Perfusion using DSC-MRI
Amit Mehndiratta (Alumnus)
Bradley MacInthosh (Toronto)
Fernando Calamante (Melbourne)
Marco Casterllaro, Alessandra Bertoldo (Padova)
Dynamic Susceptibility Contrast (DSC) MRI relies upon imaging the passage of a Gadolinium based contrast agent to measure perfusion in the brain. Since the contrast agent cannot cross the blood brain barrier (excepting in some diseases) the dynamic signal elates to the transit of the agent through the vasculature. It is thus possible to arrive at a measure of blood supply to the tissue. This is often performed in a qualitative or semi-quantitaive manner, for example by measuring timing information about the contrast passage.
The basic contrast concentration model for DSC-MRI is the same for other perfusion modalities (e.g ASL MRI). It is common in DSC MRI to measure the arterial concentration of the agent from the images by examine the signal in larger arteries and then using this as the basis for perfusion measurement in the tissue. This involves the deconvolution of the arterial input function with the tissue concentration time series, something that is inherently poorly conditioned leading to noisy and erroneous solutions. We are investigating methods for better conditioned deconvolution using Bayesian inference approaches, our aim being not only to extract more accurate quantification of perfusion, but also to extract information about the micro vasculature. The output from deconvolution is a residue function that it is directly related to the distribution of transit times for blood through the region. BY examining this it should be possible to identifying regions that have been damaged or are under stress, for example in the case of ischaemia.
Software for analysis of DSC perfusion MRI that implemented an extended version of the Vascular Model is available as the Verbena tool as part of the FMRIB Software Library. The CPI method for model-free deconvolution analysis of DSC perfusion MRI is currently a MATLAB toolbox in use by collaborating groups.
A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI.
NeuroImage, 64, 560–570. doi:10.1016/j.neuroimage.2012.08.083