Effect of block-iterative acceleration on Ga-67 tumor detection in thoracic SPECT

A combination of human localization receiver operating characteristic (LROC) and channelized Hotelling observer (CHO) ROC psychophysical studies were used to investigate how accelerated ordered-subset expectation maximization (OSEM) and rescaled block-iterative (RBI) EM reconstruction affect tumor d...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 2002-02, Vol.49 (1), p.50-55
Main Authors: Gifford, H.C., King, M.A., Narayanan, M.V., Pretorius, P.H., Smyczynski, M.S., Wells, R.G.
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Blocking
Computer simulation
Convergence
Data acquisition
Detectors
Humans
Image quality
Image reconstruction
Imaging phantoms
Iterative algorithms
Iterative methods
Neoplasms
Observers
Photons
Psychology
Psychophysiology
Reconstruction
Strategy
Studies
Tissue
Tumors
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Summary:A combination of human localization receiver operating characteristic (LROC) and channelized Hotelling observer (CHO) ROC psychophysical studies were used to investigate how accelerated ordered-subset expectation maximization (OSEM) and rescaled block-iterative (RBI) EM reconstruction affect tumor detection in simulated Ga-67 SPECT images, The tumors were 1-cm-diameter spheres within the chest region of the three-dimensional mathematical cardiac-torso phantom. Previous work with iterative detector resolution compensation showed that eight iterations of the OSEM algorithm with a subset size of eight (16 subsets) offered optimal observer performance. For the LROC study in this paper, the OSEM and RBI algorithms were implemented using subset sizes P and iterations K that satisfied the relation P=K for P=1, 2, 4, and 8. The CHO was applied to reconstruction strategies that deviated from this relation. Results show that using P/spl les/2 penalized observer performance compared to strategies with larger subset sizes. Other researchers have reported on the more stable convergence and noise properties of the RBI algorithm [(Byrne, 1996) and (Lalush and Tsui, 2000)]. In a similar vein, we found that an RBI strategy with a subset size of P produced the same performance as an OSEM strategy with subset size 2P. As neither algorithm displayed a decisive advantage in speed over the other, we conclude that the RBI algorithm is the better choice for accelerating the Ga-67 reconstructions.
ISSN:0018-9499
1558-1578
DOI:10.1109/TNS.2002.998680