document: Perfusion quantification using Gaussian process deconvolution The quantification of perfusion using dynamic susceptibility contrast MRI -LRB- DSC-MRI -RRB- requires deconvolution to obtain the residual impulse response function -LRB- IRF -RRB- . In this work , a method using the Gaussian process for deconvolution -LRB- GPD -RRB- 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 -LRB- SVD -RRB- 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 -LRB- SNR -RRB- 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
doc_bio_tags: B I O B I I O O O O O B I I I O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: perfusion quantification gaussian process deconvolution dynamic susceptibility contrast mri residual impulse response function singular value decomposition noise level
abstractive_keyphrases: optimized model parameters capillary blood flow mean transit time optimized joint gaussian distribution correlation length likelihood function