Cardiovascular disease (CVD) is the leading cause of mortality worldwide, underscoring the need for accurate and accessible cardiac imaging. Although coronary catheter angiography is the gold standard for CVD evaluation, it is invasive and relatively expensive. In contrast, cardiac computed tomography angiography (CTA) is a more practical alternative because it is less invasive, more affordable, and more accessible. Despite these advantages, its image quality is often degraded by motion artifacts from the continuously beating heart. To reduce these artifacts, CTA commonly relies on electrocardiogram (ECG) gating to estimate cardiac quiescence, defined as the period of minimal cardiac motion. However, ECG reflects electrical activity rather than mechanical motion, making it an indirect surrogate for identifying true quiescent periods. Mechanical signals such as seismocardiography (SCG) and ultrasound (US) therefore offer promising alternatives for improving quiescence prediction. Although ultrasound has previously shown promise for identifying cardiac quiescence, its use during CT acquisition was limited by streak artifacts from conventional transducers. This dissertation develops multimodal gating frameworks to improve cardiac quiescence prediction for CTA. First, an SCG-ECG prediction framework was extended with a regression-based artificial neural network and evaluated using patient data, with diagnostic image quality assessed through a blinded reader study. Second, a deep learning-based US-ECG gating framework incorporating convolutional neural networks was developed and evaluated using multimodal datasets acquired from a dynamic heart motion phantom, enabling multimodal data acquisition under varied motion conditions. Third, the effect of US-ECG-based quiescence prediction on diagnostic image quality was assessed through a phantom-based CT reader study comparing reconstructed CTA images with conventional gating strategies. These contributions highlight the potential of multimodal gating to improve cardiac CTA and advance translation toward more reliable clinical imaging workflows.