Image-Based Meshing of Patient-Specific Data: Converting Medical Scans Into Highly Accurate Computational Models

2008 ◽  
Author(s):  
Rajab Said ◽  
Jenssen Chang ◽  
Philippe Young ◽  
Gavin Tabor ◽  
Sam Coward
Keyword(s):  
Computational Models ◽  
Patient Specific ◽  
Specific Data ◽  
Image Based Meshing

Evaluation of a computerized cardiovascular simulation system using patient-specific data

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
M Kaur ◽  
N Sprunk ◽  
U Schreiber ◽  
R Lange ◽  
J Weipert ◽  
...  
Keyword(s):  
Simulation System ◽  
Patient Specific ◽  
Specific Data ◽  
Cardiovascular Simulation

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
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.

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

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.

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

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.

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

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190347 ◽  
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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