Schmid, Volker J. (2. March 2010): SpatioTemporal Modelling of Perfusion Cardiovascular MRI. Department of Statistics: Technical Reports, No.77 

545kB 
Abstract
Myocardial perfusion MRI provides valuable insight into how coronary artery and microvascular diseases affect myocardial tissue. Stenosis in a coronary vessel leads to reduced maximum blood flow (MBF), but collaterals may secure the blood supply of the myocardium but with altered tracer kinetics. To date, quantitative analysis of myocardial perfusion MRI has only been performed on a local level, largely ignoring the contextual information inherent in different myocardial segments. This paper proposes to quantify the spatial dependencies between the local kinetics via a Hierarchical Bayesian Model (HBM). In the proposed framework, all local systems are modelled simultaneously along with their dependencies, thus allowing more robust contextdriven estimation of local kinetics. Detailed validation on both simulated and patient data is provided.
Item Type:  Paper (Technical Report) 

Status:  Publisher's Version 
Keywords:  Myocardial Perfusion MRI, Bayes, Spatiotemporal modelling, PSplines,Markov random fields 
Faculties:  Medicine Mathematics, Computer Science and Statistics > Statistics > Technical Reports 
Subjects:  500 Science > 510 Mathematics 
URN:  urn:nbn:de:bvb:19epub113993 
Language:  English 
ID Code:  11399 
Deposited On:  03. Mar 2010 08:55 
Last Modified:  29. Apr 2016 09:07 
References:  [1] M. JeroschHerold, R. T. Seethamraju, C. M. Swingen, N. M. Wilke, and A. E. Stillman, “Analysis of myocardial perfusion MRI,” J Magn Reson Imaging, vol. 19, no. 6, pp. 758–70, 2004. [2] 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 England Journal of Medicine, vol. 346, no. 25, pp. 1948–1953, 2002. [3] M. JeroschHerold, X. Hu, N. S. Murthy, and R. T. Seethamraju, “Time delay for arrival of MR contrast agent in collateraldependent myocardium,” IEEE Trans Med Imaging, vol. 23, no. 7, pp. 881–90, 2004. [4] M. JeroschHerold, C. Swingen, and R. Seethamraju, “Myocardial blood flow quantification with MRI by modelindependent deconvolution,” Medical Physics, vol. 29, no. 5, pp. 886–897, 2002. [5] P. Eilers and B. Marx, “Flexible smoothing with Bsplines and penalties,” Statistical Science, vol. 11, no. 2, pp. 89–121, 1996. [6] B. Marx and P. Eilers, “Direct generalized additive modelling with penalized likelihood,” Computational Statistics and Data Analysis, vol. 28, no. 2, pp. 193–209, 1998. [7] P. R. Johnston and R. M. Gulrajani, “Selecting the corner in the Lcurve approach to Tikhonov regularization,” IEEE Trans Biomed Eng, vol. 47, no. 9, pp. 1293–6, 2000. [8] A. Brezger, L. Fahrmeir, and A. Hennerfeind, “Adaptive Gaussian Markov random fields with applications in human brain mapping,” Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 56, no. 3, pp. 327–345, 2007. [9] V. Schmid, B. Whitcher, A. Padhani, and G. Yang, “Quantitative analysis of dynamic contrastenhanced MR images based on Bayesian Psplines,” IEEE Transactions on Medical Imaging, vol. 28, pp. 789–798, 2009. [10] V. J. Schmid, P. D. Gatehouse, and G. Z. Yang, “Attenuation resilient AIF estimation based on hierarchical Bayesian modelling for first pass myocardial perfusion MRI,” in Medical Imaging Computing and ComputerAssisted Intervention  MICCAI 2007, vol. 10. Berlin: Springer, 2007, pp. 393–400. [11] M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs, S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan, and M. S. Verani, “Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association,” Int J Cardiovasc Imaging, vol. 18, no. 1, pp. 539–42, 2002. [12] K. Held, E. Rota Kops, B. J. Krause, r. Wells, W. M., R. Kikinis, and H. W. MüllerGartner, “Markov random field segmentation of brain MR images,” IEEE Trans Med Imaging, vol. 16, no. 6, pp. 878–86, 1997. [13] A. W. Liew and H. Yan, “An adaptive spatial fuzzy clustering algorithm for 3d MR image segmentation,” IEEE Trans Med Imaging, vol. 22, no. 9, pp. 1063–75, 2003. [14] C. Gössl, L. Fahrmeir, and D. P. Auer, “Bayesian modelling of the hemodynamic response function in BOLD fMRI,” NeuroImage, vol. 14, pp. 140–148, 2001. [15] W. D. Penny, N. J. TrujilloBarreto, and K. J. Friston, “Bayesian fMRI time series analysis with spatial priors,” NeuroImage, vol. 24, no. 2, pp. 350–62, 2005. [16] M. W. Woolrich, M. Jenkinson, J. M. Brady, and S. M. Smith, “Fully Bayesian spatiotemporal modelling of FMRI data,” IEEE Trans Med Imaging, vol. 23, no. 2, pp. 213–31, 2004. [17] S. Heim, L. Fahrmeir, P. H. C. Eilers, and B. D. Marx, “3D spacevarying coefficient models with application to diffusion tensor imaging,” Computational Statistics & Data Analysis, vol. 51, no. 12, pp. 6212–6228, 2007. [18] V. J. Schmid, B. Whitcher, A. R. Padhani, N. J. Taylor, and G.Z. Yang, “Bayesian methods for pharmacokinetic models in dynamic contrastenhanced magnetic resonance imaging,” IEEE Transactions on Medical Imaging, vol. 25, no. 12, pp. 1627–1636, 2006. [19] L. KnorrHeld, “Bayesian modelling of inseparable spacetime variation in disease risk,” Statistics in Medicine, vol. 19, no. 1718, pp. 2555–2567, 2000. [20] W. R. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. London: Chapman & Hall, 1996. [21] D. G. Clayton, “Generalized linear mixed models,” pp. 275–301, 1996. [22] L. Fahrmeir, T. Kneib, and S. Konrath, “Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection,” Statistics and Computing, 2010, to appear. [23] J. E. Besag and D. Higdon, “Bayesian analysis of agricultural field experiments,” Journal of the Royal Statistical Society Series B, vol. 61, pp. 691–746, 1999. [24] H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall/CRC, 2005. [25] P. Gatehouse, A. Elkington, N. Ablitt, G.Z. Yang, D. Pennell, and D. Firmin, “Accurate assesment of the arterial input function during highdose myocardial perfusion cardiovascular magnetic resonance,” Journal of Magnetic Resonance Imaging, vol. 20, pp. 39–45, 2004. 