2 research outputs found
Parallel perfusion imaging processing using GPGPU
AbstractBackground and purposeThe objective of brain perfusion quantification is to generate parametric maps of relevant hemodynamic quantities such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) that can be used in diagnosis of acute stroke. These calculations involve deconvolution operations that can be very computationally expensive when using local Arterial Input Functions (AIF). As time is vitally important in the case of acute stroke, reducing the analysis time will reduce the number of brain cells damaged and increase the potential for recovery.MethodsGPUs originated as graphics generation dedicated co-processors, but 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 large number of computing cores and constitutes an affordable high performance computing method. 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. We present the serial and parallel implementations of such algorithms and the evaluation of the performance gains using GPUs.ResultsOur method has gained a 5.56 and 3.75 speedup for CT and MR images respectively.ConclusionsIt seems that using GPGPU is a desirable approach in perfusion imaging analysis, which does not harm the quality of cerebral hemodynamic maps but delivers results faster than the traditional computation
Brain perfusion imaging : performance and accuracy
Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. The purpose of
my PhD research is to develop novel methodologies for improving the efficiency and
quality of brain perfusion-imaging analysis so that clinical decisions can be made more
accurately and in a shorter time. This thesis consists of three parts:
My research investigates the possibility that parallel computing brings to make
perfusion-imaging analysis faster in order to deliver results that are used in stroke
diagnosis earlier. Brain perfusion analysis using local Arterial Input Functions
(AIF) techniques takes a long time to execute due to its heavy computational
load. As time is vitally important in the case of acute stroke, reducing analysis
time and therefore diagnosis time can reduce the number of brain cells damaged
and improve the chances for patient recovery. We present the implementation
of a deconvolution algorithm for brain perfusion quantification on GPGPU
(General Purpose computing on Graphics Processing Units) using the CUDA
programming model. Our method aims to accelerate the process without any
quality loss. Specific features of perfusion source images are also used to reduce noise impact,
which consequently improves the accuracy of hemodynamic maps. The
majority of existing approaches for denoising CT images are optimized for 3D
(spatial) information, including spatial decimation (spatially weighted mean filters)
and techniques based on wavelet and curvelet transforms. However, perfusion
imaging data is 4D as it also contains temporal information. Our approach
using Gaussian process regression (GPR) makes use of the temporal information
in the perfusion source imges to reduce the noise level. Over the entire
image, our noise reduction method based on Gaussian process regression gains a
99% contrast-to-noise ratio improvement over the raw image and also improves
the quality of hemodynamic maps, allowing a better identification of edges and
detailed information. At the level of individual voxels, GPR provides a stable
baseline, helps identify key parameters from tissue time-concentration curves
and reduces the oscillations in the curves. Furthermore, the results show that
GPR is superior to the alternative techniques compared in this study. My research also explores automatic segmentation of perfusion images into potentially healthy areas and lesion areas, which can be used as additional information
that assists in clinical diagnosis. Since perfusion source images contain
more information than hemodynamic maps, good utilisation of source images
leads to better understanding than the hemodynamic maps alone. Correlation
coefficient tests are used to measure the similarities between the expected tissue
time-concentration curves (from reference tissue) and the measured time-concentration
curves (from target tissue). This information is then used to distinguish
tissues at risk and dead tissues from healthy tissues. A correlation coefficient
based signal analysis method that directly spots suspected lesion areas
from perfusion source images is presented. Our method delivers a clear automatic
segmentation of healthy tissue, tissue at risk and dead tissue. From our
segmentation maps, it is easier to identify lesion boundaries than using traditional
hemodynamic maps