Adjunct processors in embedded medical imaging systems

摘要

Adjunct processors have traditionally been used for certain tasks in medical imaging systems. Often based on application-specific integrated circuits (ASICs), these processors formed X-ray image-processing pipelines or constituted the backprojectors in computed tomography (CT) systems. We examine appropriate functions to perform with adjunct processing and draw some conclusions about system design trade-offs. These trade-offs have traditionally focused on the required performance and flexibility of individual system components, with increasing emphasis on time-to-market impact. Typically, front-end processing close to the sensor has the most intensive processing requirements. However, the performance capabilities of each level are dynamic and the system architect must keep abreast of the current capabilities of all options to remain competitive. Designers are searching for the most efficient implementation of their particular system requirements. We cite algorithm characteristics that point to effective solutions by adjunct processors. We have developed a field-programmable gate array (FPGA) adjunct-processor solution for a Cone-Beam Reconstruction (CBR) algorithm that offers significant performance improvements over a general-purpose processor implementation. The same hardware could efficiently perform other image processing functions such as two-dimensional (2D) convolution. The potential performance, price, operating power, and flexibility advantages of an FPGA adjunct processor over an ASIC, DSP or general-purpose processing solutions are compelling.