MRI simulation-based evaluation of image-processing and classification methods

IEEE Transactions on Medical Imaging

Volumn 18, Issue 11, 1999, Pages 1085-1097

  Kwan, Remi K S ^a   Evans, Alan C ^a   Pike, Bruce ^a,b  

^a IEEE

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER AIDED ANALYSIS; COMPUTER SIMULATION; IMAGE ANALYSIS; MAGNETIC RESONANCE IMAGING; SPURIOUS SIGNAL NOISE;

BLOCH EQUATION;

MEDICAL IMAGING;

ARTICLE; BRAIN; HISTOLOGY; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; NUCLEAR MAGNETIC RESONANCE IMAGING;

BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PHANTOMS, IMAGING;

EID: 0033233097     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/42.816072     Document Type: Article

References (31)

1. F. Bloch, Nuclear induction, Phys. Rev., vol. 70, pp. 460-474, Oct. 1946.
2. J. Bittoun, J. Taquin, and M. Sauzade, A computer algorithm for the simulation of any nuclear magnetic resonance (NMR) imaging method, Magn. Reson. Imaging, vol. 2, pp. 113-120, 1984.
3. R. M. Summers, L. Axel, and S. Israel, A computer simulation of nuclear magnetic resonance imaging, Magn. Reson. Med., vol. 3, pp. 363-376, 1986.
4. M. B. E. Olsson, R. Wirestam, and B. R. R. Persson, A computer simulation program for MR imaging: Application to RF and static magnetic field imperfections, Magn. Reson. Med., vol. 34, pp. 612-617, 1995.
5. A. R. Brenner, J. Kursen, and T. G. Noll, Parallelized high-performance MRI simulation on a workstation cluster, in Proc. Int. Soc. Magnetic Resonance Medicine., Apr. 1997, vol. 3, p. 2052.
6. D. L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans, Automatic 3-D model-based neuroanatomical segmentation, Human Brain Mapping, vol. 3, pp. 190-208, 1995.
7. R. C. Herndon, J. L. Lancaster, A. W. Toga, and P. T. Fox, Quantification of white matter and gray matter volumes for Tl parametric images using fuzzy classifiers, J. Magn. Reson. Imaging, vol. 6, pp. 425-435, 1996.
8. R. R. Ernst and W. A. Anderson, Application of Fourier transform spectroscopy to magnetic resonance, Rev. Sei. Instrum., vol. 37, no. 1, pp. 93-102, Jan. 1966.
9. E. L. Hahn, Spin echoes, Phys. Rev., vol. 80, no. 4, pp. 580-594, Nov. 1950.
10. G. Goertzel, An algorithm for the evaluation of finite trigonometric series, Am. Math. Monthly, vol. 65, pp. 34-35, Jan. 1968.
11. R. E. Blahut, Fast Algorithms for Digital Signal Processing. Reading, MA: Addison-Wesley, 1985.
12. H. S. Choi, D. R. Haynor, and Y. Kim, Partial volume tissue classification of multichannel magnetic resonance images-A mixel model, IEEE Trans. Med. Imag., vol. 10, pp. 395-407, Sept. 1991.
13. C. J. Holmes, R. D. Hoge, D. L. Collins, and A. C. Evans, Enhancement of TI images using registration for signal averaging, in Proc. Functional Mapping Human Brain, 1996, p. S28.
14. D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans, Design and construction of a realistic digital brain phantom, IEEE Trans. Med. Imag., vol. 17, p. 463, June, 1998.
15. J. Pauly, D. Nishimura, and A. Macovski, A fc-space analysis of small-tip angle excitation, J. Magn. Reson., vol. 81, pp. 43-56, 1989.
16. W. A. Edelstein, G. H. Glover, C. J. Hardy, and R. W. Redington, The intrinsic signal-to-noise ratio in NMR imaging, Magn. Reson. Med., vol. 3, pp. 604-618, 1986.
17. D. L. Parker and G. T. Gullberg, Signal-to-noise efficiency in magnetic resonance imaging, Med. Phys., vol. 17, no. 2, pp. 250-257, Mar./Apr. 1990.
18. J. Carlson, L. Crooks, D. Ortendahl, D. M. Kramer, and L. Kaufman, Signal-to-noise ratio and section thickness in two-dimensional versus three-dimensional Fourier transform MR imaging, Radiology, vol. 166, pp. 266-270, Jan. 1988.
19. R. M. Henkelman, Measurement of signal intensities in the presence of noise in MR images, Med. Phys., vol. 12, no. 2, pp. 232-233, Mar./Apr. 1985.
20. J. W. M. Bulte, J. Vymazal, R. A. Brooks, C. Pierpaoli, and J. A. Frank, Frequency dependence of MR relaxation times II. Iron oxides, J. Magn. Reson. Imaging, vol. 3, pp. 641-648, 1993.
21. J. O. Christoffersson, L. E. Olsson, and S. Sjöberg, Nickel-doped agarose gel phantoms in MR imaging, Acta Radiologica, vol. 32, pp. 426-131, 1991.
22. H. Y. Carr and E. M. Purcell, Effects of diffusion on free precession in nuclear magnetic resonance experiments, Phys. Rev., vol. 94, no. 3, pp. 630-638, May 1954.
23. S. Meiboom and D. Gill, Modified spin-echo method for measuring nuclear relaxation times, Rev. Sei. Instrum., vol. 29, no. 8, pp. 688-691, Aug. 1958.
24. B. M. Dawant, A. P. Zijdenbos, and R. A. Margolin, Correction of intensity variations in MR images for computer-aided tissue classification, IEEE Trans. Med. Imag., vol. 12, pp. 770-781, Dec. 1993.
25. A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, Morphometric analysis of white matter lesions in MR images: Method and validation, IEEE Trans. Med. Imag., vol. 13, pp. 716-724, Dec. 1994.
26. V. Kollokian, Performance analysis of automatic techniques for tissue classification in magnetic resonance images of the human brain, M.S. thesis, Concordia Univ., Montreal, PQ, Canada, Nov. 1996.
27. A. P. Zijdenbos et al., Automatic quantification of multiple sclerosis lesion volume using stereotaxic space, in Visualization in Biomédical Computing, VBC'96 (Lecture Notes in Computer Science). Berlin, Germany: Springer-Verlag, vol. 1131, pp. 439-448.
28. R. K.-S. Kwan, A. C. Evans, and G. B. Pike, An extensible MRI simulator for post-processing evaluation, in Visualization in Biomédical Computing, VBC'96 (Lecture Notes in Computer Science). Berlin, Germany: Springer-Verlag, pp. 135-140.
29. R. K.-S. Kwan, A. C. Evans, and G. B. Pike, fMRI simulation for quantitative evaluation of analysis methods, in Proc. Functional Mapping Human Brain, May 1997, vol. 3, no. 4, p. S449.
30. R. W. Brockett, Finite Dimensional Linear Systems. New York: Wiley, 1970.
31. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.