This project will use deep learning to recover image information so the images can be used for quantitative MRI.
Susceptibility weighted imaging is a magnetic resonance imaging (MRI) scan widely used in routine brain exams. The scan combines magnitude (which corresponds to the brightness of a lighthouse beam) and phase (which corresponds to the beam’s direction) information for improved image contrast. On clinical scanners, the phase has to be filtered so it can be used for SWI, and the original phase is deleted. Due to the filtering, these filtered phase images cannot be used as input into quantitative susceptibility mapping (QSM), a more recent method that allows the quantification of tissue damage. Such maps of susceptibility promise to measure brain iron, myelin content and venous blood oxygenation. The goal of this project is to make these phase images accessible to quantitative susceptibility mapping. With our approach, researchers and clinicians will be able to compute QSM on millions of existing and future clinical scans where no original phase data are available.
Amazing news! Congratulations!
– Dr. Steven Paul Miller, MDCM MAS FRCPC, Professor and Head, Pediatrics
– Dr. Mary Connolly, Head, Division of Neurology
Also worth noting, Dr. Rauscher held a Canada Research Chair Tier II (CIHR) in Developmental Neuroimaging; he was recognized with a CIHR New Investigator Award; and, he is a recipient of the Green College Leading Scholars Program Award.