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

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 <15%). 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%; P<0.01). 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.

[1]  B. Rosen,et al.  Dynamic imaging with lanthanide chelates in normal brain: Contrast due to magnetic susceptibility effects , 1988, Magnetic resonance in medicine.

[2]  Geoffrey A. Donnan,et al.  Acute Stroke Imaging Research Roadmap , 2008, Stroke.

[3]  R R Edelman,et al.  Clinical Outcome in Ischemic Stroke Predicted by Early Diffusion-Weighted and Perfusion Magnetic Resonance Imaging: A Preliminary Analysis , 1996, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  M. van Buchem,et al.  Optimal Location for Arterial Input Function Measurements near the Middle Cerebral Artery in First-Pass Perfusion MRI , 2009, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[5]  M. Viergever,et al.  Measuring the arterial input function with gradient echo sequences , 2003, Magnetic resonance in medicine.

[6]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis , 1996, Magnetic resonance in medicine.

[7]  E. Akbudak,et al.  Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options , 2005, Journal of magnetic resonance imaging : JMRI.

[8]  Gottfried Schlaug,et al.  Measurement of arterial input functions for dynamic susceptibility contrast magnetic resonance imaging using echoplanar images: Comparison of physical simulations with in vivo results , 2006, Magnetic resonance in medicine.

[9]  Leif Østergaard,et al.  Analysis of partial volume effects on arterial input functions using gradient echo: A simulation study , 2009, Magnetic resonance in medicine.

[10]  R. Frayne,et al.  Signal‐to‐noise ratio effects in quantitative cerebral perfusion using dynamic susceptibility contrast agents , 2003, Magnetic resonance in medicine.

[11]  S. Felber,et al.  The Impact of Peak Saturation of the Arterial Input Function on Quantitative Evaluation of Dynamic Susceptibility Contrast-Enhanced MR Studies , 2000, Journal of computer assisted tomography.

[12]  Christian M Kerskens,et al.  Evaluation of an AIF correction algorithm for dynamic susceptibility contrast‐enhanced perfusion MRI , 2008, Magnetic resonance in medicine.

[13]  Arno Villringer,et al.  Correcting saturation effects of the arterial input function in dynamic susceptibility contrast-enhanced MRI: a Monte Carlo simulation. , 2007, Magnetic resonance imaging.

[14]  Roland Bammer,et al.  Influence of Arterial Input Function on Hypoperfusion Volumes Measured With Perfusion-Weighted Imaging , 2003, Stroke.