A Novel Methodology for Iterative Image Reconstruction in SPECT Using Deterministic Particle Transport.
Degree: PhD, Nuclear Engineering, 2015, Virginia Tech
Single photon emission computed tomography (SPECT) is used in a variety of medical procedures, including myocardial perfusion, bone metabolism, and thyroid function studies. In SPECT, the emissions of a radionuclide within a patient are counted at a gamma camera to form a 2-dimensional projection of the 3-dimensional radionuclide distribution within the patient. This unknown 3-dimensional source distribution can be reconstructed from many 2-dimensional projections obtained at different angles around the patient. This reconstruction can be improved by properly modeling the physics in the patient, i.e., particle absorption and scattering. Currently, such modeling is done using statistical Monte Carlo methods, but deterministic codes have the potential to offer fast computation speeds while fully modeling particle interactions within the patient. Deterministic codes are not susceptible to statistical uncertainty, but have been over-looked for applications to nuclear medicine, most likely due to their own limitations, including discretization and large memory requirements.
A novel deterministic reconstruction methodology for SPECT (DRS) has been developed to apply the advantages of deterministic algorithms to SPECT iterative image reconstruction. Using a maximum likelihood expectation maximization (ML-EM) algorithm, a deterministic code can fully model particle transport in the patient in the forward projection step, without the need of a large system matrix. The TITAN deterministic transport code has a SPECT formulation that allows for fast simulation of SPECT projection images and has been benchmarked through comparison with results from the SIMIND and MCNP5 Monte Carlo codes in this dissertation. The TITAN SPECT formulation has been improved through a modified collimator representation and full parallelization. The DRS methodology has been implemented in the TITAN code to create TITAN with Image Reconstruction (TITAN-IR). The TITAN-IR code has been used to successfully reconstruct the source distribution from SPECT data for the Jaszczak and NCAT phantoms. Extensive studies have been conducted to examine the sensitivity of TITAN-IR image quality to deterministic parameter selection as well as collimator blur and noise in the projection data being reconstructed. The TITAN-IR reconstruction has also been compared with other reconstruction algorithms. This novel image reconstruction methodology has been shown to reconstruct images in short computation times, demonstrating its potential in a clinical setting with further development.
Advisors/Committee Members: Haghighat, Alireza (committeechair), Hin, Celine (committee member), Wong, Kenneth H. (committee member), Wang, Yue J. (committee member), Paul, Mark R. (committee member), Yi, Ce (committee member).
Subjects/Keywords: deterministic transport; SPECT; image reconstruction
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APA (6th Edition):
Royston, K. (2015). A Novel Methodology for Iterative Image Reconstruction in SPECT Using Deterministic Particle Transport. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73317
Chicago Manual of Style (16th Edition):
Royston, Katherine. “A Novel Methodology for Iterative Image Reconstruction in SPECT Using Deterministic Particle Transport.” 2015. Doctoral Dissertation, Virginia Tech. Accessed October 16, 2019.
MLA Handbook (7th Edition):
Royston, Katherine. “A Novel Methodology for Iterative Image Reconstruction in SPECT Using Deterministic Particle Transport.” 2015. Web. 16 Oct 2019.
Royston K. A Novel Methodology for Iterative Image Reconstruction in SPECT Using Deterministic Particle Transport. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Oct 16].
Available from: http://hdl.handle.net/10919/73317.
Council of Science Editors:
Royston K. A Novel Methodology for Iterative Image Reconstruction in SPECT Using Deterministic Particle Transport. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/73317