Scan Conversion for a Multiprocessor-based Ultrasound Processing System

摘要

To meet the computational requirements of mid-range and high-end programmable ultrasound systems, multiple processors are currently required. Algorithms optimized specifically for a single processor-based system may not perform well in a multiprocessor environment. They need to be efficiently remapped on multiple processors to take advantage of the increased computing power while minimizing the interprocessor data transfer and the latency between data acquisition and display. In this paper, we describe a multiprocessor-based implementation of scan conversion, a key processing task in an ultrasound system that geometrically transforms the acquired polar ultrasound data to Cartesian coordinates for display. The single processor-based scan conversion algorithm that was reported previously uses inverse mapping for geometric transformation, where the pixel values in the Cartesian display are determined from data in the polar domain. Inverse mapping requires access to a full frame of pre-scan-converted ultrasound data, which in a multiprocessor system can be located across multiple processors, thus requiring a significant amount of interprocessor data communications. Our modified scan conversion algorithm reduces the data movement by performing inverse-mapped scan conversion locally on the polar-domain data present in each processor's memory. Each processor handles a smaller amount of data, thus reducing the latency. The raster pixels generated by each processor are combined later. Interprocessor synchronization is used to ensure that each processor displays data belonging to the same frame. Data overlapping between processors avoids boundary artifacts between regions that are processed on different processors. Using four Hitachi/Equator Technologies' 300-MHz MAP-CA processors, scan conversion requires 5.6 ms for a 600x420 RGB frame, as compared to 14.6 ms using a single processor, and the latency is reduced by 33.3%. We believe that this type of parallel algorithms will facilitate the development and deployment of flexible multiprocessor-based ultrasound and other medical imaging systems.