AI powered medical image reconstruction has an unprecedented performance to generate images accurately by utilizing pre-trained image inferences in deep neural network. The reconstruction solution achieves stability and generalizability in clinical practices by enforcing physics model as well as the network outcomes. As an example, the solution reconstructs ground-truth-quality images from highly accelerated scans without generating additional artifacts.
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.
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.
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