Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI

Abstract

Correct arterial input function (AIF) measurements in dynamic susceptibility contrast-MRI are crucial for quantification of the hemodynamic parameters. Often a single global AIF is selected near a large brain-feeding artery. Alternatively, local AIF measurements aim for voxel-specific AIFs from smaller arteries. Because local AIFs are measured higher in the arterial-tree, it is assumed that these will reflect the true input of the microvasculature much better. However, do the measured local AIFs reflect the true concentration-time curves of small arteries? To answer this question, in vivo data were used to evaluate local AIF candidates selected based on two different types of angiograms. For interpretation purposes, a 3D numerical model that simulated partial-volume effects in local AIF measurements was created and the simulated local AIFs were compared to the ground truth. The findings are 2-fold. First, the in vivo data showed that the shape-characteristics of local AIFs are similar to the shape-characteristics of gray matter concentration-time curves. Second, these findings are supported by the simulations showing broadening of the measured local AIFs compared to the ground truth. These findings are suggesting that local AIF measurements do not necessarily reflect the true concentration-time curve in small arteries. Magn Reson Med 67:1324-1331,

Principles of T2*‐weighted dynamic susceptibility contrast MRI technique in brain tumor imaging

“…The local AIF methods may minimize the dispersion‐related errors affecting the DSC perfusion quantification process, but on the other hand they are particularly sensitive to partial volume effects . Several methods have been proposed to define a local AIF , but further efforts are still required to validate these approaches.…”

Correcting for large vessel contamination in dynamic susceptibility contrast perfusion MRI by extension to a physiological model of the vasculature

“…In reality, the signals from arteries show some angular dependence , indicative of a limitation of the simple model considered here. More extensive modeling along the lines of the work in might shed more light on this issue, as well as determining the level of accuracy the proposed correction method can be expected to achieve. This is, however, beyond the scope of this study.…”

“…The correction method proposed here relies upon the accurate identification of the AIF, which is widely recognized challenge , particularly due to partial volume effects. Recently methods have been proposed that exploit both magnitude and/or phase images to provide an improved estimate of the AIF. The proposed MV correction method will directly benefit from any such method, because a more accurate AIF should lead to a more accurate MV correction within the proposed Bayesian framework.…”

“…In principle, the contribution of the MV component to the signal should match that of the AIF, ignoring dispersive effects and complex partial volume effects ; thus, it should be possible to use the existing information about the measured AIF to assist in MV correction. In this work, we propose a model‐based solution to the correction for MV contamination inspired by related recent work in Arterial Spin Labeling , building upon work previously presented in abstract form .…”

Blood–Brain Barrier Imaging in Human Neuropathologies

“…This assumption is valid only when the temporal resolution is relatively low and when the entire brain is equally and properly perfused. Taking into account regional variations in blood flow, several studies suggested methods to define and use local AIF (54–58). This approach, although not currently accepted as the default method, should be further validated.…”