Automated enhancement and correction of brain MRI images that leverages the entire imaging exam

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Project Summary/Abstract Subtle Medical, Inc. (Subtle), in collaboration with the University of Washington, proposes a Fast-Track project to develop and validate a software product capable of enhancing an entire brain magnetic resonance imaging (MRI) study. The proposed system will use state-of-the-art machine learning methods to build a product that integrates seamlessly into the Radiology workflow and standard patient care path to determine if any required MRI acquisitions are missing or corrupted, then correct the corrupted acquisitions and synthesize the missing acquisitions by incorporating the information contained in the appropriately acquired acquisitions. Brain studies make up approximately 20% of all MRI studies worldwide, and nearly all brain studies acquire multiple image contrasts to obtain complementary information. It has been reported that nearly 60% of all MRI acquisitions contain at least minimal motion artifacts. Moreover, important acquisitions are frequently skipped or forgotten during the study. The result is that a significant fraction of brain MRI studies are sent to radiologists with missing or poor-quality exam sequences that can reduce the quality of care while increasing the burden on the radiologist to read a suboptimal study. A successful completion of this project will result in a tool for imaging centers that will improve the consistency and quality of brain MRI studies and allow them to increase their throughput by removing the need to rescan acquisitions with artifacts. This will improve the experience and care of the patients while reducing the burden on radiologists and improving the efficiency of the imaging center’s operations.