Alexander Loktyushin

Subject motion during MRI scans can cause severe degradations of the image quality. The problem of correcting for such motion artifacts is one of the most important remaining problems in the field to be solved. Even a few millimeter displacement of the imaged object is enough to generate motion artifacts, which usually appear as ghosts and blur, and make a scan unacceptable for a medical analysis. The acquisition time of high resolution scans can be of an order of minutes, which together with a requirement to keep motionless in millimeter scale is a challenge even for healthy and cooperative subjects. Patients with movement disorders, elder and child population are particularly prone to motion during the image acquisition, while at the same time being the categories of patients who are likely to benefit from MR diagnostics.

Together with my colleagues I am developing data-driven retrospective motion correction algorithms aimed at improving the quality of 3D MRI scans affected by both rigid and non-rigid motion. The crucial aspect of our algorithms is that they require no information about the displacements of a patient in the scanner, i.e. guiding from the tracking cameras. Furthermore, the developed techniques use the raw data from standard imaging sequences requiring no modifications in the scanning pipeline. Importantly, the methods are implemented to run on the graphic cards in order to attain short computation times, which are on the order of seconds when rigid motion is assumed, and on the order of minutes when more complicated non-rigid motion needs to be corrected.