Magnetic resonance brain perfusion imaging with voxel-specific arterial input functions

Journal of Magnetic Resonance Imaging

Volumn 23, Issue 3, 2006, Pages 273-284

  Grüner, Renate ^a,b   Bjørnara, Bård T ^b   Moen, Gunnar ^b   Taxt, Torfinn ^a,b  

^a UNIVERSITY OF BERGEN   (Norway)

^b HAUKELAND UNIVERSITY HOSPITAL   (Norway)

Author keywords

Blind deconvolution; Dynamic contrast MRI; Local arterial input function; Perfusion weighted imaging; Voxel specific hemodynamic parameters

Indexed keywords

ADULT; ARTERY BLOOD FLOW; ARTERY PERFUSION; ARTICLE; BRAIN ARTERY; BRAIN BLOOD FLOW; BRAIN MAPPING; BRAIN PERFUSION; COMPUTER SIMULATION; CONTRAST ENHANCEMENT; FLOW KINETICS; GRAY MATTER; HEMODYNAMICS; HUMAN; HUMAN EXPERIMENT; MEASUREMENT; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL;

ADULT; ALGORITHMS; CEREBROVASCULAR CIRCULATION; COMPUTER SIMULATION; CONTRAST MEDIA; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MONTE CARLO METHOD; NERVE FIBERS, MYELINATED; NERVE FIBERS, UNMYELINATED; STATISTICS, NONPARAMETRIC;

EID: 33644835329     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.20505     Document Type: Article

References (40)

1. 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.
2. Shin JH, Lee HK, Kwun BD, et al. Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol 2002;179:783-789.
3. Wong JC, Provenzale JM, Petrella JR. Perfusion MR imaging of brain neoplasms. AJR Am J Roentgenol 2000;174:1147-1157.
4. Østergaard L, Smith DF, Vestergaard-Poulsen P, et al. Absolute cerebral blood flow and blood volume measured by magnetic resonance bolus tracking: comparison with positron emission tomography values. J Cereb Blood Flow Metab 1998;18:425-432.
5. Emmanuelle CP, Marc JF, Didier R. Magnetic resonance perfusion imaging in ischemic heart disease. J Magn Reson Imaging 1999;10:423-433.
6. Sorensen AG, Copen WA, Ostergaard L, et al. Hyperacute stroke: simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time. Radiology 1999;210:519-527.
7. Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D. Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 2002;223:11-29.
8. National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med 1995;333:1581-1587.
9. Chiu D, Krieger D, Villar-Cordova C, et al. Intravenous tissue plasminogen activator for acute ischemic stroke: feasibility, safety, and efficacy in the first year of clinical practice. Stroke 1998;29:18-22.
10. Hacke W, Kaste M, Fieschi C, et al. Intravenous thrombolysis with recombinant tissue plasminogen activator for acute hemispheric stroke. The European Cooperative Acute Stroke Study (ECASS). JAMA 1995;274:1017-1025.
11. Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume maps and gliomas: comparison with tumor grade and histologic findings. Radiology 1994;191:41-51.
12. Grieble J, Mayr N, de Vries A, et al. Assessment of tumor microcirculation; a new role of dynamic contrast MRI imaging. J Magn Reson Imaging 1997;7:111-119.
13. Rempp KA, Brix G, Wenz F, Becker CR, Gückel F, Lorenz WJ. Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology 1994;193,637-641.
14. Sorensen AG, Reimer P. Cerebral MR perfusion imaging: principles and current applications. New York: Georg Thieme Verlag; 2000. 162 p.
15. Barbier EL, Lamalle L, Decorps M. Methodology of brain perfusion imaging. J Magn Reson Imaging 2001;13:496-520.
16. Hagen T, Bartylla K, Piepgras U. Correction of regional cerebral blood flow measured by stable xenon CT and perfusion MRI. J Comput Assist Tomogr 1999;23:257-264.
17. Kikuchi K, Murase K, Miki H, et al. Quantitative evaluation of mean transit time obtained with dynamic susceptibility contrast-enhanced MR imaging and with 133Xe SPECT in occlusive cerebrovascular disease. AJR Am J Roentgenol 2002;179:229-235.
18. Smith AM, Grandin CB, Duprez T, Mataigne F, Cosnard G. Whole brain quantitative CBF and CBV measurements using MRI bolus tracking: comparison of methodologies. Magn Reson Med 2000;43:559-564.
19. Wirestam R, Ryding E, Lindgren A, Geijer B, Holtås S, Stålberg F. Absolute cerebral blood flow measured by dynamic susceptibility contrast MRI: a direct comparison with Xe-133 SPECT. MAGMA 2000;11:96-103.
20. Østergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part 1: Mathematical approach and statistical analysis. Magn Reson Med 1996;36:715-725.
21. Andersen IK, Szymkowiak A, Rasmussen CE, et al. Perfusion quantification using Gaussian process deconvolution. Magn Reson Med 2002;48:351-361.
22. Vonken EPA, Beekman FJ, Bakker CJG, Viergever MA. Maximum likelihood estimation of cerebral blood flow in dynamic susceptibility contrast MRI. Magn Reson Med 1999;41:343-350.
23. Wirestam R, Andersson L, Østergaard L, et al. Assessment of regional cerebral blood flow by dynamic susceptibility contrast MRI using different deconvolution techniques. Magn Reson Med 2000;43:691-700.
24. Carroll TJ, Rowley HA, Haughton VM. Automatic calculation of the arterial input function for cerebral perfusion imaging with MR imaging. Radiology 2003;227:593-600.
25. Calamante F, Moerup M, Hansen KL. Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med 2004;52:789-797.
26. Oppenheim AV, Schafer RW. Discrete-time signal processing. Englewood Cliffs, NJ: Prentice-Hall Inc.; 1989. p 768-845.
27. Quatieri TE. Discrete-time speech signal processing: principles and practice. Upper Saddle River, NJ: Prentice Hall PTR, NJ 07458; 2002. p 253-309.
28. Tribolet JM. Seismic applications of homomorphic signal processing. Englewood Cliffs, NJ: Prentice-Hall; 1979. 195 p.
29. Ulrych TJ. Applications of homomorphic deconvolution to seismology. Geophysics 1971;36:650-660.
30. Taxt T. Restoration of medical ultrasound images using two-dimensional homomorphic deconvolution. IEEE Trans Ultrason Ferroelectr Freq Contr 1995;42:543-554.
31. Taxt T. Comparison of cepstrum-based methods for radial blind deconvolution of ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Contr 1997;44:666-674.
32. Ives JT, Gesteland RF, Stockham TG. An automated film reader for DNA sequencing based on homomorphic deconvolution. IEEE Trans Biomed Eng 1994;41:509-519.
33. Zierler KL. Circulation times and the theory of indicator-dilution methods for determining blood flow and volume. In: Handbook of physiology, section 2: circulation. Washington, DC: American Physiological Society; 1962. p 585-615.
34. Calamante F, Gadian DG, Connelly A. Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med 2000;44:466-473.
35. Nikias CL, Petropulu AP. Higher-order spectra analysis. Englewood Cliffs, NJ: Prentice Hall PTR; 1993. 528 p.
36. Lim JS. Spectral root homomorphic deconvolution system. IEEE Trans Acoust Speech Signal Process 1979;27:223-233.
37. Kobayashi T, Imai S. Spectral analysis using generalized cepstrum. IEEE Trans Acoust 1984;32:1087-1089.
38. Polkinghorne PJ, Sehmi K, Cross MR, Minassain D, Bird AC. Lancet 1988;2:1381-1383.
39. Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical recipes in C. 2nd ed. Cambridge: Cambridge University Press; 1992.
40. Hollander M, Wolfe DA. Nonparametric statistical methods. 2nd ed. New York: Wiley Interscience; 1999. 787 p.