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 be 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 (mu) were estimated with the emission intensities (lambda) fixed at their true values. Accurate reconstructions of attenuation parameters were obtained. Second, emission parameters lambda and attenuation parameters mu were simultaneously estimated from the emission data alone. In this case there was crosstalk between estimates of lambda and mu and final estimates of lambda and mu 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.
An iterative algorithm is presented for accelerated reconstruction of cone beam transmission CT data (CBCT). CBCT supplies an attenuation map for SPECT attenuation compensation and anatomical correlation. Iterative algorithms are necessary to reduce truncation artifacts and 3D reconstruction artifacts. An existing transmission maximum-likelihood algorithm (TRML) is accurate but the reconstruction time is too long. The new algorithm is a modified EM algorithm, based on ordered subsets (OSEM). OSEM was evaluated in comparison to TRML using a thorax phantom and a 3D Defrise phantom. A wide range of image measures were evaluated, including spatial resolution, noise, log likelihood, region quantification, truncation artifact removal, and 3D artifact removal. For appropriate subset size, OSEM produced essentially the same image as TRML, but required only one-tenth as many iterations. Thus, adequate images were available in two to four iterations (20-30 min on a SPARC 2 workstation). Further, OSEM still approximately maximizes likelihood: divergence occurs only for very high (and clinically irrelevant) iterations. Ordered subsets are likely to be useful in other geometries (fan and parallel) and for emission CT as well. Therefore, with ordered subsets, high-quality iterative reconstruction is now available in clinically practical reconstructions times.
This paper presents an analysis of two cone beam configurations (having focal lengths of 40 and 60 cm) for the acquisition of single photon emission computed tomography (SPECT) projection data. A three-dimensional filtered backprojection algorithm is used to reconstruct SPECT images of cone beam projection data obtained using Monte Carlo simulations. The mathematical analysis resulted in on-axis point source sensitivities (calculated for a distance of 15 cm from the collimator surface) for cone beam configurations that were 1.4-3 times the sensitivities of parallel-hole and fan beam geometries having similar geometric resolutions. Cone beam collimation offers the potential for improved sensitivity for SPECT devices using large-field-of-view scintillation cameras.
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