Abstract
We present a novel Bayesian framework for non-rigid motion correction and pharmacokinetic parameter estimation in dceMRI sequences which incorporates a physiological image formation model into the similarity measure used for motion correction. The similarity measure is based on the maximization of the joint posterior probability of the transformations which need to be applied to each image in the dataset to bring all images into alignment, and the physiological parameters which best explain the data. The deformation framework used to deform each image is based on the diffeomorphic logDemons algorithm. We then use this method to co-register images from simulated and real dceMRI data-sets and show that the method leads to an improvement in the estimation of physiological parameters as well as improved alignment of the images.
Chapter PDF
Similar content being viewed by others
Keywords
- Similarity Measure
- Mutual Information
- Motion Correction
- Target Registration Error
- Medical Image Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Armitage, P., Behrenbruch, C., Brady, M., Moore, N.: Extracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast. Medical Image Analysis 9(4), 315–329 (2005)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis 12(1), 26 (2008)
Buonaccorsi, G.A., Roberts, C., Cheung, S., Watson, Y., Davies, K., Jackson, A., Jayson, G.C., Parker, G.J.M.: Tracer kinetic model-driven registration for dynamic contrast enhanced MRI time series. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 91–98. Springer, Heidelberg (2005)
Hayton, P., Brady, M., Tarassenko, L., Moore, N.: Analysis of dynamic MR breast images using a model of contrast enhancement. Medical Image Analysis 1(3), 207–224 (1997)
Karsa, L., Lignini, T.A., Patnick, J., Lambert, R., Sauvaget, C.: The dimensions of the CRC problem. Best Pract. Res. Cl. Ga. 24(4), 381–396 (2010)
Roche, A., Malandain, G., Ayache, N.: Unifying maximum likelihood approaches in medical image registration. Int. J. Imag. Syst. Tech. 11(1), 71–80 (2000)
Schmid, V.J., Whitcher, B., Padhani, A.R., Taylor, N.J., Yang, G.Z.: A Bayesian hierarchical model for the analysis of a longitudinal dynamic contrast-enhanced MRI oncology study. Magn. Reson. Med. 61(1), 163–174 (2009)
Tofts, P.S., Kermode, A.G.: Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging 1. Fundamental Concepts. MR in Medicine 17(2), 357–367 (1991)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric log-domain diffeomorphic registration: A demons-based approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008)
Weinmann, H.J., Brasch, R.C., Press, W.R., Wesbey, G.E.: Characteristics of gadolinium-DTPA complex: a potential NMR contrast agent. AJR 142(3), 619 (1984)
Zahra, M.A., Hollingsworth, K.G., Sala, E., Lomas, D.J., Tan, L.T.: Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy. The Lancet Oncology 8(1), 63–74 (2007)
Zöllner, F.G., Sance, R., Rogelj, P., Ledesma-Carbayo, M.J., Rørvik, J., Santos, A., Lundervold, A.: Assessment of 3D dceMRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Comput. Med. Imaging Graph 33(3), 171–181 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhushan, M., Schnabel, J.A., Risser, L., Heinrich, M.P., Brady, J.M., Jenkinson, M. (2011). Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23623-5_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-23623-5_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23622-8
Online ISBN: 978-3-642-23623-5
eBook Packages: Computer ScienceComputer Science (R0)