Dynamic Contrast Enhanced MRI (DCE-MRI) is being increasingly used as a method for studying the tumor
vasculature. It is also used as a biomarker to evaluate the response to anti-angiogenic therapies and the efficacy of a
therapy. The uptake of contrast in the tissue is analyzed using pharmacokinetic models for understanding the perfusion
characteristics and cell structure, which are indicative of tumor proliferation. However, in most of these 4D acquisitions
the time required for the complete scan are quite long as sufficient time must be allowed for the passage of contrast
medium from the vasculature to the tumor interstitium and subsequent extraction. Patient motion during such long scans
is one of the major challenges that hamper automated and robust quantification. A system that could automatically detect
if motion has occurred during the acquisition would be extremely beneficial. Patient motion observed during such 4D
acquisitions are often rapid shifts, probably due to involuntary actions such as coughing, sneezing, peristalsis, or jerk due
to discomfort. The detection of such abrupt motion would help to decide on a course of action for correction for motion
such as eliminating time frames affected by motion from analysis, or employing a registration algorithm, or even
considering the exam us unanalyzable. In this paper a new technique is proposed for effective detection of motion in 4D
medical scans by determination of the variation in the signal characteristics from multiple regions of interest across time.
This approach offers a robust, powerful, yet simple technique to detect motion.