Name
Abstract | ||
|---|---|---|
In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. GPUs originated as graphics generation dedicated co-processors, but the modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its huge number of computing cores and constitutes an affordable high performance computing method. The objective of brain perfusion quantification is to generate parametric maps of relevant haemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that can be used in diagnosis of conditions such as stroke or brain tumors. These calculations involve deconvolution operations that in the case of using local Arterial Input Functions (AIF) can be very expensive computationally. We present the serial and parallel implementations of such algorithm and the evaluation of the performance gains using GPUs. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/HISB.2011.6 | HISB |
Keywords | Field | DocType |
modern gpus,cerebral blood flow,arterial input functions,affordable high performance computing,parallel computing environment,deconvolution,cerebral blood volume,diseases,parametric map,stroke,haemorheology,perfusion imaging,blood vessels,brain perfusion quantification,parallelization,deconvolution algorithm,graphics generation dedicated coprocessor,deconvolution operation,haemodynamic quantity,high performance computing,parallel algorithms,mean transit time,brain,brain tumor,gpgpu,parallel deconvolution algorithm,tumours,coprocessors,parallel implementation,parallel computing,cuda programming model,general purpose graphics processor units,patient diagnosis,medical image processing,haemodynamics,matrix decomposition,computed tomography | Perfusion scanning,Supercomputer,Parallel algorithm,Computer science,CUDA,Parallel computing,Algorithm,Deconvolution,General-purpose computing on graphics processing units,Coprocessor,Graphics processing unit | Conference |
ISBN | Citations | PageRank |
978-0-7695-4407-6 | 1 | 0.35 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhu | 1 | 6 | 2.29 |
| David Rodriguez Gonzalez | 2 | 11 | 2.14 |
| Trevor Carpenter | 3 | 5 | 1.71 |
| Malcolm P. Atkinson | 4 | 1991 | 1094.48 |
| Joanna Wardlaw | 5 | 40 | 3.67 |