Abstract
Bolus-tracking perfusion measurements in patients with vascular abnormalities are often unreliable, because delay and/or dispersion of the bolus within the vessels distorts the measured arterial input function (AIF). Erroneous measurements of perfusion can be identified by examining the measured response function, the shape of which is determined by both the tissue and arterial retention. In this work, an accurate response function is extracted by combining maximum-likelihood expectation-maximisation deconvolution, regularised using an oscillation index, with subsequent wavelet thresholding. Simulations show that this method recovers both the smooth-dispersed and the sharp-delayed response functions. This enables regions where the bolus is delayed and/or dispersed to be identified when the methodology is applied to data from patients with vascular abnormalities. Simulations also demonstrate robust and accurate perfusion estimates when there is no bolus delay and/or dispersion. The presence of delay and/or dispersion in the response function suggests that the perfusion measurements are erroneous, and that the global AIF is an inaccurate approximation to the true AIF in these regions. Perfusion measurements are corrected within the affected regions by defining a regional AIF from the independent component analysis of the dynamic susceptibility contrast MRI data. The regional AIF is shown to remove the delay and dispersion, improving the accuracy of the perfusion maps.
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