Image-Based Computational Cardiology: From Data to Understanding

Over the past decade, rapid advances in imaging technologies have enabled state-of-the-art image-based analysis of cardiac function at a variety of scales and from multiple aspects, such as cardiac electrophysiology, cardiac biomechanics, and cardiovascular system. This special issue aims to showcase developments in cardiac image computing over the past five years, with a special emphasis given to novel computing techniques used to analyze cardiac anatomy and functions for better understanding of cardiac diseases. The final issue includes 8 high quality manuscripts that addressed some very interesting and timely research questions faced by researchers worldwide. The topics range from the improvement of real-time imaging acquisition, through the design of novel image analysis techniques and image-guided surgical planning tools, to cardiovascular risk assessment and detection. Studying the evolution of heart formation has become an active research area [1]. Embryonic heart morphogenesis (EHM) is a complex and dynamic process where the heart transforms from a single tube into a four-chambered pump. Conventional imaging techniques fail to capture this process due to limited penetration depth. H. Mao et al. utilized confocal microscopy imaging with tissue optical immersion clearing technique to image the heart at different stages of development for an EHM study of quail. This area of study not only showed promising results in obtaining snapshots of hearts during growth, but also laid the foundation to investigate data-driven modeling of heart growth, which in turn could deepen our understanding of the cause, progression, treatment, and prevention of diseases related to cardiac growth. Improving the speed of image reconstruction directly enhances the abundance of the data. In the work of N. Cai et al., a new method was developed to improve compressive sensing (CS) based reconstruction for dynamic cardiac MRI leveraging the theory of structured sparse representation. Their experimental results demonstrated that the proposed method improved the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art CS method [2]. Whilst numerous researches have been dedicated to imaging cardiac structure and mechanical function, non-invasive imaging of cardiac electrical activities remains to be a challenging task which entails the use of sophisticated mathematical models and optimization techniques [3, 4]. A. Rahimi et al. presented the superiority of an Lp-norm model over its L1 and L2 counterparts in imaging cardiac current sources with various spatial extents and distributions. Through computer-simulated and real-data experiments, they further demonstrated the feasibility of the proposed method in imaging the complex structure …