| Assessment of a three-dimensional line-of-response probability density function system matrix for PET. | |
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MedLine Citation:
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PMID: 23032702 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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To achieve optimal PET image reconstruction through better system modeling, we developed a system matrix that is based on the probability density function for each line of response (LOR-PDF). The LOR-PDFs are grouped by LOR-to-detector incident angles to form a highly compact system matrix. The system matrix was implemented in the MOLAR list mode reconstruction algorithm for a small animal PET scanner. The impact of LOR-PDF on reconstructed image quality was assessed qualitatively as well as quantitatively in terms of contrast recovery coefficient (CRC) and coefficient of variance (COV), and its performance was compared with a fixed Gaussian (iso-Gaussian) line spread function. The LOR-PDFs of three coincidence signal emitting sources, (1) ideal positron emitter that emits perfect back-to-back γ rays (γγ) in air; (2) fluorine-18 (¹⁸F) nuclide in water; and (3) oxygen-15 (¹⁵O) nuclide in water, were derived, and assessed with simulated and experimental phantom data. The derived LOR-PDFs showed anisotropic and asymmetric characteristics dependent on LOR-detector angle, coincidence emitting source, and the medium, consistent with common PET physical principles. The comparison of the iso-Gaussian function and LOR-PDF showed that: (1) without positron range and acollinearity effects, the LOR-PDF achieved better or similar trade-offs of contrast recovery and noise for objects of 4 mm radius or larger, and this advantage extended to smaller objects (e.g. 2 mm radius sphere, 0.6 mm radius hot-rods) at higher iteration numbers; and (2) with positron range and acollinearity effects, the iso-Gaussian achieved similar or better resolution recovery depending on the significance of positron range effect. We conclude that the 3D LOR-PDF approach is an effective method to generate an accurate and compact system matrix. However, when used directly in expectation-maximization based list-mode iterative reconstruction algorithms such as MOLAR, its superiority is not clear. For this application, using an iso-Gaussian function in MOLAR is a simple but effective technique for PET reconstruction. |
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Authors:
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Rutao Yao; Ranjith M Ramachandra; Neeraj Mahajan; Vinay Rathod; Noel Gunasekar; Ashish Panse; Tianyu Ma; Yiqiang Jian; Jianhua Yan; Richard E Carson |
Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural Date: 2012-10-03 |
Journal Detail:
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Title: Physics in medicine and biology Volume: 57 ISSN: 1361-6560 ISO Abbreviation: Phys Med Biol Publication Date: 2012 Nov |
Date Detail:
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Created Date: 2012-10-17 Completed Date: 2013-03-18 Revised Date: 2013-05-08 |
Medline Journal Info:
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Nlm Unique ID: 0401220 Medline TA: Phys Med Biol Country: England |
Other Details:
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Languages: eng Pagination: 6827-48 Citation Subset: IM |
Affiliation:
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Department of Nuclear Medicine, University at Buffalo, SUNY, Buffalo, NY 14214, USA. rutaoyao@buffalo.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Animals Imaging, Three-Dimensional / methods* Monte Carlo Method Phantoms, Imaging Photons Positron-Emission Tomography / instrumentation, methods* Probability Sensitivity and Specificity |
| Grant Support | |
ID/Acronym/Agency:
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1R01NS058360/NS/NINDS NIH HHS; R01 NS058360/NS/NINDS NIH HHS; UL1 TR000142/TR/NCATS NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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