Using human and model performance to compare MRI reconstructions

IEEE Transactions on Medical Imaging

Volumn 25, Issue 11, 2006, Pages 1510-1517

  Tisdall, M Dylan ^a   Atkins, M Stella ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Denoising; Magnetic resonance imaging (MRI); Observers; Signal detection; Signal processing

Indexed keywords

IMAGE ANALYSIS; IMAGE RECONSTRUCTION; MATHEMATICAL MODELS; SIGNAL DETECTION; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO;

CONTRAST TO NOISE RATIO; DENOISING; MAGNETIC RESONANCE IMAGING (MRI) RECONSTRUCTION; OBSERVERS;

MAGNETIC RESONANCE IMAGING;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; HUMAN; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; OBSERVER VARIATION; PATTERN RECOGNITION; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TASK PERFORMANCE; VALIDATION STUDY;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; OBSERVER VARIATION; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TASK PERFORMANCE AND ANALYSIS;

EID: 33750802387     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2006.881374     Document Type: Article

References (19)

1. 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.
2. R. D. Nowak, "Wavelet-based Rician noise removal for magnetic resonance imaging," IEEE Trans. Image Process., vol. 8, no. 10, pp. 1408-1418, Oct. 1999.
3. M. E. Alexander, R. Baumgartner. A. R. Summers, C. Windischberger. M. Klarhoefer, E. Moser, and R. L. Somorjai, "A wavelet-based method for improving signal-to-noise ratio and contrast in MR images," Magn, Reson. Imag., vol. 18, pp. 169-180, 2000.
4. P. Bao and L. Zhang, "Noise reduction for magnetic resonance images via adaptive multiscale products thresholding," IEEE Trans. Med. Imag., vol. 22, no. 9, pp. 1089-1099, Sep. 2003.
5. M. A. Bernstein, D. M. Thomasson, and W. Perman, "Improved detectability in low signal-to-noise ratio magnetic resonance images by means of a phase-corrected real reconstruction," Med. Phys., vol. 16, pp. 813-817, Sep. 1989.
6. D. Noll, D. Nishimura, and A. Macovski, "Homodyne detection in magnetic resonance imaging," IEEE Trans. Med. Imag., vol. 10, no. 2, pp. 154-163, Jun. 1991.
7. M. D. Tisdall and M. S. Atkins, "MRI denoising via phase error estimation," Proc. SPIE, vol. 5747, pp. 646-654, 2005.
8. Z. Chang and Q.-S. Xiang, "Nonlinear phase correction with an extended statistical algorithm," IEEE Trans. Med. Imag., vol. 24, no. 6, pp. 791-798, Jun. 2005.
9. M. P. Eckstein, C. K. Abbey, and J. S. Whiting, "Human vs model observers in anatomic backgrounds," Proc. SPIE, vol. 3340, pp. 16-26, 1998.
10. M. P. Eckstein, C. K. Abbey, F. O. Bochud, J. L. Bartoff, and J. S. Whiting, "The effect of image compression in model and human performance," Proc. SPIE, vol. 3663, pp. 243-252, 1999.
11. J. P. Rolland and H. H. Barrett, "Effect of random background inhomogeneity on observer detection performance," J. Opt. Soc. Am. A, vol. 9, no. 5, pp. 649-658, May 1992.
12. K. J. Myers and H. H. Barrett, "Addition of a channel mechanism to the ideal-observer model," J. Opt. Soc. Am. A, vol. 4, no. 12, pp. 2447-2457, Dec. 1987.
13. C. K. Abbey and F. O. Bouchud, "Modelling visual detection tasks in correlated image noise with linear model observers," in Handbook Med. Imag.. Bellingham, WA: SPIE, 2000, vol. 1, ch. 11, pp. 629-654.
14. A. Macovski, "Noise in MRI," Magn. Reson. Med., vol. 36, pp. 494-497, 1996.
15. V. Kuperman, Magnetic Resonance Imaging - Physical Principles and Applications. New York: Academic, 2000.
16. H. Gudbjartsson and S. Patz, "The Rician distribution of noisy MRI data," Magn. Reson. Med., vol. 34, pp. 910-914, 1995.
17. G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. New York: Wiley, 1982.
18. S. Park, "Efficiency of the human observer detecting random signals in random backgrounds," J. Opt. Soc. Am. A, vol. 22, no. 1, pp. 3-16, Jan. 2005.
19. H. H. Barrett, C. K. Abbey, and E. Clarkson, "Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating function,"J. Opt. Soc. Am. A, vol. 15, no. 6, pp. 1520-1535, June 1998.