Penalized maximum-likelihood image reconstruction for lesion detection

Published on Aug 21, 2006in Physics in Medicine and Biology3.609
路 DOI :10.1088/0031-9155/51/16/009
Jinyi Qi53
Estimated H-index: 53
(UC Davis: University of California, Davis),
Ronald H. Huesman33
Estimated H-index: 33
(LBNL: Lawrence Berkeley National Laboratory)
Sources
Abstract
Detecting cancerous lesions is one major application in emission tomography. In this paper, we study penalized maximum-likelihood image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modelling the photon detection process and measurement noise in imaging systems. To explore the full potential of penalized maximum-likelihood image reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters to improve lesion detectability. We conducted computer-based Monte Carlo simulations to compare the proposed penalty function, conventional penalty function, and a penalty function for isotropic point spread function. The lesion detectability is measured by a channelized Hotelling observer. The results show that the proposed penalty function outperforms the other penalty functions for lesion detection. The relative improvement is dependent on the size of the lesion. However, we found that the penalty function optimized for a 5 mm lesion still outperforms the other two penalty functions for detecting a 14 mm lesion. Therefore, it is feasible to use the penalty function designed for small lesions in image reconstruction, because detection of large lesions is relatively easy.
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References39
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#1Jinyi Qi (University of California, Berkeley)H-Index: 53
Statistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior is the Gaussian prior, whose logarithm has a quadratic form. Gaussian priors are relatively easy to analyze. It has been shown that the effect of a Gaussian prior can be approximated by linear-filtering a maximum likelihood (ML) reconstruction. As ...
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#1J. Oldan (SUNY: State University of New York System)H-Index: 1
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We compared the performance of a channelized Hotelling observer (CHO) to that of human observers to determine an optimal smoothing parameter /spl beta/ for an SKE/BKE detection task in a SPECT MAP (maximum a posteriori) reconstruction. The study is motivated in part by the recent development of theoretical methods that can rapidly predict CHO signal-to-noise ratios (SNRs) for MAP reconstructions. We found that a CHO not adjusted for internal noise effects was less predictive of the optimal smoot...
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#1Jinyi Qi (LBNL: Lawrence Berkeley National Laboratory)H-Index: 53
Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is built on the recent progress on the theoretical analysis of image properties of statistical re...
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#1J.W. Stayman (UM: University of Michigan)H-Index: 3
#2Jeffrey A. Fessler (UM: University of Michigan)H-Index: 79
Imaging systems that form estimates using a statistical approach generally yield images with nonuniform resolution properties. That is, the reconstructed images possess resolution properties marked by space-variant and/or anisotropic responses. We have previously developed a space-variant penalty for penalized-likelihood (PL) reconstruction that yields nearly uniform resolution properties . We demonstrated how to calculate this penalty efficiently and apply it to an idealized positron emission t...
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#1Yuxiang Xing (SBU: Stony Brook University)H-Index: 18
#2Ing-Tsung Hsiao (CGU: Chang Gung University)H-Index: 26
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We consider the calculation of lesion detectability using a mathematical model observer, the channelized Hotelling observer (CHO), in a signal-known-exactly/background-known-exactly detection task for single photon emission computed tomography (SPECT). We focus on SPECT images reconstructed with Bayesian maximum a posteriori methods. While model observers are designed to replace time-consuming studies using human observers, the calculation of CHO detectability is usually accomplished using a lar...
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#1Jinyi Qi (LBNL: Lawrence Berkeley National Laboratory)H-Index: 53
Iterative image estimation methods have been widely used in emission tomography. Accurate estimate of the uncertainty of the reconstructed images is essential for quantitative applications. While theoretical approach has been developed to analyze the noise propagation from iteration to iteration, the current results are limited to only a few iterative algorithms that have an explicit multiplicative update equation. This paper presents a theoretical noise analysis that is applicable to a wide ran...
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#1Jeffrey A. Fessler (UM: University of Michigan)H-Index: 79
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#1Jinyi Qi (LBNL: Lawrence Berkeley National Laboratory)H-Index: 53
#2Ronald H. Huesman (LBNL: Lawrence Berkeley National Laboratory)H-Index: 33
The low signal-to-noise ratio (SNR) in emission data has stimulated the development of statistical image reconstruction methods based on the maximum a posteriori (MAP) principle. Experimental examples have shown that statistical methods improve image quality compared to the conventional filtered backprojection (FBP) method. However, these results depend on isolated data sets. Here, the authors study the lesion detectability of MAP reconstruction theoretically, using computer observers. These the...
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The maximum-likelihood (ML) approach in emission tomography provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. The expectation-maximization (EM) algorithm is an iterative algorithm for maximizing the Poisson likelihood in emission computed tomography that became very popular for solving the ML problem because of its attractive theoretical and practical properties. Recently, (Browne and DePierro, 1996 and Hudson and Larkin, 1991)...
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