Perfusion quantification using Gaussian process deconvolution

TY - JOUR

T1 - Perfusion quantification using Gaussian process deconvolution

AU - Andersen, Irene Klærke

AU - Szymkowiak, A

AU - Hanson, Lars G.

AU - Marstrand, J R

AU - Larsson, H B W

AU - Hansen, L K

N1 - Copyright 2002 Wiley-Liss, Inc.

PY - 2002/8

Y1 - 2002/8

N2 - 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.

AB - 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.

KW - Cerebrovascular Circulation

KW - Contrast Media

KW - Gadolinium DTPA

KW - Humans

KW - Image Processing, Computer-Assisted

KW - Magnetic Resonance Imaging/methods

KW - Normal Distribution

U2 - 10.1002/mrm.10213

DO - 10.1002/mrm.10213

M3 - Journal article

C2 - 12210944

SN - 0740-3194

VL - 48

SP - 351

EP - 361

JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

IS - 2

ER -