Medical imaging plays a crucial role in the diagnosis and understanding of various pathological conditions, particularly in the cardiovascular domain. Increasingly, these imaging techniques are complemented by computational technologies grounded in mathematical modeling, enabling the extraction of clinically relevant insights that are not directly visible in raw images. In this minisymposium, we will discuss the latest developments in this interdisciplinary field at the intersection of imaging, mathematics, and computational science. International experts and researchers will focus on algorithmic issues and the use of both classical and newer techniques involving machine learning methods for image processing and for solving the underlying mathematical equations. We will explore both model-driven and data-driven strategies, with particular attention to hybrid methods that combine the strengths of both paradigms. The symposium will also address the clinical applicability of these approaches, along with efforts in validation and translation to real-world healthcare settings.