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.
