Sakthivel Sadayappan | Searcharxiv

arXiv2026

AI-enabled cardiac shape reconstruction from routine magnetic resonance imaging

Computational models of cardiac structure and function are increasingly central to the development of subject-specific cardiac digital twins, enabling improved characterization of contractile dysfunction, pathological remodeling, and electrical abnormalities. A critical prerequisite for these models is the accurate reconstruction of three-dimensional (3D) cardiac anatomy from medical imaging. Multi-planar magnetic resonance imaging, particularly when combined with artificial intelligence, offers a clinically feasible alternative to conventional reconstruction techniques. In this study, we present a neural field-based reconstruction framework that recovers 3D cardiac geometries from sparse planar contour data by learning continuous shape representations. Reconstruction performance was evaluated using complementary in-silico and in vivo datasets spanning variations in sampling density and geometric complexity. Across both datasets, reconstructed meshes closely matched reference geometries, demonstrating that the neural field approach faithfully captures cardiac planar contours. Compared with traditional local interpolation methods, the proposed framework exhibited improved geometric fidelity in anatomically challenging regions, including the left ventricular apex and basal segments, particularly under sparse sampling conditions. Collectively, these findings demonstrate that neural field-based reconstruction provides a robust and efficient pathway for multi-planar cardiac shape recovery, with particular relevance for AI-driven modeling pipelines and data-limited settings such as small-animal and time-resolved cardiac imaging.

physics.med-ph↗

arXiv2025

Human-Guided Feature Selection for Accurate Cardiomyocyte Dysfunction Classification

Early identification of cardiomyocyte dysfunction is a critical challenge for the prognosis of diastolic heart failure (DHF) exhibiting impaired left ventricular relaxation (ILVR). Myocardial relaxation relies strongly on efficient intracellular calcium (${\text{Ca}}^{2+}$) handling. During diastole, a sluggish removal of ${\text{Ca}}^{2+}$ from cardiomyocytes disrupts sarcomere relaxation, leading to ILVR \textit{at the organ level}. Characterizing myocardial relaxation \textit{at the cellular level} requires analyzing both sarcomere length (SL) transients and intracellular calcium kinetics (CK). However, due to the complexity and redundancy in SL and CK data, identifying the most informative features for accurate classification is challenging. To address this, we developed a robust feature selection pipeline involving statistical significance testing (p-values), hierarchical clustering, and feature importance evaluation using random forest (RF) classification to select the most informative features from SL and CK data. SL and CK transients were obtained from prior studies involving a transgenic phospho-ablated mouse model exhibiting ILVR (AAA mice) and wild-type as non-transgenic control mice (NTG). By iteratively refining the feature set, we trained a RF classifier using the selected reduced features. For comparison, we evaluated the performance of the classifier using the full set of original features as well as a dimensionally reduced set derived through principal component analysis (PCA). The confusion matrices demonstrated that the reduced feature set achieved comparable performance to the full feature set and outperformed the PCA-based approach, while offering better interpretability by retaining biologically relevant features. These findings suggest that a small, carefully chosen set of biological features can effectively detect early signs of cardiomyocyte dysfunction.

q-bio.QM↗