Efficient Processing of Multidimensional Medical Data
Multidimensional data, Cardiac MRI, volume compression, volume rendering, motion etimation
New MRI techniques enable the reconstruction of multidimensional data sets, including 3D volumes of cardiac image data reconstructed along both cardiac and respiratory cycles. However, these new type of data present several challenges in their manipulation, visualization and analysis. Due to the huge size of these data, it is not trivial to get an overview or extract other information. Also, there are no off-the-shelf tools for interactive visualization of such high-dimensional data sets. In this work, we propose a way to approach these new kind of data sets, dealing with their huge amounts of data, producing animations with no lagging and allowing users to interact in real-time with the content. The partial results reported here are the result of incorporating the proposed developments into a multi-platform open source tool for the visualization and analysis of multidimensional cardiac data. We also propose the use of lossless compression techniques in order to store and manipulate the data efficiently and an associated file model.