On the convergence of the maximum likelihood estimator method of tomographic image reconstruction
The Maximum Likelihood Estimator (MLE) method of image reconstruction has been reported to exhibit image deterioration in regions of expected uniform activity as the number of iterations increases beyond a certain point. This apparent instability raises questions as to the usefulness of a method that yields images at different stages of the reconstruction that could have different medical interpretations. In this paper we look in some detail into the question of convergence of MLE solutions at a large number of iterations and show that the MLE method converges towards the image that it was designed to yield, i.e., the image which has the maximum likelihood to have generated the specific projection data resulting from a measurement. We also show that the maximum likelihood image can be a very deteriorated version of the true source image and that only as the number of counts in the projection data becomes very high, will the maximum likelihood image converge towards an acceptable reconstruction.
- Research Organization:
- Lawrence Berkeley Lab., CA (USA); California Univ., Los Angeles (USA). Dept. of Radiological Sciences
- DOE Contract Number:
- AC03-76SF00098
- OSTI ID:
- 6405066
- Report Number(s):
- LBL-21800; CONF-870269-1; ON: DE87006935
- Resource Relation:
- Conference: Society of Photo-Optical Instrumentation Engineers' medical imaging I conference, Newport Beach, CA, USA, 1 Feb 1987; Other Information: Portions of this document are illegible in microfiche products
- Country of Publication:
- United States
- Language:
- English
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