Document Detail


Assessment of a three-dimensional line-of-response probability density function system matrix for PET.
MedLine Citation:
PMID:  23032702     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Rutao Yao; Ranjith M Ramachandra; Neeraj Mahajan; Vinay Rathod; Noel Gunasekar; Ashish Panse; Tianyu Ma; Yiqiang Jian; Jianhua Yan; Richard E Carson
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2012-10-03
Journal Detail:
Title:  Physics in medicine and biology     Volume:  57     ISSN:  1361-6560     ISO Abbreviation:  Phys Med Biol     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-10-17     Completed Date:  2013-03-18     Revised Date:  2014-05-01    
Medline Journal Info:
Nlm Unique ID:  0401220     Medline TA:  Phys Med Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  6827-48     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
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:
1R01NS058360/NS/NINDS NIH HHS; R01 NS058360/NS/NINDS NIH HHS
Comments/Corrections

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