In Partial Fulfillment of the Requirements for the Degree of Master of Science
will defend his thesis
Generation of Synthetic MRI with Deep Flow Field Estimation for Faster MR Imaging
Magnetic resonance imaging (MRI) is an effective, non-invasive, and revolutionary imaging technique used for diagnosis, study, and analyzing of chemical and physical structures inside the body. MR image acquisition suffers from two significant problems. First of all, prolonged scanning sessions are inconvenient and costly for patients. Secondly, artifacts caused by the motion of the patients or external noise. Addressing these two issues is a strong motivation for making MRI procedures faster and more accurate. A wide variety of methods has been used to shorten the MR image acquisition by optimizing current techniques or improving the mechanical and computational performance of the scanners. An appealing solution for the mentioned problems is to scan a fewer number of MR images and generate in-between image to make the MRI procedure faster. Also, in the presence of motion artifacts, we can reconstruct the imperfect images, if such a technique is available. In this thesis, we trained and applied a deep learning model based on optical flow fields of a sequence of MR images for synthesizing an arbitrary number of in-between MR frames or slices.
Date: Monday, November 18, 2019
Time: 12:00 PM - 1:00 PM
Place: PGH 501D
Advisor: Dr. Nikolaos V. Tsekos
Faculty, students, and the general public are invited.