Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

Abstract

Cerebral perfusion imaging using dynamic susceptibility contrast (DSC) has been the subject of considerable research and shows promise for basic science and clinical use. In DSC, the MRI signals in brain tissue and feeding arteries are monitored dynamically in response to a bolus injection of paramagnetic agents, such as gadolinium (Gd) chelates. DSC has the potential to allow quantitative imaging of parameters such as cerebral blood flow (CBF) with a high signal‐to‐noise ratio (SNR) in a short scan time; however, quantitation depends critically on accurate and precise measurement of the arterial input function (AIF). We discuss many requirements and factors that make it difficult to measure the AIF. The AIF signal should be linear with respect to Gd concentration, convertible to the same concentration scale as the tissue signal, and independent of hematocrit. Complicated relationships between signal and concentration can violate these requirements. The additional requirements of a high SNR and high spatial/temporal resolution are technically challenging. AIF measurements can also be affected by signal saturation and aliasing, as well as dispersion/delay between the AIF sampling site and the tissue. We present new in vivo preliminary results for magnitude‐based (ΔR2*) and phase‐based (Δϕ) AIF measurements that show a linearity advantage of phase, and a disparity in the scaling of Δϕ AIFs, ΔR2* AIFs, and ΔR2* tissue curves. Finally, we discuss issues related to the choice of AIF signal for quantitative perfusion imaging. J. Magn. Reson. Imaging 2005. © 2005 Wiley‐Liss, Inc.

Comparison of errors associated with single‐ and multi‐bolus injection protocols in low‐temporal‐resolution dynamic contrast‐enhanced tracer kinetic analysis

“…The SNR of the experiment is critical for the AIF calculation, with higher SNR increasing the precision of the calculation (30). Our experiments show that AIF 1 is on the whole less vulnerable to low SNR than AIF 2 when no mis-sampling of the data occurs, particularly for v p .…”

Perfusion CT Imaging of Brain Tumors: An Overview

“…In the latter case, when the contrast agent remains intravascular, the method is widely accepted as a relative estimate of CBF and CBV, though there is a possibility for artifacts because of difficulties in assessing the shape and timing of the arterial input function. 42 In the event that substantial leakage of a contrast agent from intra-to extravascular space takes place, a strong and competing T1 contrast effect is often noticed in the areas of pathology because of the necessity of the short (ϳ1 second) TR needed to estimate CBF. As a first-order tactic to minimize the competing T1 contrast, preloading with contrast agent has been proposed with some success.…”

Cerebral blood flow estimation in vivo using local tissue reference functions

“…AIF signal quality for a given subject cannot be validated in vivo (e.g., PVE), and so the selection criteria are often vague and qualitative. Contrary to the definition of the AIF (i.e., on the artery) it is often the case that signals adjacent to arteries (off-vessel AIFs) are selected as AIFs (10,16). AIF voxels practically always have PVE with surrounding tissue given the size and orientation of common arteries used for the AIF (e.g., MCA ϳ3 mm) relative to common voxel sizes (i.e., ϳ2 ϫ 2 ϫ 5 mm), and the PVE can be increased further by spatial noise filtering methods and AIF signal averaging.…”

“…However, acquisition limitations in the measurement of the tracer concentration from arterial blood (called the arterial input function [AIF]) is a cause of large errors in absolute DSC-MR CBF estimates derived by deconvolution (10). If left uncorrected, these AIF CBF errors generally cause large, physiologically unreasonable CBF estimates relative to gold standard techniques (e.g., positron emission tomography [PET]) (3,7,9,(11)(12)(13).…”

“…Given the known technical errors associated with measurement of AIFs (10,18 -22), LRFs have considerable theoretical advantages over the standard AIF approach when using a patient-specific CBF calibration factor (e.g., the CCR method). The dependence between the measured ⌬R 2 * and tracer concentration is more linear for tissue signals as compared to arterial signals (10). Additionally, LRFs are less likely to suffer from AIF partial volume effect (PVE) CBF errors that occur in vivo when arterial signals are averaged with surrounding tissue influenced by complex field inhomogeneities (20).…”