[1] M. Jerosch-Herold, 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. Jerosch-Herold, X. Hu, N. S. Murthy, and R. T. Seethamraju, “Time delay for arrival of MR contrast agent in collateral-dependent myocardium,” IEEE Trans Med Imaging, vol. 23, no. 7, pp. 881–90, 2004. [4] M. Jerosch-Herold, C. Swingen, and R. Seethamraju, “Myocardial blood flow quantification with MRI by model-independent deconvolution,” Medical Physics, vol. 29, no. 5, pp. 886–897, 2002. [5] P. Eilers and B. Marx, “Flexible smoothing with B-splines 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 L-curve 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 contrast-enhanced 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 Computer-Assisted 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. Müller-Gartner, “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 3-d 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. Trujillo-Barreto, 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 spatio-temporal 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. Knorr-Held, “Bayesian modelling of inseparable space-time variation in disease risk,” Statistics in Medicine, vol. 19, no. 17-18, 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 high-dose myocardial perfusion cardiovascular magnetic resonance,” Journal of Magnetic Resonance Imaging, vol. 20, pp. 39–45, 2004.