Analysis of spatio-temporal prediction methods in 4D volumetric medical image datasets

Due to the huge amount of data and the increasing utilization of 4D medical image processing, compression of such data sets is essential. Unlike moving 3D objects in computer graphic applications, medical 4D datasets consist of a number of sampled volume elements, varying in time. Based on an analysis of H.264 compression for such data, this paper presents a spatio-temporal prediction scheme leveraging effective block-based prediction for 4D volumetric image data. Experimental results show that this new spatiotemporal approach achieves better prediction results than pure spatial or temporal schemes.