Reliable Perfusion Maps in Stroke MRI Using Arterial Input Functions Derived From Distal Middle Cerebral Artery Branches

Abstract

Background and Purpose-Perfusion imaging is widely used in stroke, but there are uncertainties with regard to the choice of arterial input function (AIF). Two important aspects of AIFs are signal-to-noise ratio and bolus-related signal drop, ideally close to 63%. We hypothesized that distal branches of the middle cerebral artery (MCA) provide higher quality of AIF compared with proximal branches. Methods-Over a period of 3 months, consecutive patients with suspected stroke were examined in a 3-T MRI scanner within 24 hours of symptom onset. AIFs were selected manually in M1, M2, and M3 branches of the MCA contralateral to the suspected ischemia. Signal-to-noise ratio and bolus-related signal drop were analyzed. Perfusion maps were created for every patient and mean values at the insular level as well as relative ranges were compared. Results-Mean age of 132 included patients (53 females) was 67.3 years (SD, 14.9) and median National Institutes of Health Stroke Scale was 3 (interquartile range [IQR] 0 to 6). For further analyses, 4 patients were excluded due to discontinuation of scanning or insufficient bolus arrival (signal drop Ͻ15%). Median signal-to-noise ratio was highest in M3 branches (36.41; IQR, 29.29 to 43.58). Median signal-to-noise ratio in M2 branches was intermediate (27.54; IQR, 20.78 to 34.00) and median signal-to-noise ratio in M1 was low (12.40; IQR, 9.11 to 17.15). Using AIFs derived from M1 and M2 branches of the MCA median signal drop was 77% (IQR, 72% to 82%) and 78% (IQR, 73% to 83%), respectively. Signal drop was significantly reduced when AIF was selected in M3 branches with a median of 72% (IQR, 63% to 77%; PϽ0.01). Highest variability of 3456 perfusion maps was found in those derived from M1. Conclusion-The level of AIF selection in the MCA has a major impact on reliability and even quantitative parameters of perfusion maps. For better comparison of perfusion maps, the AIF should be defined by selection of distal branches of the MCA contralateral to the suspected ischemia. In future trials involving perfusion imaging, the MCA segment used for the AIF should be specified. (Stroke. 2010;41:95-101.)

Comparison of 10 TTP and Tmax Estimation Techniques for MR Perfusion-Diffusion Mismatch Quantification in Acute Stroke

“…Among others, these parameters comprise the injection protocol (injection volume and injection rate), MR imaging parameters, usage and techniques for preprocessing including deconvolution, perfusion parameter selection, arterial input function localization, image registration accuracy, and finally the definition of thresholds to define critical hypoperfusion. 11,14,15,21 Although several studies have evaluated the impact of 1 or more of these different parameters directly or indirectly, the influence of the TTP/Tmax estimation technique has not gained much attention. So far, only 1 study has been conducted comparing direct TTP and deconvolution-based Tmax estimation.…”

“…4,12,13 Several factors may influence the PWI analysis, such as the use and localization of the arterial input function. 14,15 Apart from this, there is another important aspect of PWI analysis that has not attracted much attention; that is the method for computation of TTP or Tmax parameter maps. Consequently, the method selection may influence the quantitative perfusion lesion definition and subsequent treatment decisions.…”

Relationship Between Changes in the Temporal Dynamics of the Blood-Oxygen-Level-Dependent Signal and Hypoperfusion in Acute Ischemic Stroke

“…The arterial input function was selected by manually identifying 5 to 10 voxels in the distal branches of the middle cerebral artery contralateral to the acute infarction. 14,15 Time to maximum (Tmax), mean transit time (MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV) maps were generated after block-circulant singular value decomposition deconvolution of the concentration-time curve. 16 Processing of the rsfMRI data was performed according to Lv et al 7 The first 3 volumes were discarded, and the data were corrected for slice timing effects and motion.…”

Vessel Size Imaging Reveals Pathological Changes of Microvessel Density and Size in Acute Ischemia

“…In this study, arterial input function was selected manually in the M3 segment at the top of the ventricle contralateral to the suspected ischemia to yield a relatively high signal-to-noise ratio (SNR; Ebinger et al, 2010). The deconvolution of R(t) from equation (5) was accomplished in Fourier domain by applying the Tikhonov regularization with a control parameter optimized according to the voxelwise baseline SNR as l = 10 3.7 /SNR 2.7 by the following equation:…”