AN EFFICIENT APPROACH FOR CONVERTING PATIENT-SPECIFIC SCANS INTO HIGHLY ACCURATE COMPUTATIONAL MODELS(1E1 Computational Biomechanics)

Advances in computational mechanics, constitutive modeling, and techniques for subject-specific modeling have opened the door to patient-specific simulation of the relationships between joint mechanics and osteoarthritis (OA), as well as patient-specific preoperative planning. This article reviews the application of computational biomechanics to the simulation of joint contact mechanics as relevant to the study of OA. This review begins with background regarding OA and the mechanical causes of OA in the context of simulations of joint mechanics. The broad range of technical considerations in creating validated subject-specific whole joint models is discussed. The types of computational models available for the study of joint mechanics are reviewed. The types of constitutive models that are available for articular cartilage are reviewed, with special attention to choosing an appropriate constitutive model for the application at hand. Issues related to model generation are discussed, including acquisition of model geometry from volumetric image data and specific considerations for acquisition of computed tomography and magnetic resonance imaging data. Approaches to model validation are reviewed. The areas of parametric analysis, factorial design, and probabilistic analysis are reviewed in the context of simulations of joint contact mechanics. Following the review of technical considerations, the article details insights that have been obtained from computational models of joint mechanics for normal joints; patient populations; the study of specific aspects of joint mechanics relevant to OA, such as congruency and instability; and preoperative planning. Finally, future directions for research and application are summarized.

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Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽

10.3390/e23070898 ◽

2021 ◽

Vol 23 (7) ◽

pp. 898

Author(s):

Marta Saiz-Vivó ◽

Adrián Colomer ◽

Carles Fonfría ◽

Luis Martí-Bonmatí ◽

Valery Naranjo

Keyword(s):

High Performance ◽

Computational Models ◽

Domain Adaptation ◽

Semantic Segmentation ◽

Patient Specific ◽

Mr Images ◽

Training Samples ◽

Volumetric Images ◽

Acquisition Costs ◽

Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

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Analyzing the tradeoff between electrical complexity and accuracy in patient-specific computational models of deep brain stimulation

Journal of Neural Engineering ◽

10.1088/1741-2560/13/3/036023 ◽

2016 ◽

Vol 13 (3) ◽

pp. 036023 ◽

Cited By ~ 27

Author(s):

Bryan Howell ◽

Cameron C McIntyre

Keyword(s):

Deep Brain Stimulation ◽

Brain Stimulation ◽

Computational Models ◽

Patient Specific ◽

Deep Brain

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Patient-specific computational biomechanics for simulating adolescent scoliosis surgery: Predicted vs clinical correction for a preliminary series of six patients

International Journal for Numerical Methods in Biomedical Engineering ◽

10.1002/cnm.1422 ◽

2010 ◽

Vol 27 (3) ◽

pp. 347-356 ◽

Cited By ~ 13

Author(s):

J. Paige Little ◽

Clayton Adam

Keyword(s):

Patient Specific ◽

Scoliosis Surgery ◽

Computational Biomechanics

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Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

Scientific Reports ◽

10.1038/s41598-020-74583-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Dimitrios S. Pleouras ◽

Antonis I. Sakellarios ◽

Panagiota Tsompou ◽

Vassiliki Kigka ◽

Savvas Kyriakidis ◽

...

Keyword(s):

Disease Progression ◽

Plaque Burden ◽

Patient Specific ◽

High Density Lipoproteins ◽

Computational Biomechanics ◽

Plaque Area ◽

Causes Of Mortality ◽

Plaque Growth ◽

Atherosclerotic Process ◽

The One

Abstract Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors.

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Accuracy of Cartilage Geometry Reconstruction From Volumetric CT Data: A Phantom Study

ASME 2007 Summer Bioengineering Conference ◽

10.1115/sbc2007-174936 ◽

2007 ◽

Author(s):

Andrew E. Anderson ◽

Benjamin J. Ellis ◽

Christopher L. Peters ◽

Jeffrey A. Weiss

Keyword(s):

Articular Cartilage ◽

Contrast Agent ◽

Computational Models ◽

Image Data ◽

Cartilage Thickness ◽

Patient Specific ◽

Joint Spacing ◽

Ct Arthrography ◽

Diarthrodial Joints ◽

Joint Contact

Segmentation of medical image data is often used for the construction of computational models to study the mechanics of diarthrodial joints such as the hip and knee. The analyst must demonstrate that the reconstructed geometry is an accurate representation of the true continuum to ensure model validity. This becomes especially important for computational modeling of joint contact, which requires accurate reconstruction of articular cartilage. Although volumetric computed tomography (CT) is often used to image diarthrodial joints, the lower bounds for detecting articular cartilage thickness and the influence of imaging parameters on the ability to image cartilage have not been reported. The use of contrast agent (CT arthrography) is necessary to visualize the surface of articular cartilage in live patients. Thus, it is of primary interest to quantify the accuracy of CT arthrography to demonstrate the feasibility of patient-specific modeling. The objectives of this study were to assess the accuracy and detection limits of CT for measuring simulated cartilage thickness using a phantom and to quantify changes in accuracy due to alterations in contrast agent concentration, imaging plane direction, spatial resolution and joint spacing.

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Stereoscopic Particle Image Velocimetry of Native Ovine Mitral Valves in a Pulsatile Left Heart Simulator

ASME 2012 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2012-80144 ◽

2012 ◽

Author(s):

Jean-Pierre Rabbah ◽

Neelakantan Saikrishnan ◽

Ajit P. Yoganathan

Keyword(s):

Particle Image Velocimetry ◽

Mitral Valve ◽

Computational Models ◽

Particle Image ◽

Numerical Models ◽

Stereoscopic Particle Image Velocimetry ◽

Patient Specific ◽

Flow Loop ◽

Mitral Valves ◽

Image Velocimetry

Patient specific mitral valve computational models are being actively developed to facilitate surgical planning. These numerical models increasingly employ more realistic geometries, kinematics, and mechanical properties, which in turn requires rigorous experimental validation [1]. However, to date, native mitral flow dynamics have not been accurately and comprehensively characterized. In this study, we used Stereoscopic Particle Image Velocimetry (SPIV) to characterize the ventricular flow field proximal to a native mitral valve in a pulsatile experimental flow loop.

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Construction of patient‐specific computational models for organ dose estimation in radiological imaging

Medical Physics ◽

10.1002/mp.13471 ◽

2019 ◽

Vol 46 (5) ◽

pp. 2403-2411 ◽

Cited By ~ 2

Author(s):

Tianwu Xie ◽

Azadeh Akhavanallaf ◽

Habib Zaidi

Keyword(s):

Computational Models ◽

Organ Dose ◽

Patient Specific ◽

Radiological Imaging ◽

Dose Estimation

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Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2019.0347 ◽

2020 ◽

Vol 378 (2173) ◽

pp. 20190347 ◽

Cited By ~ 1

Author(s):

Nick van Osta ◽

Aurore Lyon ◽

Feddo Kirkels ◽

Tijmen Koopsen ◽

Tim van Loon ◽

...

Keyword(s):

Right Ventricular ◽

Computational Models ◽

Clinical Decision Making ◽

Early Stage ◽

Patient Specific ◽

Model Parameters ◽

Arrhythmogenic Cardiomyopathy ◽

Tissue Properties ◽

Myocardial Disease ◽

Deformation Patterns

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters ( n = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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