Comparison of sparse domain approaches for 4D SPECT dynamic image reconstruction

Purpose Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult becau...

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Published inMedical physics (Lancaster) Vol. 45; no. 10; pp. 4493 - 4509
Main Authors Mitra, Debasis, Abdalah, Mahmoud, Boutchko, Rostyslav, Chang, Haoran, Shrestha, Uttam, Botvinick, Elias, Seo, Youngho, Gullberg, Grant T.
Format Journal Article
LanguageEnglish
Published United States American Association of Physicists in Medicine 01.10.2018
Subjects
Animals
BASIC BIOLOGICAL SCIENCES
cardiac imaging
convergence measures in optimization
Dogs
dynamic SPECT reconstruction
Humans
Imaging, Three-Dimensional - methods
INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
matrix factorization
Tomography, Emission-Computed, Single-Photon
matrix factorization
convergence measures in optimization
cardiac imaging
dynamic SPECT reconstruction
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Abstract Purpose Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. Methods SIFADS is different from “pure” factor analysis in dynamic structures (FADS) in that it employs a dedicated spline‐based pre‐initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. Results For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. Conclusion The analysis supports the utility of the pre‐initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
AbstractList Purpose: Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. Methods: SIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. Results: For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. Conclusion: The analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction.PURPOSEDynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction.SIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects.METHODSSIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects.For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS.RESULTSFor SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS.The analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.CONCLUSIONThe analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. SIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. The analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
Purpose Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. Methods SIFADS is different from “pure” factor analysis in dynamic structures (FADS) in that it employs a dedicated spline‐based pre‐initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. Results For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. Conclusion The analysis supports the utility of the pre‐initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
Author Boutchko, Rostyslav
Seo, Youngho
Gullberg, Grant T.
Abdalah, Mahmoud
Botvinick, Elias
Mitra, Debasis
Chang, Haoran
Shrestha, Uttam
AuthorAffiliation 4 University of California San Francisco
2 Moffitt Cancer Center
3 Lawrence Berkeley National Lab
1 Florida Institute of Technology
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convergence measures in optimization
cardiac imaging
dynamic SPECT reconstruction
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Snippet Purpose Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of...
Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being...
Purpose: Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of...
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SubjectTerms Animals
BASIC BIOLOGICAL SCIENCES
cardiac imaging
convergence measures in optimization
Dogs
dynamic SPECT reconstruction
Humans
Imaging, Three-Dimensional - methods
INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
matrix factorization
Tomography, Emission-Computed, Single-Photon
Title Comparison of sparse domain approaches for 4D SPECT dynamic image reconstruction
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmp.13099
https://www.ncbi.nlm.nih.gov/pubmed/30027577
https://www.proquest.com/docview/2073331301
https://www.osti.gov/servlets/purl/1559152
https://pubmed.ncbi.nlm.nih.gov/PMC6211286
Volume 45
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