Abstract
Introduction
Dynamic perfusion-CT (PCT) with deconvolution requires an arterial input function (AIF) for postprocessing. In clinical settings, the anterior cerebral artery (ACA) is often chosen for simplicity. The goals of this study were to determine how the AIF selection influences PCT results in acute stroke patients and whether the ACA is an appropriate default AIF.
Methods
We retrospectively identified consecutive patients suspected of hemispheric stroke of less than 48 h duration who were evaluated on admission by PCT. PCT datasets were postprocessed using multiple AIF, and cerebral blood volume (CBV) and flow (CBF), and mean transit time (MTT) values were measured in the corresponding territories. Results from corresponding territories in the same patients were compared using paired t-tests. The volumes of infarct core and tissue at risk obtained with different AIFs were compared to the final infarct volume.
Results
Of 113 patients who met the inclusion criteria, 55 with stroke were considered for analysis. The MTT values obtained with an “ischemic” AIF tended to be shorter (P=0.055) and the CBF values higher (P=0.108) than those obtained using a “nonischemic” AIF. CBV values were not influenced by the selection of the AIF. No statistically significant difference was observed between the size of the PCT infarct core (P=0.121) and tissue at risk (P=0.178), regardless of AIF selection.
Conclusion
In acute stroke patients, the selection of the AIF has no statistically significant impact of the PCT results; standardization of the PCT postprocessing using the ACA as the default AIF is adequate.
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References
Ladurner G, Zilkha E, Iliff D, du Boulay GH, Marshall J (1976) Measurement of regional cerebral blood volume by computerized axial tomography. J Neurol Neurosurg Psychiatry 39:152–158
Calamante F, Gadian DG, Connelly A (2000) Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med 44:466–473
Tan JC, Dillon WP, Liu S, Adler F, Smith WS, Wintermark M (2007) Systematic comparison of perfusion-CT and CT-angiography in acute stroke patients. Ann Neurol 61:533–543
Wintermark M, Maeder P, Thiran JP, Schnyder P, Meuli R (2001) Quantitative assessment of regional cerebral blood flows by perfusion CT studies at low injection rates: a critical review of the underlying theoretical models. Eur Radiol 11:1220–1230
Axel L (1983) Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique. Invest Radiol 18:94–99
Wintermark M, Flanders AE, Velthuis B, Meuli R, van Leeuwen M, Goldsher D, Pineda C, Serena J, van der Schaaf I, Waaijer A, Anderson J, Nesbit G, Gabriely I, Medina V, Quiles A, Pohlman S, Quist M, Schnyder P, Bogousslavsky J, Dillon WP, Pedraza S (2006) Perfusion-CT assessment of infarct core and penumbra: receiver operating characteristic curve analysis in 130 patients suspected of acute hemispheric stroke. Stroke 37:979–985
Lee T-Y (2002) Functional CT: physiological models. Trends Biotechnol 20:S3–S10
Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis. Magn Reson Med 36:715–725
Turk AS, Grayev A, Rowley HA, Field AS, Turski P, Pulfer K, Mukherjee R, Haughton V (2007) Variability of clinical CT perfusion measurements in patients with carotid stenosis. Neuroradiology 49:955–961
Ostergaard L, Johannsen P, Host-Poulsen P, Vestergaard-Poulsen P, Asboe H, Gee AD, Hansen SB, Cold GE, Gjedde A, Gyldensted C (1998) Cerebral blood flow measurements by magnetic resonance imaging bolus tracking: comparison with [(15)O]H2O positron emission tomography in humans. J Cereb Blood Flow Metab 18:935–940
Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50:164–174
Ibaraki M, Shimosegawa E, Toyoshima H, Takahashi K, Miura S, Kanno I (2005) Tracer delay correction of cerebral blood flow with dynamic susceptibility contrast-enhanced MRI. J Cereb Blood Flow Metab 25:378–390
Smith AM, Grandin CB, Duprez T, Mataigne F, Cosnard G (2000) Whole brain quantitative CBF and CBV measurements using MRI bolus tracking: comparison of methodologies. Magn Reson Med 43:559–564
Calamante F, Willats L, Gadian DG, Connelly A (2006) Bolus delay and dispersion in perfusion MRI: implications for tissue predictor models in stroke. Magn Reson Med 55:1180–1185
Ostergaard L, Chesler DA, Weisskoff RM, Sorensen AG, Rosen BR (1999) Modeling cerebral blood flow and flow heterogeneity from magnetic resonance residue data. J Cereb Blood Flow Metab 19:690–699
Calamante F, Yim PJ, Cebral JR (2003) Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics. Neuroimage 19:341–353
Lorenz C, Benner T, Chen PJ, Lopez CJ, Ay H, Zhu MW, Menezes NM, Aronen H, Karonen J, Liu Y, Nuutinen J, Sorensen AG (2006) Automated perfusion-weighted MRI using localized arterial input functions. J Magn Reson Imaging 24:1133–1139
Lorenz C, Benner T, Lopez CJ, Ay H, Zhu MW, Aronen H, Karonen J, Liu Y, Nuutinen J, Sorensen AG (2006) Effect of using local arterial input functions on cerebral blood flow estimation. J Magn Reson Imaging 24:57–65
Calamante F, Morup M, Hansen LK (2004) Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med 52:789–797
Knutsson L, Larsson EM, Thilmann O, Stahlberg F, Wirestam R (2006) Calculation of cerebral perfusion parameters using regional arterial input functions identified by factor analysis. J Magn Reson Imaging 23:444–453
Essig M, Lodemann KP, Le-Huu M, Bruning R, Kirchin M, Reith W (2006) Intraindividual comparison of gadobenate dimeglumine and gadobutrol for cerebral magnetic resonance perfusion imaging at 1.5 T. Invest Radiol 41:256–263
Sanelli PC, Lev MH, Eastwood JD, Gonzalez RG, Lee TY (2004) The effect of varying user-selected input parameters on quantitative values in CT perfusion maps. Acad Radiol 11:1085–1092
Bisdas S, Konstantinou GN, Gurung J, Lehnert T Donnerstag F, Becker H, Vogl TJ, Koh TS (2007) Effect of the arterial input function on the measured perfusion values and infarct volumetric in acute cerebral ischemia evaluated by perfusion computed tomography. Invest Radiol 42:147–156
Thijs VN, Somford DM, Bammer R, Robberecht W, Moseley ME, Albers GW (2004) Influence of arterial input function on hypoperfusion volumes measured with perfusion-weighted imaging. Stroke 35:94–98
Schellinger PD, Latour LL, Wu CS, Chalela JA, Warach S (2006) The association between neurological deficit in acute ischemic stroke and mean transit time: comparison of four different perfusion MRI algorithms. Neuroradiology 48:69–77
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Wintermark, M., Lau, B.C., Chien, J. et al. The anterior cerebral artery is an appropriate arterial input function for perfusion-CT processing in patients with acute stroke. Neuroradiology 50, 227–236 (2008). https://doi.org/10.1007/s00234-007-0336-8
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DOI: https://doi.org/10.1007/s00234-007-0336-8