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The perfusion of the brain is essential to maintain brain function. Stroke is an example of a decrease in blood
flow and reduced perfusion. During ischemic stroke the blood flow to tissue is hampered due to a clot inside
a vessel. To investigate the recovery of stroke patients, follow up studies are necessary. MRI is the preferred
imaging modality for follow up because of the absence of radiation dose concerns, contrary to CT. Dynamic
Susceptibility Contrast (DSC) MRI is an imaging technique used for measuring perfusion of the brain, however,
is not standard applied in the clinical routine due to lack of immediate patient benefit. Several post processing
algorithms are described in the literature to obtain cerebral blood flow (CBF). The quantification of CBF relies
on the deconvolution of a tracer concentration-time curve in an arterial and a tissue voxel. There are several
methods to obtain this deconvolution based on singular-value decomposition (SVD). This contribution describes
a comparison between the different approaches as currently there is no best practice for (all) clinical relevant
situations. We investigate the influence of tracer delay, dispersion and recirculation on the performance of the
methods. In the presence of negative delays, the truncated SVD approach overestimates the CBF. Block-circulant
and reformulated SVD are delay-independent. Due to its delay dependent behavior, the truncated SVD approach
performs worse in the presence of dispersion as well. However all SVD approaches are dependent on the amount
of dispersion. Moreover, we observe that the optimal truncation parameter varies when recirculation is added to
noisy data, suggesting that, in practice, these methods are not immune to tracer recirculation. Finally, applying
the methods to clinical data resulted in a large variability of the CBF estimates. Block-circulant SVD will work
in all situations and is the method with the highest potential.
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