Abstract
ObjectiveIn dynamic susceptibility contrast MRI (DSC-MRI), an arterial input function (AIF) is required to quantify perfusion. However, estimation of the concentration of contrast agent (CA) from magnitude MRI signal data is challenging. A reasonable alternative would be to quantify CA concentration using quantitative susceptibility mapping (QSM), as the CA alters the magnetic susceptibility in proportion to its concentration.Material and methodsAIFs with reasonable appearance, selected on the basis of conventional criteria related to timing, shape, and peak concentration, were registered from both ΔR2* and QSM images and mutually compared by visual inspection. Both ΔR2*- and QSM-based AIFs were used for perfusion calculations based on tissue concentration data from ΔR2*as well as QSM images.ResultsAIFs based on ΔR2* and QSM data showed very similar shapes and the estimated cerebral blood flow values and mean transit times were similar. Analysis of corresponding ΔR2* versus QSM-based concentration estimates yielded a transverse relaxivity estimate of 89 s−1 mM−1, for voxels identified as useful AIF candidate in ΔR2* images according to the conventional criteria.DiscussionInterestingly, arterial concentration time curves based on ΔR2* versus QSM data, for a standard DSC-MRI experiment, were generally very similar in shape, and the relaxivity obtained in voxels representing blood was similar to tissue relaxivity obtained in previous studies.
Highlights
Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is a common MRI technique for assessment of brain perfusion and perfusion-related parameters
The most common approach in DSC-MRI is to use arterial input function (AIF) based on ΔR2* data
Since numerous drawbacks are associated with this approach, it was regarded to be of value to investigate the potential use of AIFs based on quantitative susceptibility mapping (QSM) data
Summary
Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is a common MRI technique for assessment of brain perfusion and perfusion-related parameters. Several of the methodological complications associated with quantitative DSC-MRI derive from the difficulties in estimating a reliable AIF owing to, for example, the non-linear relationship between CA concentration and ΔR2* and different relaxivities in blood and brain tissue, as mentioned above These relaxivity-related issues have, in most cases, been investigated under the assumption that AIF measurements are made in pure blood, which, in practice, is not normally the case owing to, for example, limited spatial resolution implying that a voxel assumed to represent blood is likely to contain a partial volume of other brain tissue types [5, 6]. Signal saturation (signal clipping) and signal pixel shifts (at low bandwidths) [7] of blood signal are likely to occur at high CA concentrations, and these effects tend to deteriorate the shape of the arterial concentration time curve Such voxels will typically be excluded from the selected AIF. PVEs can affect the magnitude signal in several ways, large PVEs tend, in general, to imply overestimated absolute values of CBF and CBV [1, 3]
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