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

• Published in: Medical 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
• Language: English
• 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
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.13099

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.