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M.Tech. Research Thesis Defense (Oral Exam): Aug 29 @ 10 AM: “Accelerating Estimation of Perfusion Maps in Contrast X-ray Computed Tomography Using Many Core GPU and CPUs.”
29 Aug @ 10:00 AM -- 11:00 AM
DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES
M.Tech. Research Thesis Defense (Oral Exam)
Speaker : Mr. Rahul Wankhede
S.R. Number : 06-18-00-10-22-19-1-16583
Title : “Accelerating Estimation of Perfusion Maps in Contrast X-ray Computed Tomography Using Many Core GPU and CPUs.”
Research Supervisor : Prof. Phaneendra Kumar Yalavarthy
Date & Time : August 29, 2022 (Monday), 10:00 AM
Venue : #102, CDS Seminar Hall
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 GPU and CPUs.
Current major steps involved in perfusion maps estimation from CT perfusion data involves 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 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 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 focused 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, 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 utilisation of multiple-AIF deconvolution, although slow, is the gold standard for brain perfusion imaging. These algorithms were developed with the help of the KBLAS library which utilizes multiple CPU and GPU cores to computed perfusion maps in a span of seconds. A detailed computational analysis through use cases reveal that GP-GPU computing is a viable option for accelerating the X-ray CT perfusion imaging and are attractive in clinic due to the foot print of these GPU machines.