Spatio-Temporal Data Fusion for 3D+T Image Reconstruction in Cerebral Angiography
Name
Copeland-2010-Spatio-temporal data fusion for 3D+T image reconstruction in cerebral angiography.pdf
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1.24 MB
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Author(s)
Copeland, Andrew D.
Mangoubi, Rami S.
Desai, Mukund N.
Mitter, Sanjoy K.
Malek, Adel M.
Date Issued
June 2010
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Copeland, Andrew D et al. “Spatio-Temporal Data Fusion for 3D+T Image Reconstruction in Cerebral Angiography.” IEEE Transactions on Medical Imaging 29.6 (2010): 1238–1251. Web. 5 Apr. 2012. © 2010 Institute of Electrical and Electronics Engineers
Version
Final published version
Abstract
This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to allow image feedback during medical procedures that facilitate the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. The 3D time series is constructed by fusing a single static 3D model with two time sequences of 2D projections of the same imaged region. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of nonzero support. The variational problem is solved using a modified version of the Gauss-Seidel algorithm that exploits the spatio-temporal structure of the angiography problem. The 3D time series results are visualized using time series of isosurfaces, synthetic X-rays from arbitrary perspectives or poses, and 3D surfaces that show arrival times of the contrasted blood front using color coding. The derived visualizations provide physicians with a previously unavailable wealth of information that can lead to safer procedures, including quicker localization of flow altering abnormalities such as blood clots, and lower procedural X-ray exposure. Quantitative SNR and other performance analysis of the algorithm on computational phantom data are also presented.
MIT Department
Charles Stark Draper Laboratory
Lincoln Laboratory
Massachusetts Institute of Technology. Engineering Systems Division
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/TMI.2009.2039645
