Scar conducting channel characterization to predict arrhythmogenicity during ventricular tachycardia ablation.

AIMS: Heterogeneous tissue channels (HTCs) detected by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) are related to ventricular arrhythmias, but there are few published data about their arrhythmogenic characteristics. METHODS AND RESULTS: We enrolled 34 consecutive patients with ischaemic and non-ischaemic cardiomyopathy who were referred for ventricular tachycardia (VT) ablation. LGE-CMR was performed prior to ablation, and the HTCs were analyzed. Arrhythmogenic HTCs linked to induced VT were identified during the VT ablation procedure. The characteristics of arrhythmogenic HTCs were compared with those of non-arrhythmogenic HTCs. Three patients were excluded due to low-quality LGE-CMR images. A total of 87 HTCs were identified on LGE-CMR in 31 patients (age:63.8 ± 12.3 years; 96.8% male; left ventricular ejection fraction: 36.1 ± 10.7%). Of the 87 HTCs, only 31 were considered arrhythmogenic because of their relation to a VT isthmus. The HTCs related to a VT isthmus were longer [64.6 ± 49.4 vs. 32.9 ± 26.6 mm; OR: 1.02; 95% CI: (1.01-1.04); P < 0.001] and had greater mass [2.5 ± 2.2 vs. 1.2 ± 1.2 grams; OR: 1.62; 95% CI: (1.18-2.21); P < 0.001], a higher degree of protectedness [26.19 ± 19.2 vs. 10.74 ± 8.4; OR 1.09; 95% CI: (1.04-1.14); P < 0.001], higher transmurality [number of wall layers with CCs: 3.8 ± 2.4 vs. 2.4 ± 2.0; OR: 1.31; 95% CI: (1.07-1.60); P = 0.008] and more ramifications [3.8 ± 2.0 vs. 2.7 ± 1.1; OR: 1.59; 95% CI: (1.15-2.19); P = 0.002] than non-arrhythmogenic HTCs. Multivariate logistic regression analysis revealed that protectedness was the strongest predictor of arrhythmogenicity. CONCLUSION: The protectedness of an HTC identified by LGE-CMR is strongly related to its arrhythmogenicity during VT ablation.

3-D Subject-Specific Shape and Density Estimation of the Lumbar Spine From a Single Anteroposterior DXA Image Including Assessment of Cortical and Trabecular Bone.

Dual Energy X-ray Absorptiometry (DXA) is the standard exam for osteoporosis diagnosis and fracture risk evaluation at the spine. However, numerous patients with bone fragility are not diagnosed as such. In fact, standard analysis of DXA images does not differentiate between trabecular and cortical bone; neither specifically assess of the bone density in the vertebral body, which is where most of the osteoporotic fractures occur. Quantitative computed tomography (QCT) is an alternative technique that overcomes limitations of DXA-based diagnosis. However, due to the high cost and radiation dose, QCT is not used for osteoporosis management. We propose a method that provides a 3-D subject-specific shape and density estimation of the lumbar spine from a single anteroposterior (AP) DXA image. A 3-D statistical shape and density model is built, using a training set of QCT scans, and registered onto the AP DXA image so that its projection matches it. Cortical and trabecular bone compartments are segmented using a model-based algorithm. Clinical measurements are performed at different bone compartments. Accuracy was evaluated by comparing DXA-derived to QCT-derived 3-D measurements for a validation set of 180 subjects. The shape accuracy was 1.51 mm at the total vertebra and 0.66 mm at the vertebral body. Correlation coefficients between DXA and QCT-derived measurements ranged from 0.81 to 0.97. The method proposed offers an insightful 3-D analysis of the lumbar spine, which could potentially improve osteoporosis and fracture risk assessment in patients who had an AP DXA scan of the lumbar spine without any additional examination.

3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images.

The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality.

OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project.

The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for example, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction-diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community.