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Abstract #2954

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

Kevin M. Koch1,2 and Andrew S. Nencka1,2
1Radiology, Medical College of Wisconsin, Milwaukee, WI, United States, 2Center for Imaging Research, Medical College of Wisconsin, Milwaukee, WI, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Vessels, Metal ArtifactMulti-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.

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