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

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 SPECT (Single Photon Emission Computed Tomography) 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” 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: Most metrics analyzed for reconstruction algorithm convergence show better features of the metric curves versus 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.

Research Progress of Imaging Methods for Detection of Microvascular Angina Pectoris in Diabetic Patients

“…Because the camera rotates slowly, the dynamic data must be reconstructed from the projection, and the attenuation should be corrected appropriately. Therefore, Debasis Mitra developed a Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) algorithm ( 41 ), which performed well in animal and human experiments and significantly reduced image noise and improved image quality. In addition, deep learning technology is developing rapidly and shows excellent potential in image acquisition and processing, which may be widely used in the future ( 42 ).…”

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

“…1,2 Many CHPs incorporate detailed anatomy and physiological functions, such as respiratory and cardiac motion and find application in com-puted tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Uses of these phantoms include internal and external dosimetry, [2][3][4][5][6][7][8][9] the development and testing of novel image reconstruction algorithms (e.g., motion compensation, artifact suppression, sparse reconstruction), 1,[10][11][12][13][14][15] image acquisition techniques (e.g., artifact avoidance, collimator and detector optimization), 1,16,17 post-processing techniques (e.g., noise reduction), 1,18,19 and more. 20 Furthermore, the Quantitative Imaging Biomarker Alliance (QIBA) has used the term digital reference object (DRO) for the use of CHPs and other phantoms to establish a minimum performance requirement for quantitative imaging algorithms and reduce inter-scanner variability.…”