From Expected Propagator Distribution to Optimal Q-space Sample Metric
2014 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III / [ed] Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe, Springer, 2014, 217-224 p.Conference paper (Refereed)
We present a novel approach to determine a local q-space metric that is optimal from an information theoretic perspective with respect to the expected signal statistics. It should be noted that the approach does not attempt to optimize the quality of a pre-defined mathematical representation, the estimator. In contrast, our suggestion aims at obtaining the maximum amount of information without enforcing a particular feature representation.
Results for three significantly different average propagator distributions are presented. The results show that the optimal q-space metric has a strong dependence on the assumed distribution in the targeted tissue. In many practical cases educated guesses can be made regarding the average propagator distribution present. In such cases the presented analysis can produce a metric that is optimal with respect to this distribution. The metric will be different at different q-space locations and is defined by the amount of additional information that is obtained when adding a second sample at a given offset from a first sample. The intention is to use the obtained metric as a guide for the generation of specific efficient q-space sample distributions for the targeted tissue.
Place, publisher, year, edition, pages
Springer, 2014. 217-224 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8675
IdentifiersURN: urn:nbn:se:liu:diva-110383DOI: 10.1007/978-3-319-10443-0_28ISI: 000346250200028ISBN: 978-3-319-10442-3 (print)ISBN: 978-3-319-10443-0 (online)OAI: oai:DiVA.org:liu-110383DiVA: diva2:745160
17th International Conference (MICCAI 2014), Boston, MA, USA, September 14-18, 2014
FunderSwedish Research CouncilNIH (National Institute of Health)