Image sparsity priors are used to overcome sampling limits.

Medical imaging scan assistance suggests optimal scan parameter settings for the desired options of the user between minimum scan-time and best image quality. The assistance also standardizes the scan parameter settings that rely on personal experience in conventional.

A new paradigm for evaluating image quality is needed in the new era of image reconstruction that involves non-linear and complex processes. ​Our image quality assessment system visualizes unseen artifacts even from the seemingly nice images. The results of the evaluation system improve the reliability of AI powered medical image reconstruction process from the user.

Once the image quality assessment system displays unwanted artifacts in medical images, a quality enhancement technique can resuscitate artifact-free images. The image quality enhancement technology is applicable for motion artifact, metal artifact, and even hardware imperfection. ​Both physics models and data-driven knowledge are integrated into the technology to ensure the enhanced outcomes.