M.Sc Thesis

M.Sc StudentLerner Tal
SubjectMotion Correction in fMRI Images
DepartmentDepartment of Computer Science
Supervisors ASSOCIATE PROF. Moshe Gur
PROF. Ehud Rivlin


This work presents a new vision-based system for motion correction in functional-MRI experiments. fMRI is a popular technique for studying brain functionality by utilizing MRI technology. In an fMRI experiment a subject is required to perform a task while his brain is being scanned by an MRI scanner. In order to achieve a high quality analysis, the fMRI slices should be aligned. Hence, the subject is requested to avoid head movements during the entire experiment. However, due to the long duration of such experiments, head motion is practically unavoidable. Most of the previous work in this field addresses this problem by extracting the head motion parameters from the acquired MRI data. Therefore, these studies are limited to relatively small movements and may confuse head motion with brain activities. In this work, the head movements are detected by a system comprised of two cameras with zooming capability. These cameras monitor a specially designed device worn on the subject's head and the motion is analyzed using a new multi camera approach referred to as the TwoCamPose algorithm. The system requires a calibration procedure to detect the subject's head motion w.r.t to the MRI system which the slices are represented in. For this purpose specially designed devices, such as the phantom, where used. The detected motion represented under the MRI system yields the compensating transformation which is applied on the slices to produce new motion-free slices. The system does not depend on the acquired MRI data and therefore can overcome large head movements. Additionally, the system can be extended to cope with inter-block motion and can be integrated into the MRI scanner for real-time updates of the scan-planes. The performance and applicability of the proposed system were tested in a laboratory environment and in fMRI experiments and proved to achieve a high quality correction of corrupted MRI data even when dealing with large head motion.