Cerebral blood flow estimation in vivo using local tissue reference functions†
Jayme Cameron Kosior BSc
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Seaman Family MR Research Centre, Foothills Medical Centre, Calgary Health Region, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Search for more papers by this authorMichael R. Smith PhD
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Department of Radiology, University of Calgary, Calgary, Alberta, Canada
Search for more papers by this authorRobert Karl Kosior BSc
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Seaman Family MR Research Centre, Foothills Medical Centre, Calgary Health Region, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Search for more papers by this authorCorresponding Author
Richard Frayne PhD
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Seaman Family MR Research Centre, Foothills Medical Centre, Calgary Health Region, Calgary, Alberta, Canada
Department of Radiology, University of Calgary, Calgary, Alberta, Canada
Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Department of Radiology, Seaman Family MR Research Centre, Foothills Medical Centre/University of Calgary, 1403, 29th Street, NW, Calgary, AB, Canada T2N 2T9Search for more papers by this authorJayme Cameron Kosior BSc
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Seaman Family MR Research Centre, Foothills Medical Centre, Calgary Health Region, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Search for more papers by this authorMichael R. Smith PhD
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Department of Radiology, University of Calgary, Calgary, Alberta, Canada
Search for more papers by this authorRobert Karl Kosior BSc
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Seaman Family MR Research Centre, Foothills Medical Centre, Calgary Health Region, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Search for more papers by this authorCorresponding Author
Richard Frayne PhD
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
Seaman Family MR Research Centre, Foothills Medical Centre, Calgary Health Region, Calgary, Alberta, Canada
Department of Radiology, University of Calgary, Calgary, Alberta, Canada
Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
Department of Radiology, Seaman Family MR Research Centre, Foothills Medical Centre/University of Calgary, 1403, 29th Street, NW, Calgary, AB, Canada T2N 2T9Search for more papers by this authorPresented in part at the 15th Annual Meeting of ISMRM, Berlin, Germany, 2007.
Abstract
Purpose
To evaluate the use of bolus signals obtained from tissue as reference functions (or local reference functions [LRFs]) rather than arterial input functions (AIFs) when deriving cross-calibrated cerebral blood flow (CBFCC) estimates via deconvolution.
Materials and Methods
AIF and white matter (WM) LRF CBFCC maps (cross-calibrated so that normal WM was 23.7 mL/minute/100 g) derived using singular value decomposition (SVD) were examined in 28 ischemic stroke patients. Median CBFCC estimates from normal gray matter (GM) and ischemic tissue were obtained.
Results
AIF and LRF median CBFCC estimates resembled one another for all 28 patients (average paired CBFCC difference 0.4 ± 1.7 mL/minute/100 g and –0.4 ± 1.4 mL/minute/100 g in GM and ischemic tissue, respectively). Wilcoxon signed-rank comparisons of patient median CBFCC measurements revealed no statistically significant differences between using AIFs and LRFs (P > 0.05).
Conclusion
If CBF is quantified using a patient-specific cross-calibration factor, then LRF CBF estimates are at least as accurate as those from AIFs. Therefore, until AIF quantification is achievable in vivo, perfusion protocols tailored for LRFs would simplify the methodology and provide more reliable perfusion information. J. Magn. Reson. Imaging 2009;29:183–188. © 2008 Wiley-Liss, Inc.
REFERENCES
- 1 Latchaw RE. Cerebral perfusion imaging in acute stroke. J Vasc Interv Radiol 2004; 15 ( Pt 2): S29–S46.
- 2 Adhya S, Johnson G, Herbert J, et al. Pattern of hemodynamic impairment in multiple sclerosis: dynamic susceptibility contrast perfusion MR imaging at 3.0 T. Neuroimage 2006; 33: 1029–1035.
- 3 Lorenz IH, Kolbitsch C, Hormann C, et al. The influence of nitrous oxide and remifentanil on cerebral hemodynamics in conscious human volunteers. Neuroimage 2002; 17: 1056–1064.
- 4 Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S. High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 2005; 60: 493–502.
- 5 Wirestam R, Ryding E, Lindgren A, et al. Regional cerebral blood flow distributions in normal volunteers: dynamic susceptibility contrast MRI compared with 99mTc-HMPAO SPECT. J Comput Assist Tomogr 2000; 24: 526–530.
- 6
Liu Y,
Karonen JO,
Vanninen RL, et al.
Cerebral hemodynamics in human acute ischemic stroke: a study with diffusion- and perfusion-weighted magnetic resonance imaging and SPECT.
J Cereb Blood Flow Metab
2000;
20:
910–920.
10.1097/00004647-200006000-00003 Google Scholar
- 7 Ibaraki M, Ito H, Shimosegawa E, et al. Cerebral vascular mean transit time in healthy humans: a comparative study with PET and dynamic susceptibility contrast-enhanced MRI. J Cereb Blood Flow Metab 2006; 27: 404–413.
- 8 Grandin CB, Bol A, Smith AM, Michel C, Cosnard G. Absolute CBF and CBV measurements by MRI bolus tracking before and after acetazolamide challenge: repeatability and comparison with PET in humans. Neuroimage 2005; 26: 525–535.
- 9 Mukherjee P, Kang HC, Videen TO, McKinstry RC, Powers WJ, Derdeyn CP. Measurement of cerebral blood flow in chronic carotid occlusive disease: comparison of dynamic susceptibility contrast perfusion MR imaging with positron emission tomography. AJNR Am J Neuroradiol 2003; 24: 862–871.
- 10 Conturo TE, Akbudak E, Kotys MS, et al. Arterial input functions for dynamic susceptibility contrast MRI: requirements and signal options. J Magn Reson Imaging 2005; 22: 697–703.
- 11 Sakaie KE, Shin W, Curtin KR, McCarthy RM, Cashen TA, Carroll TJ. Method for improving the accuracy of quantitative cerebral perfusion imaging. J Magn Reson Imaging 2005; 21: 512–519.
- 12 van Osch MJ, Rutgers DR, Vonken EP, et al. Quantitative cerebral perfusion MRI and CO2 reactivity measurements in patients with symptomatic internal carotid artery occlusion. Neuroimage 2002; 17: 469–478.
- 13
Vonken EJ,
van Osch MJ,
Bakker CJ,
Viergever MA.
Measurement of cerebral perfusion with dual-echo multi-slice quantitative dynamic susceptibility contrast MRI.
J Magn Reson Imaging
1999;
10:
109–117.
10.1002/(SICI)1522-2586(199908)10:2<109::AID-JMRI1>3.0.CO;2-# CAS PubMed Web of Science® Google Scholar
- 14 Østergaard L, Johannsen P, Host-Poulsen P, et al. 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 1998; 18: 935–940.
- 15 Arakawa S, Wright PM, Koga M, et al. Ischemic thresholds for gray and white matter: a diffusion and perfusion magnetic resonance study. Stroke 2006; 37: 1211–1216.
- 16 Østergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimental comparison and preliminary results. Magn Reson Med 1996; 36: 726–736.
- 17 Butcher KS, Parsons M, MacGregor L, et al. Refining the perfusion-diffusion mismatch hypothesis. Stroke 2005; 36: 1153–1159.
- 18 Ellinger R, Kremser C, Schocke MF, et al. The impact of peak saturation of the arterial input function on quantitative evaluation of dynamic susceptibility contrast-enhanced MR studies. J Comput Assist Tomogr 2000; 24: 942–948.
- 19 Thilmann O, Larsson EM, Björkman-Burtscher IM, Ståhlberg F, Wirestam R. Effects of echo time variation on perfusion assessment using dynamic susceptibility contrast MR imaging at 3 tesla. Magn Reson Imaging 2004; 22: 929–935.
- 20 van Osch MJ, Vonken EJ, Viergever MA, van der Grond J, Bakker CJ. Measuring the arterial input function with gradient echo sequences. Magn Reson Med 2003; 49: 1067–1076.
- 21 Calamante F, Gadian DG, Connelly A. Quantification of perfusion using bolus tracking magnetic resonance imaging in stroke: assumptions, limitations, and potential implications for clinical use. Stroke 2002; 33: 1146–1151.
- 22 Chen JJ, Smith MR, Frayne R. The impact of partial-volume effects in dynamic susceptibility contrast magnetic resonance perfusion imaging. J Magn Reson Imaging 2005; 22: 390–399.
- 23 Rempp KA, Brix G, Wenz F, Becker CR, Guckel F, Lorenz WJ. Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology 1994; 193: 637–641.
- 24 Axel L. Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis. Radiology 1980; 137: 679–686.
- 25 Grandin CB. Assessment of brain perfusion with MRI: methodology and application to acute stroke. Neuroradiology 2003; 45: 755–766.
- 26 Zierler KL. Equations for measuring blood flow by external monitoring of radioisotopes. Circ Res 1965; 16: 309–321.
- 27 Kosior JC, Frayne R. PerfTool: a software platform for investigating bolus-tracking perfusion imaging quantification strategies. J Magn Reson Imaging 2007; 25: 653–659.
- 28 Smith MR, Lu H, Trochet S, Frayne R. Removing the effect of SVD algorithmic artifacts present in quantitative MR perfusion studies. Magn Reson Med 2004; 51: 631–634.
- 29 Salluzzi M, Frayne R, Smith MR. An alternative viewpoint of the similarities and differences of SVD and FT deconvolution algorithms used for quantitative MR perfusion studies. Magn Reson Imaging 2005; 23: 481–492.
- 30 Albers GW, Thijs VN, Wechsler L, et al. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study. Ann Neurol 2006; 60: 508–517.
- 31 Lin W, Celik A, Derdeyn C, et al. Quantitative measurements of cerebral blood flow in patients with unilateral carotid artery occlusion: a PET and MR study. J Magn Reson Imaging 2001; 14: 659–667.
- 32 Li TQ, Haefelin TN, Chan B, et al. Assessment of hemodynamic response during focal neural activity in human using bolus tracking, arterial spin labeling and BOLD techniques. Neuroimage 2000; 12: 442–451.
- 33 Grandin CB, Duprez TP, Smith AM, et al. Which MR-derived perfusion parameters are the best predictors of infarct growth in hyperacute stroke. Comparative study between relative and quantitative measurements? Radiology 2002; 223: 361–370.
- 34 Hou BL, Bradbury M, Peck KK, Petrovich NM, Gutin PH, Holodny AI. Effect of brain tumor neovasculature defined by rCBV on BOLD fMRI activation volume in the primary motor cortex. Neuroimage 2006; 32: 489–497.
- 35 Butcher K, Parsons M, Baird T, et al. Perfusion thresholds in acute stroke thrombolysis. Stroke 2003; 34: 2159–2164.
- 36 Ko L, Salluzzi M, Frayne R, Smith M. Reexamining the quantification of perfusion MRI data in the presence of bolus dispersion. J Magn Reson Imaging 2007; 25: 639–643.
