TY - JOUR

T1 - Cross-reference maximum likelihood estimate reconstruction for positron emission tomography

AU - Chen, C. M.

AU - Lu, Horng-Shing

AU - Hsu, Y. P.

PY - 2001/8/25

Y1 - 2001/8/25

N2 - Maximum likelihood estimate (MLE) is a widely used approach for PET image reconstruction. However, it has been shown that reconstructing emission tomography based on MLE without regularization would result in noise and edge artifacts. In the attempt to regularize the maximum likelihood estimate, we propose a new and efficient method in this paper to incorporate the correlated but possibly incomplete structure information which may be derived from expertise, PET systems or other imaging modalities. A mean estimate smoothing the MLE locally within each region of interest is derived according to the boundaries provided by the structure information. Since the boundaries may not be correct, a penalized MLE using the mean estimate is sought. The resulting reconstruction is called a cross-reference maximum likelihood estimate (CRMLE). The CRMLE can be obtained through a modified EM algorithm, which is computation and storage efficient. By borrowing the strength from the correct portion of boundary information, the CRMLE is able to extract the useful information to improve reconstruction for different kinds of incomplete and incorrect boundaries in Monte Carlo studies. The proposed CRMLE algorithm not only reduces the estimation errors, but also preserves the correct boundaries. The penalty parameters can be selected through human interactions or automatically data-driven methods, such as the generalized cross validation method.

AB - Maximum likelihood estimate (MLE) is a widely used approach for PET image reconstruction. However, it has been shown that reconstructing emission tomography based on MLE without regularization would result in noise and edge artifacts. In the attempt to regularize the maximum likelihood estimate, we propose a new and efficient method in this paper to incorporate the correlated but possibly incomplete structure information which may be derived from expertise, PET systems or other imaging modalities. A mean estimate smoothing the MLE locally within each region of interest is derived according to the boundaries provided by the structure information. Since the boundaries may not be correct, a penalized MLE using the mean estimate is sought. The resulting reconstruction is called a cross-reference maximum likelihood estimate (CRMLE). The CRMLE can be obtained through a modified EM algorithm, which is computation and storage efficient. By borrowing the strength from the correct portion of boundary information, the CRMLE is able to extract the useful information to improve reconstruction for different kinds of incomplete and incorrect boundaries in Monte Carlo studies. The proposed CRMLE algorithm not only reduces the estimation errors, but also preserves the correct boundaries. The penalty parameters can be selected through human interactions or automatically data-driven methods, such as the generalized cross validation method.

KW - Cross-validation

KW - Generalized

KW - Generalized EM algorithm

KW - Maximum likelihood estimate

KW - Regularization

UR - http://www.scopus.com/inward/record.url?scp=0035948949&partnerID=8YFLogxK

U2 - 10.4015/S1016237201000248

DO - 10.4015/S1016237201000248

M3 - Article

AN - SCOPUS:0035948949

SN - 1016-2372

VL - 13

SP - 190

EP - 198

JO - Biomedical Engineering - Applications, Basis and Communications

JF - Biomedical Engineering - Applications, Basis and Communications

IS - 4

ER -