Microstructural characterization of myocardial infarction with optical coherence tractography and two‐photon microscopy

Abstract Myocardial infarction leads to complex changes in the fiber architecture of the heart. Here, we present a novel optical approach to characterize these changes in intact hearts in three dimensions. Optical coherence tomography (OCT) was used to derive a depth‐resolved field of orientation on which tractography was performed. Tractography of healthy myocardium revealed a smooth linear transition in fiber inclination or helix angle from the epicardium to endocardium. Conversely, in infarcted hearts, no coherent microstructure could be identified in the infarct with OCT. Additional characterization of the infarct was performed by the measurement of light attenuation and with two‐photon microscopy. Myofibers were imaged using autofluorescence and collagen fibers using second harmonic generation. This revealed the presence of two distinct microstructural patterns in areas of the infarct with high light attenuation. In the presence of residual myofibers, the surrounding collagen fibers were aligned in a coherent manner parallel to the myofibers. In the absence of residual myofibers, the collagen fibers were randomly oriented and lacked any microstructural coherence. The presence of residual myofibers thus exerts a profound effect on the microstructural properties of the infarct scar and consequently the risk of aneurysm formation and arrhythmias. Catheter‐based approaches to segment and image myocardial microstructure in humans are feasible and could play a valuable role in guiding the development of strategies to improve infarct healing

Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before