The heart is an intricate electromechanical pump whose function is highly preserved across species. Understanding the details of cardiac function and homeostasis is fundamental to develop effective treatments for cardiovascular diseases. In this talk, we present a framework that utilizes advanced magnetic resonance imaging (MRI) data to drive hypothesis generation and the development of mechanistic cardiac models. While pressure-volume loops are a traditional validation target in the field, they often fail to capture the local mechanical complexities of the myocardium due to leaving the option that even significantly inaccurate deformation fields can still yield plausible pressure-volume curves. By combining in-vivo and ex-vivo MRI measurements, we propose a comprehensive set of validation criteria [1]. This includes global strains, characteristic kinematic quantities, and microstructural mobility during contraction, extending far beyond the typically used global hemodynamic metrics. Within this data-driven context, we employ our open-source multiphysics framework, Thunderbolt.jl [2], and an extended Hill framework [3] to analyze the active material behavior of the heart. We investigate how different modeling assumptions and anatomical complexities influence the simulated mechanical state when compared against experimental data. This synthesis of imaging data and unified contraction modeling demonstrates how systematic structural investigations can provide concrete guidelines for model development. Our goal is to clarify the essential features required for high-fidelity cardiac simulations, establishing a robust platform for investigating cardiac disease.