Abstract
We propose an information theoretic framework to automatically infer the physical relationship and asses the quality of multiparametric MRI sequences. The method is based on the Crutchfield information metric. This distance measure can be computed solely based on the voxel intensities. In a series of experiments we proof its usefulness. First, we show that given multiparametric MRI data sets it is possible to discover the physical relationship w.r.t. the acquisition parameters of the individual sequences. Next, we demonstrate that this relationship can be employed to perform a quality check of a large (\(N=216\)) data set by identifying faulty components, e.g. due to motion artifacts. Finally, we use a multidirectional diffusion weighted data set to confirm that the approach is fine grained enough to even detect small differences of diffusion vectors as well as the direction of the phase encoding of an echo planar imaging (EPI) sequence. Future work aims at transferring the preliminary results of these promising experiments into clinical routine and at standardizing MRI protocols for large scale clinical trials.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
3D Slicer v4.3. http://www.slicer.org
Cornfeld, D., Sprenkle, P.: Multiparametric MRI: standardizations needed. Oncol. (Williston Park) 27(4), 277–280 (2013)
Crutchfield, J.: Information and its metric. In: Lam, L., Morris, H.C. (eds.) Nonlinear Structures in Physical Systems. Woodward Conference. Springer, New York (1990)
Feinberg, D.A., Moeller, S., Smith, S.M., Auerbach, E., Ramanna, S., Gunther, M., Glasser, M.F., Miller, K.L., Ugurbil, K., Yacoub, E.: Multiplexed echo planar imaging for sub-second whole brain fmri and fast diffusion imaging. PLoS One 5(12), e15710 (2010)
FMRIB’s software Library FSL v5.0. http://fsl.fmrib.ox.ac.uk
Graphviz. http://www.graphviz.org
Hagberg, A.A., Schult, D.A., Swart, P.J.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference (SciPy 2008), pp. 11–15. Pasadena, CA USA, August 2008
Johnson, H., Harris, G., Williams, K.: BRAINSFit: mutual information registrations of whole-brain 3D images, using the insight toolkit. The Insight J., October 2007. http://hdl.handle.net/1926/1291
Jones, D.K., Horsfield, M.A., Simmons, A.: Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42(3), 515–525 (1999)
Kaplan, F., Hafner, V.V.: Information-theoretic framework for unsupervised activity classification. Adv. Robot. 20(10), 1087–1103 (2006). http://www.tandfonline.com/doi/abs/10.1163/156855306778522514
Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J Med. Internet Res. 15(11), e245 (2013)
Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soci. 7(1), 48–50 (1956). http://www.jstor.org/stable/2033241
Kullback, S.: Information Theory and Statistics. Dover, New York (1968)
Olsson, L.A., Nehaniv, C.L., Polani, D.: From unknown sensors and actuators to actions grounded in sensorimotor perceptions. Connect. Sci. 18(2), 121–144 (2006). http://www.tandfonline.com/doi/abs/10.1080/09540090600768542
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Python v2.7.6. http://www.python.org
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Sci. 290(5500), 2323–2326 (2000)
Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Chicago (1949)
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Sci. 290(5500), 2319–2323 (2000)
Tononi, G., Edelman, G.M., Sporns, O.: Complexity and coherency: integrating information in the brain. Trends Cogn. Sci. 2(12), 474–484 (1998)
Acknowledgements
Thanks to the anonymous reviewer who suggested the experiment with multiple diffusion directions. This work was supported by a postdoctoral fellowship from the Medical Faculty of the University of Heidelberg.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Kleesiek, J., Biller, A., Bartsch, A.J., Ueltzhöffer, K. (2015). Crutchfield Information Metric: A Valid Tool for Quality Control of Multiparametric MRI Data?. In: Fred, A., Gamboa, H., Elias, D. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2015. Communications in Computer and Information Science, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-27707-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-27707-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27706-6
Online ISBN: 978-3-319-27707-3
eBook Packages: Computer ScienceComputer Science (R0)