The field of cardiovascular imaging and analysis is rapidly evolving, with a focus on developing innovative methods for accurate and efficient image reconstruction, segmentation, and analysis. Recent developments have centered around the use of deep learning techniques, such as diffusion models and neural operators, to improve the quality and speed of image reconstruction. Additionally, there is a growing interest in developing methods for real-time imaging and analysis, such as ultrasound computed tomography and cardiac motion tracking. These advances have the potential to improve diagnosis, treatment, and patient outcomes in cardiovascular medicine. Notable papers in this area include AortaDiff, which introduces a diffusion-based framework for generating smooth aortic surfaces from CT/MRI volumes, and Dyna3DGR, which proposes a novel framework for 4D cardiac motion tracking using dynamic 3D Gaussian representation. Overall, the field is moving towards more accurate, efficient, and personalized imaging and analysis methods, with significant potential for clinical impact.