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Published: August 14, 2014

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Blog Post number 2

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Published: August 14, 2013

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Blog Post number 1

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Published: August 14, 2012

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Biomechanical root cause analysis of complications in head immobilization devices for pediatric neurosurgery

Published in International Manufacturing Science and Engineering Conference, 2018

Precise and firm fixation of the cranium is critical during craniotomy and delicate brain neurosurgery making head immobilization devices (HIDs) a staple instrument in brain neurosurgical operations today. However, despite their popularity, there is no standard procedure for their use and many complications arise from using HIDs in pediatric neurosurgery. In this paper, we identify biomechanical causes of complications and quantify risks in pin-type HIDs including clamping force selection, positioning and age effects. Based on our root cause analysis, we develop a framework to address the biomechanical factors that influence complications and understand the biomechanics of the clamping process. We develop an age-dependent finite element model (FEM) of a single pin on a cranial bone disc with the representative properties and skull thickness depending on age. This model can be utilized to reduce risk of complications by design as well as to provide recommendations for current practices.

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Biomechanics of diastolic dysfunction: a one-dimensional computational modeling approach

Published in The American Journal of Physiology-Heart and Circulatory Physiology, 2021

Diastolic dysfunction (DD) is a major component of heart failure with preserved ejection fraction (HFpEF). Accordingly, a profound understanding of the underlying biomechanical mechanisms involved in DD is needed to elucidate all aspects of HFpEF. In this study, we have developed a computational model of DD by leveraging the power of an advanced one-dimensional arterial network coupled to a four-chambered zero-dimensional cardiac model. The two main pathologies investigated were linked to the active relaxation of the myocardium and the passive stiffness of the left ventricular wall. These pathologies were quantified through two parameters for the biphasic delay of active relaxation, which simulate the early and late-phase relaxation delay, and one parameter for passive stiffness, which simulates the increased nonlinear stiffness of the ventricular wall. A parameter sensitivity analysis was conducted on each of the three parameters to investigate their effect in isolation. The three parameters were then concurrently adjusted to produce the three main phenotypes of DD. It was found that the impaired relaxation phenotype can be replicated by mainly manipulating the active relaxation, the pseudo-normal phenotype was replicated by manipulating both the active relaxation and passive stiffness, and, finally, the restricted phenotype was replicated by mainly changing the passive stiffness. This article presents a simple model producing a holistic and comprehensive replication of the main DD phenotypes and presents novel biomechanical insights on how key parameters defining the relaxation and stiffness properties of the myocardium affect the development and manifestation of DD.

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A platform for high-fidelity patient-specific structural modelling of atherosclerotic arteries: from intravascular imaging to three-dimensional stress distributions

Published in Journal of the Royal Society: Interface, 2022

The pathophysiology of atherosclerotic lesions, including plaque rupture triggered by mechanical failure of the vessel wall, depends directly on the plaque morphology-modulated mechanical response. The complex interplay between lesion morphology and structural behaviour can be studied with high-fidelity computational modelling. However, construction of three-dimensional (3D) and heterogeneous models is challenging, with most previous work focusing on two-dimensional geometries or on single-material lesion compositions. Addressing these limitations, we here present a semi-automatic computational platform, leveraging clinical optical coherence tomography images to effectively reconstruct a 3D patient-specific multi-material model of atherosclerotic plaques, for which the mechanical response is obtained by structural finite-element simulations. To demonstrate the importance of including multi-material plaque components when recovering the mechanical response, a computational case study was conducted in which systematic variation of the intraplaque lipid and calcium was performed. The study demonstrated that the inclusion of various tissue components greatly affected the lesion mechanical response, illustrating the importance of multi-material formulations. This platform accordingly provides a viable foundation for studying how plaque micro-morphology affects plaque mechanical response, allowing for patient-specific assessments and extension into clinically relevant patient cohorts.

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Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography

Published in Computers in Biology and Medicine, 2023

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of n=40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches

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Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization

Published in Arxiv, 2023

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of n=40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

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Karim Kadry

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