Accelerating Estimation of Perfusion Maps in Contrast X-ray Computed Tomography using Many-core CPUs and GPUs

Abstract

X-ray Computed Tomography (CT) perfusion imaging is a non-invasive medical imaging modality that has been established as a fast and economical method for diagnosing cerebrovascular diseases such as acute ischemia, sub-arachnoid hemorrhage, and vasospasm. Current CT perfusion imaging being dynamic in nature, requires three-dimensional data acquisition at multiple time points, resulting in a long time for processing ranging from six to twelve minutes post acquisition. In emergency medical conditions such as stroke, every second is crucial for obtaining the perfusion maps, which are used for deploying brain-saving therapies. Since time is of the utmost importance, this thesis work attempts to develop strategies for computationally accelerating the processing of the CT perfusion data to provide perfusion maps using many-core CPUs and GPUs.

Current major steps involved in perfusion maps estimation from CT perfusion data involve estimation of Arterial Input Function (AIF), followed by model-based deconvolution of AIF from tissue enhancement curves pixel-by-pixel to assess the cerebral blood flow (CBF) accurately. The deconvolution of the AIF is embarrassingly parallel and current methodologies do not account for this process to be accelerated using high performance computing environments. Specifically, this thesis utilises the multiple CPU cores that are available in current computing environments as well as General Purpose Graphics Processing Units (GP-GPUs) to provide massively parallel computing power to parallelise the deconvolution process at the pixel level. The GPUs are attractive for this application as they are built on the SIMD (Single Instruction Multiple Data) architecture. Though there are multiple ways of solving the ill-posed inverse problem of deconvolution for obtaining high-quality perfusion maps, this thesis work focuses on the Circulant Truncated-SVD based method, which was implemented using the Nvidia CUDA API that Nvidia provides for its GPUs.

Further, this thesis work explores the algorithms that work for single-AIF deconvolution, which, though not very accurate, is a very good first approximation for time-critical cases to know the area of damage. These experiments were followed by the exploration of multiple-AIF deconvolution, which, although slow, is the gold standard for brain perfusion imaging. These algorithms were developed using the KBLAS library which utilizes multiple CPU and GPU cores. A detailed computational analysis through use cases reveals that GP-GPU computing is a viable option for accelerating the X-ray CT perfusion imaging and are attractive in clinic due to the footprint of these GPU machines.