Voxel-based adaptive spatio-temporal modelling of perfusion cardiovascular MRI

IEEE Transactions on Medical Imaging

Volumn 30, Issue 7, 2011, Pages 1305-1313

  Schmid, Volker J ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Bayes methods; cardiac imaging; hierarchical modelling; magnetic resonance imaging (MRI); quantitative image analysis

Indexed keywords

BAYES METHOD; BLOOD FLOW; BLOOD SUPPLY; CARDIAC IMAGING; CONTRAST-ENHANCED; CORONARY FLOW; CORONARY VESSEL; HIERARCHICAL BAYESIAN MODELS; HIERARCHICAL MODELLING; LOCAL RESPONSE; LOCAL SYSTEM; LOW SIGNAL-TO-NOISE RATIO; MYOCARDIAL BLOOD FLOW; MYOCARDIAL PERFUSION; MYOCARDIAL TISSUE; PATIENT DATA; QUANTITATIVE IMAGE ANALYSIS; ROBUST ANALYSIS; SPATIAL INFORMATIONS; SPATIO-TEMPORAL; TRACER KINETICS;

BAYESIAN NETWORKS; BLOOD; HEMODYNAMICS; HOSPITAL DATA PROCESSING; RESONANCE; SIGNAL TO NOISE RATIO; TISSUE;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; CORONARY ARTERY DISEASE; CORONARY BLOOD VESSEL; HEART; HUMAN; IMAGE PROCESSING; METHODOLOGY; MONTE CARLO METHOD; MYOCARDIAL PERFUSION IMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOPHYSIOLOGY; PHYSIOLOGY; PROBABILITY; REPRODUCIBILITY;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; CORONARY ARTERY DISEASE; CORONARY VESSELS; HEART; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MODELS, CARDIOVASCULAR; MONTE CARLO METHOD; MYOCARDIAL PERFUSION IMAGING; REPRODUCIBILITY OF RESULTS;

EID: 79959782486     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2011.2109733     Document Type: Article

References (30)

1. J. R. Panting, P. D. Gatehouse, G.-Z. Yang, F. Grothues, D. N. Firmin, P. Collins, and D. J. Pennell, "Abnormal subendocardial perfusion in cardiac syndrome X detected by cardiovascular magnetic resonance imaging,"New Eng. J. Med., vol. 346, no. 25, pp. 1948-53, 2002. (Pubitemid 34651354)
2. M. Jerosch-Herold, R. T. Seethamraju, C. Swingen, N. M. Wilke, and A. E. Stillman, "Analysis of myocardial perfusion MRI," J. Magn. Reson. Imag., vol. 19, no. 6, pp. 758-770, 2004. (Pubitemid 38788658)
3. M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs, and S. Kaul, "Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: A statement for healthcare profes-sionals fromthe Cardiac Imaging Committee ofthe Council on Clinical Cardiology of the American Heart Association," Circulation, vol. 105, no. 4, pp. 539-542, 2002. (Pubitemid 34262671)
4. T. A. Goldstein, M. Jerosch-Herold, B. Misselwitz, H. Zhang, R. J. Gropler, and J. Zheng, "Fast mapping ofmyocardial blood flow with MR first-pass perfusion imaging,"Magn. Reson. Med., vol. 59, no. 6, pp. 1394-1400, 2008. (Pubitemid 351813270)
5. M. Jerosch-Herold, C. Swingen, and R. T. Seethamraju, "Myocardial blood flow quantification with MRI by model-independent deconvolution,"Med. Phys., vol. 29, no. 5, p. 886, 2002.
6. P. H. C. Eilers and B. D. Marx, "Flexible smoothing with B-splines and penalties (with comments and rejoinder)," Stat Sci., vol. 11, no. 2, pp. 89-121, 1996. (Pubitemid 126706128)
7. P. R. Johnston and R. M. Gulrajani, "Selectingthe corner in the L-curve approachto Tikhonov regularization,"IEEE Trans. Biomed. Eng., vol. 47,no.9,pp.1293-1296,Sep.2000.
8. B. D. Marx and P. H. C. Eilers, "Direct generalized additive modeling with penalized likelihood," Computat Stat DataAnal., vol. 28, no. 2, pp. 193-209, 1998. (Pubitemid 128369603)
9. A. Brezger, L. Fahrmeir, and A. Hennerfeind, "Adaptive Gaussian Markov random fields with applications in human brain mapping," J. R. Stat Soc.: Series C (AppliedStatistics), vol. 56, no. 3, pp. 327-345, 2007.
10. V. J. Schmid, B. Whitcher, A. R. Padhani, and G.-Z. Yang, "Quan-titative analysis of dynamic contrast-enhanced MR images based on Bayesian P-splines," IEEE Trans. Med. Imag, vol. 28, no. 6, pp. 789-798, Jun. 2009.
11. V. J. Schmid, P. D. Gatehouse, and G.-Z. Yang, N. Ayache, S. Ourselin, and A. Maeder, Eds., "Attenuation resilient AIF estimation based on hierarchical Bayesian modelling for first pass myocardial perfusion MRI," inMed. Image Computing Computer-Assisted Intervent.\-MICCAI2007, Berlin, Germany, 2007, pp. 393-400.
12. K. Held, E. Rota Kops, B. J. Krause, W. Wells, R. Kikinis, and H. W. Muller-Gartner, "Markov random field segmentation of brain MR images," IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 878-886, Dec. 1997. (Pubitemid 127770446)
13. C. Gössl, D. P. Auer, and L. Fahrmeir, "Bayesian spatiotemporal infer- ence in functional magnetic resonance imaging," Biometrics, vol. 57, no. 2, pp. 554-562, 2001. (Pubitemid 32488616)
14. W. D. Penny, N. J. Trujillo-Barreto, and K. J. Friston, "Bayesian fMRI time series analysis with spatial priors," NeuroImage, vol. 24, no. 2, pp. 350-362, 2005. (Pubitemid 41359032)
15. M. W. Woolrich, M. Jenkinson, J. M. Brady, and S. M. Smith, "Fully Bayesian spatio-temporal modeling of FMRI data," IEEE Trans. Med. Imag,vol. 23,no. 2,pp. 213-231, Feb.2004.
16. Q. Williams, J. A. Noble, A. Ehlgen, and H. Becher, "Tissue perfusion diagnostic classification using a spatio-temporal analysis ofcon- trastultrasound imagesequences,"in Information Processing in Medical Imaging, G. E. Christensen and M. Sonka, Eds. Berlin: Springer, 2005, vol. 19, pp. 222-33. (Pubitemid 41425318)
17. V. J. Schmid, B. Whitcher, A. R. Padhani, N. J. Taylor, and G.-Z. Yang, "Bayesian methods for pharmacokinetic models in dynamic contrast- enhanced magnetic resonance imaging," IEEE Trans. Med. Imag,vol. 25, no. 12, pp. 1627-36, Dec. 2006. (Pubitemid 44936711)
18. S. Heim, L. Fahrmeir, P. H. C. Eilers, and B. D. Marx, "3D space-varying coefficient models with application to diffusion tensor imaging," Computat Stat DataAnal., vol. 51, no. 12, pp. 6212-6228, 2007. (Pubitemid 47126015)
19. V. J. Schmid and G.-Z. Yang, "Spatio-temporal modelling offirst-pass perfusion cardiovascular MRI,"in WorldCongr. Med. Phys. Biomed. Eng., Munich, Germany, Sep. 7-12, 2009, pp. 45-48.
20. H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications (Monographs on Statistics and Applied Probability). London,U.K.:Chapman&Hall,2005.
21. D. G. Clayton, Generalized Linear Mixed Models, pp. 275-301, 1996.
22. J. E. Besag and D. M. Higdon, "Bayesian analysis ofagricultural field experiments,"J. R. Stat Soc. SeriesB (Stat Methodol), vol. 61, no. 4, pp. 691-746, 1999.
23. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo inPractice. London, U.K.: Chapman & Hall, 1996.
24. D. Bates and M. Maechler, Matrix: sparse and dense matrix classes and methods 2010, R package ver. 0.999375-45 [Online]. Available: http://CRAN.R-project.org/package=Matrix
25. R: A language and environment for statistical computing. Vienna, Austria, R Development Core Team, R Foundation for Statistical Com-puting 2010 [Online]. Available: http://www.R-project.org
26. P. D. Gatehouse, A. G. Elkington, N. A. Ablitt, G.-Z. Yang, D. J. Pen-nell, and D. N. Firmin, "Accurate assessment ofthe arterial inputfunc-tion during high-dose myocardial perfusion cardiovascular magnetic resonance,"J. Magn. Reson. Imag., vol. 20, pp. 39-45, 2004. (Pubitemid 38857515)
27. M. Jerosch-Herold, X. Hu, N. S. Murthy C. Rickers, and A. E. Stillman, "Magnetic resonance imaging of myocardial contrast en-hancement with MS-325 and its relation to myocardial blood flow and the perfusion reserve," J. Magn. Reson. Imag, vol. 18, no. 5, pp. 544-54, 2003. (Pubitemid 37339799)
28. L. Fahrmeir, T. Kneib, and S. Konrath, "Bayesian regularisation in structured additive regression: A unifying perspective on shrinkage, smoothing and predictor selection," Stat Comput, vol. 20, no. 2, pp. 203-219, 2009.
29. L. M. Bidaut and J. P. Vallée, "Automated registration ofdynamic MR images forthe quantification ofmyocardial perfusion,"J. Magn. Reson. Imag.,vol. 13,no.4,pp.648-55,2001. (Pubitemid 32231523)
30. J. Milles, R. van der Geest, M. Jerosch-Herold, J. Reiber, and B. Lelieveldt, "Fully automated registration offirst-pass myocardial per-fusion MRI using independent component analysis," in Information Processing inMedical Imaging, N. Karssemeijer and B. Lelieveldt, Eds. Berlin, Germany: Springer, 2007, vol. 4584, Lecture Notes in ComputerScience,pp.544-555.