On Post-Acquisition Motion Compensation for Prostate Perfusion Analysis

Abstract

Dynamic Contrast enhance magnetic resonance imaging has been established as an accurate method to detect and localize prostate cancer. Time series of three-dimensional datasets of the prostate are acquired and used to obtain per-voxel signal-intensity vs. time curves. These are then used to differentiate cancerous from non-cancerous tissue. However, rectal peristalsis and patient movement may result in spatial-mismatching of the serial datasets and therefore, incorrect enhancement curves. In this work, we discuss and test four methods based on image registration to compensate for these movements. These methods include a serial approach that uses the registration of consecutive images and the accumulation of the obtained transformations, an all-to one registration approach, an approach that first aligns a sub-set of images that are already closely aligned, and then uses synthetic references to register the remaining images, and an approach that uses independent component analysis (ICA) to create synthetic references and register the images to these. We conclude that the method based on ICA does not provide a viable approach for motion compensation in prostate perfusion imaging, and that the serial approach fails when motion artifacts are present in the series. The other two approaches provide qualitatively pleasing results.