SU‐E‐I‐83: Parallel Programming Upgrades for the Control Acquisition, Processing and Image Display System (CAPIDS) of the Micro Angiographic Fluoroscope (MAF)

Abstract

Purpose:CAPIDS is a unique software platform designed to control and acquire images from the high‐resolution MAF detector, process them and display them in a clinical environment. The images are then stored for optional playback at a later stage. CAPIDS also acquires and records the exposure parameters from the x‐ray unit. We present new parallel programming modifications using the host computer system's Graphics Processing Unit (GPU) and Central Processing unit (CPU) to improve the system performance for the various MAF imaging tasks.Methods:Multicore CPU's allow for concurrent tasks to be executed at the same time in parallel. During runtime, CAPIDS has three concurrent tasks: image acquisition and processing, image saving, and exposure parameter acquisition. Parallel programming constructs from LabVIEW allow each tasks to be executed on a separate core.GPU's allow for the same task to be performed on independent data sets in parallel. During runtime, all the image processing including flat‐field correction, digital image subtraction, image averaging, and temporal recursive filtering are performed on the GPU.Results:The new version of CAPIDS with all the parallel programming updates was successfully used for the first time to control the MAF, acquire the images, process the images and display the images during an actual clinical intervention. The images were acquired under fluoroscopy, digital subtraction angiography, and roadmap modes.Conclusion:Distributing concurrent tasks to different cores of a multicore CPU results in an efficient utilization of resources, efficient power management and increases operation speed. Use of GPU's for image processing further enhances the speed of operation.Supported by NIH Grant: 2R01EB002873 and an equipment grant from Toshiba Medical Systems Corporation