Title
Generalization of the image space reconstruction algorithm Generalization of the image space reconstruction algorithm
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
bookPart
Publication
New York, N.Y. :IEEE, [*]
Subject
Physics
Engineering sciences. Technology
Computer. Automation
Source (book)
18th IEEE Nuclear Science Symposium/Medical Imaging Conference (NSS/MIC), International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors, October 23-29, 2011, Valencia, Spain
ISBN
978-1-4673-0120-6
ISI
000304755604099
Carrier
E
Target language
English (eng)
Abstract
The image space reconstruction algorithm (ISRA) has been shown to be a non-negative least squares estimator, and was introduced as an alternative iterative image reconstruction method for positron emission tomography (PET) data. The implementation of ISRA is straightforward: the ratio of the backprojected measured data to that of the backprojected expected data is used to multiplicatively update the current image estimate. This work starts with a modified weighted least squares objective function to derive a more general form of the ISRA algorithm, which importantly accommodates weighting of the backprojection. Simply by changing the choice of backprojection weighting factors at a given iteration, both the well known ML-EM (maximum likelihood expectation maximization) algorithm as well as the standard ISRA, are obtained as special cases. ML-EM corresponds to using the current estimate of the expected data as the weights for backprojection, and ISRA corresponds to the case of unit weighting during backprojection. Of particular interest however, is that the framework naturally suggests the existence of many alternative reconstruction algorithms through alternative data weighting choices. By changing the weighting factors, a performance improvement over ISRA is obtained, as well as a slight performance improvement compared to ML-EM (for the task of accurate region quantification which is considered in this work). Specifically, these improvements are obtained, for example, by using a spatially-smoothed copy of the measured data as weighting factors during backprojection.
E-info
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