Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Abstract

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid–structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

Features of immediate adaptation of the circulatory system to static load in persons with different body mass index

“…The initial decrease in HR, SV, and MBV values immediately after SL, present in both groups, is a strain effect. It is caused by maintaining a powerful tonic effort, increasing intrathoracic, intrapulmonary pressure, and as a result, increasing compression on the heart [5,6]. Under conditions of compression of blood vessels inside skeletal muscles during static effort, the volume of venous return of blood to the heart decreases [4,20,21].…”

“…However, sports physiologists pay much more attention to physical exercises of a dynamic nature [3,14]. In the vast majority of sports, muscle activity is dynamic in nature, but recently there has been a growing interest in health physical culture in static exercises [5,15], to the study of which a significant contribution was made by the domestic scientist O. R.…”

Fluid-Structure Interactions of Peripheral Arteries Using a Coupledin silicoandin vitroApproach

“…These local differences may be non-negligible in studies where local arterial wall distensions and stress concentrations are studied such as aneurysm models [110]. For the numerical modelling of the arterial wall, the importance of applying an appropriate pre-stress to the structural model is widely established in the literature [111].…”

Is the time right for a new initiative in mathematical modeling of the lower urinary tract? ICI‐RS 2023

“…In the last decade, the focus of cardiovascular computational modeling has shifted toward patient-specific analyses, the so-called "inverse modeling" approach that combines computational fluid mechanical modeling with medical imaging technology. 31 Rapid technological advances in echocardiography, magnetic resonance imaging (MRI), and machine learning have led to high resolution measurements of tissue motion and blood flow (e.g., 4D MRI). 31 The pairing of this kinematic information with tissue mechanics, fluid dynamics, and fluid structure methods is shedding light on realistic material properties and dynamic data (e.g., shear stresses and forces), which are not directly measurable in living individuals.…”

“…31 Rapid technological advances in echocardiography, magnetic resonance imaging (MRI), and machine learning have led to high resolution measurements of tissue motion and blood flow (e.g., 4D MRI). 31 The pairing of this kinematic information with tissue mechanics, fluid dynamics, and fluid structure methods is shedding light on realistic material properties and dynamic data (e.g., shear stresses and forces), which are not directly measurable in living individuals. 31 These approaches are unveiling several strong correlations between retrieved data and corresponding clinical risk factors.…”