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An EM algorithm for estimating SPECT emission and transmission parameters from emission data only

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6 Author(s)
Krol, A. ; Dept. of Radiol., State Univ. of New York, Syracuse, NY, USA ; Bowsher, J.E. ; Manglos, S.H. ; Feiglin, D.H.
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A maximum-likelihood (ML) expectation-maximization (EM) algorithm (called EM-IntraSPECT) is presented for simultaneously estimating single photon emission computed tomography (SPECT) emission and attenuation parameters from emission data alone. The algorithm uses the activity within the patient as transmission tomography sources, with which attenuation coefficients can he estimated. For this initial study, EM-IntraSPECT was tested on computer-simulated attenuation and emission maps representing a simplified human thorax as well as on SPECT data obtained from a physical phantom. Two evaluations were performed. First, to corroborate the idea of reconstructing attenuation parameters from emission data, attenuation parameters (μ) were estimated with the emission intensities (λ) fixed at their true values. Accurate reconstructions of attenuation parameters were obtained. Second, emission parameters λ and attenuation parameters Cl were simultaneously estimated from the emission data alone. In this case there was crosstalk between estimates of λ and μ and final estimates of λ and μ depended on initial values. Estimates degraded significantly as the support extended out farther from the body, and an explanation for this is proposed. In the EM-IntraSPECT reconstructed attenuation images, the lungs, spine, and soft tissue were readily distinguished and had approximately correct shapes and sizes. As compared with standard EM reconstruction assuming a fix uniform attenuation map, EM-IntraSPECT-provided more uniform estimates of cardiac activity in the physical phantom study and in the simulation study with tight support, but less uniform estimates with a broad support. The new EM algorithm derived here has additional applications, including reconstructing emission and transmission projection data under a unified statistical model.

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Medical Imaging, IEEE Transactions on  (Volume:20 ,  Issue: 3 )