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Author Andersen, I. K. ♦ Szymkowiak, A. ♦ Rasmussen, C. E. ♦ Hanson, L. G. ♦ Marstr, J. R. ♦ Larsson, H. B. W. ♦ Hansen, L. K.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Perfusion Quantification Using Gaussian Process ♦ Noise Level ♦ Common Threshold ♦ Signal-to-noise Ratio ♦ Time Resolution ♦ Gaussian Process ♦ Artificial Data ♦ Model Parameter ♦ Value Decomposition ♦ Dynamic Susceptibility Contrast Mri ♦ Large Distribution Volume ♦ Healthy Volunteer ♦ Singular Value ♦ Entire Irf ♦ Residual Impulse Response Function ♦ Gpd Method
Abstract The quantification of perfusion using dynamic susceptibility contrast MRI (DSC-MRI) requires deconvolution to obtain the residual impulse response function (IRF). In this work, a method using the Gaussian process for deconvolution (GPD) is proposed. The fact that the IRF is smooth is incorporated as a constraint in the method. The GPD method, which automatically estimates the noise level in each voxel, has the advantage that model parameters are optimized automatically. The GPD is compared to singular value decomposition (SVD) using a common threshold for the singular values, and to SVD using a threshold optimized according to the noise level in each voxel. The comparison is carried out using artificial data as well as data from healthy volunteers. It is shown that GPD is comparable to SVD with a variable optimized threshold when determining the maximum of the IRF, which is directly related to the perfusion. GPD provides a better estimate of the entire IRF. As the signal-to-noise ratio (SNR) increases or the time resolution of the measurements increases, GPD is shown to be superior to SVD. This is also found for large distribution volumes. Magn
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study