Dynamic contrast-enhanced QSM for perfusion imaging: a systematic comparison of ΔR2*- and QSM-based contrast agent concentration time curves in blood and tissue

Abstract

Objective In 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 methods AIFs 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. Results AIFs 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. Discussion Interestingly, 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.

Perfusion Quantification in the Human Brain Using DSC MRI – Simulations and Validations at 3T

“…also reports a linear relationship and relaxivity of roughly 50 mM -1 s -1 at 3T, dependent on the specific Gd-chelate. The linear relationships were ruled out in our work – for Lind et al ., this value was determined in vivo and likely suffered high partial volume errors; the value 89 mM -1 s -1 is far more reflective of extravascular relaxivity (Lind et al ., 2020; Kjolby et al ., 2006). For Wilson et al ., the concentration of contrast agent was studied at levels beyond those typically observed (Wilson et al ., 2017).…”

“…One notable limitation in the signal modeling was the uncertainty of the maximum concentration of Gd for input into the simulations. Researchers have reported a vast array of values for this (Kellner et al , 2018; Lind et al , 2020; Patil et al ., 2013); therefore, we sought to balance two competing concerns: creating a concentration profile for Gd which results in tissue curves representative of those in our validation data; avoiding signal saturation, or hitting the noise floor, in arterial and venous voxels. A further limitation arises with respect to potential vasodilation from the hypoxic stimulus.…”

“…While it is difficult to attain the exact concentration profile of Gd in an artery (Kellner et al , 2018; Lind et al , 2020; Patil et al ., 2013), the shape parameters, b (time to peak) and c , can be set to 2.5 s and 3 (unitless) respectively, to roughly model the shape observed experimentally. a (the peak concentration) can be set to approximately 2 mM – see Discussion.…”

“…While quadratic modeling of the intravascular contrast relaxation rate is shown in numerous papers, some researchers have instead proposed a linear model to describe the intravascular relaxation rates (Lind et al ., 2020; Wilson et al ., 2017). The linear relationships were ruled out in our work (and later shown that the experimental results are not compatible with a linear relationship) – for Lind et al ., this value was determined in vivo and likely suffered high partial volume errors; the value 89 mM -1 s -1 is far more reflective of extravascular relaxivity (Lind et al ., 2020; Kjolby et al ., 2006). For Wilson et al ., the concentration of contrast agent was studied at levels beyond those typically observed (Wilson et al ., 2017).…”

Quantitative susceptibility mapping (QSM) of the cardiovascular system: challenges and perspectives

“…Although this method is technically challenging, owing to the difficulty of obtaining high fidelity B 0 maps and successfully performing the field-tosource reconstruction, QSM can provide key information on the biochemical constituents and microstructure of tissue. So far, QSM research has primarily focused on applications in neurology, particularly the identification of iron containing substances such as hemosiderin associated with hemorrhage, iron more generally, and paramagnetic contrast agents [1][2][3][4][5][6][7]. However, more recently, major advances have also been achieved in QSM outside the central nervous system, including the abdomen [8][9][10], neck [11] and cardiovascular system.…”