Tensor Metrics and Charged Containers for 3D Q-space Sample Distribution
2013 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI, Springer, 2013, 679-686 p.Conference paper (Refereed)
This paper extends Jones’ popular electrostatic repulsion based algorithm for distribution of single-shell Q-space samples in two fundamental ways. The first alleviates the single-shell requirement en- abling full Q-space sampling. Such an extension is not immediately ob- vious since it requires distributing samples evenly in 3 dimensions. The extension is as elegant as it is simple: Add a container volume of the de- sired shape having a constant charge density and a total charge equal to the negative of the sum of the moving point charges. Results for spherical and cubic charge containers are given. The second extension concerns the way distances between sample point are measured. The Q-space samples represent orientation, rather than direction and it would seem appropri- ate to use a metric that reflects this fact, e.g. a tensor metric. To this end we present a means to employ a generalized metric in the optimization. Minimizing the energy will result in a 3-dimensional distribution of point charges that is uniform in the terms of the specified metric. The radi- cally different distributions generated using different metrics pinpoints a fundamental question: Is there an inherent optimal metric for Q-space sampling? Our work provides a versatile tool to explore the role of differ- ent metrics and we believe it will be an important contribution to further the continuing debate and research on the matter.
Place, publisher, year, edition, pages
Springer, 2013. 679-686 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8149
dMRI q-space sampling
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-95753DOI: 10.1007/978-3-642-40811-3_85ISI: 000333330700085ISBN: 978-3-642-40810-6 (print)ISBN: 978-3-642-40811-3 (online)OAI: oai:DiVA.org:liu-95753DiVA: diva2:637547
MICCAI 2013, Nagoya, Japan, September 22-26 2013
FunderLinnaeus research environment CADICSSwedish Research CouncilNIH (National Institute of Health)