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Geometric Model Extraction from Magnetic Resonance Volume Data

Laidlaw, David H. (1995) Geometric Model Extraction from Magnetic Resonance Volume Data. Computer Science Technical Reports, California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechCSTR:1995.cs-tr-95-05

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Abstract

This thesis presents a computational framework and new algorithms for creating geometric models and images of physical objects. Our framework combines magnetic resonance imaging (MRI) research with image processing and volume visualization. One focus is feedback of requirements from later stages of the framework to earlier ones. Within the framework we measure physical objects yielding vector-valued MRI volume datasets. We process these datasets to identify different materials, and from the classified data we create images and geometric models. New algorithms developed within the framework include a goal-based technique for choosing MRI collection protocols and parameters and a family of Bayesian tissue-classification methods. The goal-based data-collection technique chooses MRI protocols and parameters subject to specific goals for the collected data. Our goals are to make identification of different tissues possible with data collected in the shortest possible time. Our method compares results across different collection protocols, and is fast enough to use for steering the data-collection process. Our new tissue-classification methods operate on small regions within a volume dataset, not directly on the sample points. We term these regions voxels and assume that each can contain a mixture of materials. The results of the classification step are tailored to make extraction of surface boundaries between solid object parts more accurate. Another new algorithm directly renders deformed volume data produced, for example, by simulating the movement of a flexible body. The computational framework for building geometric models allows computer graphics users to more easily create models with internal structure and with a high level of detail. Applications exist in a variety of fields including computer graphics modeling, biological modeling, anatomical studies, medical diagnosis, CAD/CAM, robotics, and computer animation. We demonstrate the utility of the computational framework with a set of computer graphics images and models created from data.


Item Type:Report or Paper (Technical Report)
Additional Information:© 1995 David H. Laidlaw. California Institute of Technology. Defended May 23, 1995. This work was supported in part by grants from Apple, DEC, Hewlett Packard, and IBM. Additional support was provided by NSF (ASC-89-20219) as part of the NSF/ARPA STC for Computer Graphics and Scientific Visualization, by the DOE (DE-FG03-92ER25134) as part of the Center for Research in Computational Biology, by the National Institute on Drug Abuse and the National Institute of Mental Health as part of the Human Brain Project, and by the Beckman Institute Foundation. All opinions, findings, conclusions, or recommendations expressed in this document are those of the author(s) and do not necessarily reflect the views of the sponsoring agencies. Matthew Avalos has been instrumental in implementing large parts of this work. Without his dedication and incisive comments, this would have been smaller and taken a lot longer. Thanks to Jose Jimenez for the late-night MR sessions and to Dr. Brian Ross for allowing scanning time at the Huntington Magnetic Resonance Center in Pasadena, where some of our data was collected. I am grateful to Professor Alan Barr and his computer graphics group past and present: Cindy Ball, Ronen Barzel, Allen Corcoran, Carolyn Collins, Bena Currin, Dan Fain, Louise Foucher, Kurt Fleischer, Dave Kirk, Alf Mikula, Mark Montague, Preston Pfarner, John Snyder, Eric Winfree, Adam Woodbury, and Denis Zorin. The lab folks provided a great environment together with lots of collaborative help and advice. My appreciation also goes to the MRI and Biology folks in the Beckman Institute at Caltech: Erik Ahrens, John Allman, Andres Collazo, Hanan Davidowitz, Dian De Sha, Michael Figdor, Mary Flowers, Scott Fraser, Pratik Ghosh, Russ Jacobs, Jim Narasimhan, Mark O’Dell, John Shih, and Bill Trevarro. They provided me with many discussions of how MRI works and suggestions on how I ought to be doing things. Thanks to the readers of early drafts for their patience, stamina and very helpful suggestions: Cindy Ball, Alan Barr, Ronen Barzel, Bena Currin, Dian De Sha, Dan Fain, David Kirk, Barbara Meier, and Preston Pfarner. I also want to express my appreciation to Charles and Patricia Laidlaw, my parents, for their support and help throughout my time at Caltech. And finally, thanks to my wife, Barbara Meier, for the moral and emotional support that helped me make it through this long and sometimes arduous journey.
Group:Computer Science Technical Reports
Funders:
Funding AgencyGrant Number
AppleUNSPECIFIED
DECUNSPECIFIED
Hewlett-PackardUNSPECIFIED
IBMUNSPECIFIED
NSFASC-89-20219
Advanced Research Projects Agency (ARPA)UNSPECIFIED
STC for Computer Graphics and Scientific VisualizationUNSPECIFIED
Department of Energy (DOE)DE-FG03-92ER25134
Center for Research in Computational BiologyUNSPECIFIED
National Institute on Drug AbuseUNSPECIFIED
National Institute of Mental Health (NIMH)UNSPECIFIED
Arnold and Mabel Beckman FoundationUNSPECIFIED
NIHUNSPECIFIED
DOI:10.7907/Z98S4MXP
Record Number:CaltechCSTR:1995.cs-tr-95-05
Persistent URL:http://resolver.caltech.edu/CaltechCSTR:1995.cs-tr-95-05
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:26879
Collection:CaltechCSTR
Deposited By: Imported from CaltechCSTR
Deposited On:14 May 2001
Last Modified:02 May 2017 18:04

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