonrigid Registration

Cited by 79 publications

References 38 publications

Improved identifiability of myocardial material parameters by an energy-based cost function

Improved identifiability of myocardial material parameters by an energy-based cost function

A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters

Understanding the need of ventricular pressure for the estimation of diastolic biomarkers

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