Implementing real-time adaptive filtering for medical applications on the cell processor using a generic multicore framework

Adaptive filtering is a compute-intensive algorithm aimed at effectively reducing noise without blurring the structures contained in a set of digital images. In this study, we take a generalized approach for adaptive filtering based on seven oriented filters, each individual filter implemented by a two-dimensional (2D) convolution with a mask size of 11 by 11 pixels. Digital radiology workflow imposes severe real-time constraints that require the use of hardware acceleration such as provided by multicore processors. Implementing complex algorithms on heterogeneous multicore architectures is a complex task especially for taking advantage of the DMA engines. We have implemented the algorithm on a Cell Broadband Engine (CBE) processor clocked at 3.2 GHz using a generic framework for multicore processors. This implementation is capable of filtering images of 5122 pixels at a throughput of 40 frames per second while allowing changing the parameters in real time. The resulting images are directed to the DR monitor or to the real-time computed tomography (CT) reconstruction engine.