Time-frequency analysis based motion detection in perfusion weighted MRI

Abstract

Abstract-In this paper, we present a novel automated method to detect motion in perfusion weighted images (PWI), which is a type of magnetic resonance imaging (MRI). In PWI, blood perfusion is measured by injecting an exogenous tracer called bolus into the blood flow of a patient and then tracking it in the brain. PWI requires a long data acquisition time to form a time series of volumes. Hence, motion occurs due to patient's unavoidable movements during a scan, which in turn results into motion corrupted data. There is a necessity of detection of these motion artifacts on captured data for correct disease diagnosis. In PWI, intensity profile gets disturbed due to occurrence of motion and/or bolus passage through the blood vessels. There is no way to distinguish between motion occurrence and bolus passage. In this paper, we propose an efficient time-frequency analysis based motion detection method. We show that proposed method is computationally inexpensive and fast. This method is evaluated on a DSC-MRI sequence with simulated motion of different degrees. We show that our approach detects motion in a few seconds.

Research on Rotating Machinery Fault Diagnosis Based on an Improved Eulerian Video Motion Magnification

“…The method was originally used for medical remote monitoring. Sushma [22] adopted the method of motion magnification to detect motion in perfusion-weighted MRIs, so as to reduce the data destruction caused by patients' involuntary activities during the wait for blood perfusion. Thomas V. et al [23] adopted the motion magnification method based on the Riesz Pyramid to make the changes in blood flow, head movement and skin and facial color controlled by the heartbeat's rhythm more obvious.…”