Memory-efficient 3D multi-resolution image enhancement and processing to increase throughput

Advanced signal processing such as multi-resolution decomposition and three-dimensional processing and data sets are gradually becoming a integral part of medical imaging. With the growing number of signal dimensions, the bandwidth requirements increase exponentially. Because memory bandwidth is a scarce parameter, this paper focusses on bandwidth optimization at the processor-chip level within multiprocessor systems. We introduce a practical model including formulas for the computing, memory and cache read/write procedures to optimize the mapping of data into the memory and cache for different configurations. A substantial performance improvement is realized by a new memory-communication model that incorporates the data-dependencies of the image-processing functions. More specifically, bandwidth optimization and minimization is achieved by implementing two measures: (1) breaking down the algorithm such that the processing gets a locality that fits with the cache size of the processor, and (2) a technique known from based on addressing and organizing the data prior to processing in such a way that memory traffic is minimized. For the experiments, we have concentrated particularly on image enhancement and noise reduction build around image pyramids for 3D X-ray data sets. First experimental results show a bandwidth reduction in the order of 80% and a throughput increase of 60% compared to straightforward implementation