Deconvolution for DCE-MRI using an exponential approximation basis

Medical Image Analysis

Volumn 13, Issue 1, 2009, Pages 80-90

  Keeling, Stephen L ^a   Kogler, Thomas ^b   Stollberger, Rudolf ^c  

^a UNIVERSITY OF GRAZ   (Austria)

^b UNIV KLIN FÜR RADIODIAGNOSTIK   (Austria)

^c GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Deconvolution; Dynamic contrast enhanced magnetic resonance imaging; Exponential approximation basis; Monotonicity; Total variation

Indexed keywords

ATOMS; CONVOLUTION; DISTRIBUTION FUNCTIONS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; PHYSIOLOGICAL MODELS; POLYNOMIAL APPROXIMATION; PROBABILITY DENSITY FUNCTION; RESONANCE;

BASIS FUNCTIONS; CEREBRAL TUMORS; DECONVOLUTION APPROACHES; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; EXPONENTIAL APPROXIMATION BASIS; EXPONENTIAL APPROXIMATIONS; EXPONENTIAL FUNCTIONS; EXPONENTIALS; KERNEL PARAMETERS; MONOTONICITY; PHYSIOLOGICAL PARAMETERS; TOTAL VARIATION; TOTAL VARIATIONS; TRUNCATED SINGULAR VALUE DECOMPOSITIONS;

SINGULAR VALUE DECOMPOSITION;

GADOLINIUM PENTETATE;

ARTICLE; BRAIN BLOOD FLOW; BRAIN SIZE; BRAIN TUMOR; CONTRAST ENHANCEMENT; CONTROLLED STUDY; IMAGE ANALYSIS; IMAGE PROCESSING; KERNEL METHOD; MATHEMATICAL ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; OSCILLATION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; VOLUMETRY;

ALGORITHMS; BRAIN NEOPLASMS; CONTRAST MEDIA; DIFFUSION MAGNETIC RESONANCE IMAGING; GADOLINIUM DTPA; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 57049116728     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2008.06.011     Document Type: Article

References (23)

1. Ash R.B. Real Analysis and Probability (1972), Academic Press, New York
2. Calamante F., Gadian D.G., and Connelly A. Quantification of bolus tracking MRI: improved characterization of the tissue residue function using Tikhonov regularisation. Magn. Reson. Med. 50 6 (2003) 1237-1247
3. Cunningham V.J., and Jones T. Spectral analysis of dynamic PET studies. J. Cereb. Blood Flow Metab. 13 1 (1993) 15-23
4. Engl H.W., Hanke M., and Neubauer A. Regularization of Inverse Problems (1996), Kluwer, Dordrecht
5. Gill P.E., Murray W., and Wright M.H. Practical Optimization (1981), Academic Press, London, UK
6. Greensite F. The temporal prior in bioelectromagnetic source imaging problems. IEEE Trans. Biomed. Eng. 50 10 (2003) 1152-1159
7. Griebel J., Pahernik S., Lucht R., DeVries A., Englmeier K.-H., Dellian M., and Brix G. Perfusion and permeability: can both parameters be evaluated separately from dynamic MR data?. Proc. Intl. Soc. Mag. Reson. Med. 9 (2001)
8. Henderson E., Sykes J.S., Drost D., Weinmann H.-J., Rutt B.K., and Lee T.-Y. Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents. J. Magn. Reson. Imag. 12 (2000) 991-1003
9. Keeling S.L. Total variation based convex filters for medical imaging. Appl. Math. Comp. 139 (2003) 101-119
10. Keeling, S.L., Bammer, R., Kogler, T., Stollberger, R., 2004. On the convolution model of dynamic contrast enhanced magnetic resonance imaging and non-parametric deconvolution approaches. Special Research Center Report 298, University of Graz, Austria.
11. Keeling S.L., Bammer R., and Stollberger R. Revision of the theory of tracer transport and the convolution model of dynamic contrast enhanced magnetic resonance imaging. J. Math. Biol. 55 (2007) 389-411
12. Kety S.S. The theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol. Rev. 3 (1951) 1-41
13. St. Lawrence K.S., and Lee T.Y. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain. I. Theoretical derivation. J. Cereb. Blood Flow Metab. 18 (1998) 1365-1377
14. Liang Z., and Lauterbur P.C. Principles of Magnetic Resonance Imaging: A Signal Processing Perspective (2000), IEEE Press, Picsataway
15. Milne-Thomson L.M. The Calculus of Finite Differences (1981), Chelsea, New York
16. Østergaard L., Weisskoff R.M., Chesler D.A., Glydensted C., and Rosen B.R. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn. Reson. Med. 36 (1996) 715-725
17. Pazy A. Semigroups of Linear Operators and Applications to Partial Differential Equations (1983), Springer-Verlag, New York
18. Reishofer, G., Enzinger, C., Fazekas, F., Keeling, S.L., Stollberger, R., 2004. Improved visibility of hemodynamic parameter maps using ICA corrected DCE-MRI time series. In: ISMRM Workshop on Quantitative Cerebral Perfusion Imaging Using MRI: A Technical Perspective, Venice, Italy, March 21-23.
19. Reishofer G., Fazekas F., Keeling S.L., Enzinger C., Payer F., Simbrunner J., and Stollberger R. Minimizing macrovessel signal in cerebral perfusion imaging using independent component analysis. Magn. Reson. Med. 57 2 (2007) 278-288
20. Schumaker L. Spline Functions: Basic Theory (1981), Wiley, New York
21. Schwartz L. Etude des sommes d'exponentielles (1959), Hermann, Paris
22. Tofts P.S. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J. Magn. Reson. Imag. 7 (1997) 91-101
23. Widder D.V. The Laplace Transform (1966), Princeton University Press, Princeton, NJ