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Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests., (Copyright © 2020. Published by Elsevier B.V.)

Aims: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT)., Methods and Results: We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT-D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K-means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT-D on the primary outcome (all-cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT-D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19-0.64; P = 0.0005 and HR 0.36; 95% CI 0.19-0.68; P = 0.001] than observed in the other groups (interaction P = 0.02)., Conclusions: Our results serve as a proof-of-concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies., (© 2018 The Authors. European Journal of Heart Failure © 2018 European Society of Cardiology.)

Contemporary imaging modalities offer noninvasive quantification of myocardial deformation; however, they make gross assumptions about internal structure of the cardiac walls. Our aim is to study the possible impact of the trabeculations on the stroke volume, strain, and capacity of differently sized ventricles. The cardiac left ventricle is represented by an ellipsoid and the trabeculations by a tissue occupying a fixed volume. The ventricular contraction is modeled by scaling the ellipsoid whereupon the measurements of longitudinal strain, end-diastolic, end-systolic, and stroke volumes are derived and compared. When the trabeculated and nontrabeculated ventricles, having the same geometry and deformation pattern, contain the same amount of blood and contract with the same strain, we observed an increased stroke volume in our model of the trabeculated ventricle. When these ventricles contain and eject the same amount of blood, we observed a reduced strain in the trabeculated case. We identified that a trade-off between the strain and the amount of trabeculations could be reached with a 0.35- to 0.41-cm dense trabeculated layer, without blood filled recesses (for a ventricle with end-diastolic volume of about 150 mL). A trabeculated ventricle can work at lower strains compared to a nontrabeculated ventricle to produce the same stroke volume, which could be a possible explanation why athletes and pregnant women develop reversible signs of left ventricular noncompaction, since the trabeculations could help generating extra cardiac output. This knowledge might help to assess heart failure patients with dilated cardiomyopathies who often show signs of noncompaction., (Copyright © 2017 John Wiley & Sons, Ltd.)

Reparameterization of surfaces is a widely used tool in computer graphics known mostly from the remeshing algorithms. Recently, the surface reparameterization techniques started to gain popularity in the field of medical imaging, but mostly for convenient 2D visualization of the information initially represented on 3D surfaces (e.g. continuous bulls-eye plot). However, by consistently mapping the 3D information to the same 2D domain, surface reparameterization techniques allow us to put into correspondence anatomical shapes of inherently different geometry. In this paper, we propose a method for anatomical parameterization of cardiac ventricular anatomies that include myocardium, trabeculations, tendons and papillary muscles. The proposed method utilizes a quasi-conformal flattening of the myocardial surfaces of the left and right cardiac ventricles and extending it to cover the interior of the cavities using the local coordinates given by the solution of the Laplace's equation. Subsequently, we define a geometry independent representation for the detailed cardiac left and right ventricular anatomies that can be used for convenient visualization and statistical analysis of the trabeculations in a population. Lastly we show how it can be used for mapping the detailed cardiac anatomy between different hearts, which is of considerable interest for detailed cardiac computational models or shape atlases., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Integration of electrical and structural information for scar characterization in the left ventricle (LV) is a crucial step to better guide radio-frequency ablation therapies, which are usually performed in complex ventricular tachycardia (VT) cases. This integration requires finding a common representation where to map the electrical information from the electro-anatomical map (EAM) surfaces and tissue viability information from delay-enhancement magnetic resonance images (DE-MRI). However, the development of a consistent integration method is still an open problem due to the lack of a proper evaluation framework to assess its accuracy. In this paper we present both: (i) an evaluation framework to assess the accuracy of EAM and imaging integration strategies with simulated EAM data and a set of global and local measures; and (ii) a new integration methodology based on a planar disk representation where the LV surface meshes are quasi-conformally mapped (QCM) by flattening, allowing for simultaneous visualization and joint analysis of the multi-modal data. The developed evaluation framework was applied to estimate the accuracy of the QCM-based integration strategy on a benchmark dataset of 128 synthetically generated ground-truth cases presenting different scar configurations and EAM characteristics. The obtained results demonstrate a significant reduction in global overlap errors (50-100%) with respect to state-of-the-art integration techniques, also better preserving the local topology of small structures such as conduction channels in scars. Data from seventeen VT patients were also used to study the feasibility of the QCM technique in a clinical setting, consistently outperforming the alternative integration techniques in the presence of sparse and noisy clinical data. The proposed evaluation framework has allowed a rigorous comparison of different EAM and imaging data integration strategies, providing useful information to better guide clinical practice in complex cardiac interventions., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The construction of statistical shape models (SSMs) that are rich, i.e., that represent well the natural and complex variability of anatomical structures, is an important research topic in medical imaging. To this end, existing works have addressed the limited availability of training data by decomposing the shape variability hierarchically or by combining statistical and synthetic models built using artificially created modes of variation. In this paper, we present instead a method that merges multiple statistical models of 3D shapes into a single integrated model, thus effectively encoding extra variability that is anatomically meaningful, without the need for the original or new real datasets. The proposed framework has great flexibility due to its ability to merge multiple statistical models with unknown point correspondences. The approach is beneficial in order to re-use and complement pre-existing SSMs when the original raw data cannot be exchanged due to ethical, legal, or practical reasons. To this end, this paper describes two main stages, i.e., (1) statistical model normalization and (2) statistical model integration. The normalization algorithm uses surface-based registration to bring the input models into a common shape parameterization with point correspondence established across eigenspaces. This allows the model fusion algorithm to be applied in a coherent manner across models, with the aim to obtain a single unified statistical model of shape with improved generalization ability. The framework is validated with statistical models of the left and right cardiac ventricles, the L1 vertebra, and the caudate nucleus, constructed at distinct research centers based on different imaging modalities (CT and MRI) and point correspondences. The results demonstrate that the model integration is statistically and anatomically meaningful, with potential value for merging pre-existing multi-modality statistical models of 3D shapes., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The assessment of cardiac pump function and the potential of local myocardium to contribute to the overall performance are of great importance in many cardiovascular abnormalities. Assessing intrinsic cardiac function requires obtaining information on the true contractility of the heart muscle, assessed locally but interpreted in the context of its contribution to the global ejection performance and potential to adapt to changing circumstances. Contemporary imaging techniques offer the possibility of noninvasive quantification of myocardial deformation. These new clinical tools are attractive to use for the assessment of ventricular function. However, it is of great importance to understand cardiac mechanics - a complex interplay between the tissue structure/shape, force development, and interaction with the environment/neighbors - to interpret alterations in deformation and to extract clinically relevant conclusions. The combination of insight into the measurement techniques and their limitations, together with knowledge of myocardial mechanics and physiology, opens new perspectives to improve the assessment and management of fetal, pediatric, and adult patients., (Copyright © 2012 S. Karger AG, Basel.)