Within Patient Contrast Adjustment Through a Self-Consistent Deep Learning Model when Imaging Near Metal: An Example in Angiography

Multi-spectral imaging around metallic implants allows for a unique application of subject-specific deep learning applications because both high-resolution standard acquisitions and multi-spectral acquisitions are performed with significant regions of artifact-free overlap. A deep neural network can be trained in that region of artifact-free overlap within a single patient to yield a contrast transform on the multi-spectral images and achieve similar contrast to traditional acquisitions in the region of those acquisitions obscured by metal artifacts. In this proof of concept, a preliminary example of synthesizing time of flight-like contrast from a multi-spectral acquisition with vascular flow voids was shown.