A major step towards quantitative SPECT imaging may be achieved if attenuation, scatter and blurring effects are accounted for in the reconstruction process. Here we consider an approach which simultaneously estimates the unknown attenuation coefficient and the emission source using the emission data only. This leads to an inverse mathematical problem which could no longer be solved via iterative procedures like the well-known EM-algorithm. Instead, a regularization approach based on nonlinear optimization techniques is used. We present a successful strategy of the analytic type, and we test it in a simulated case study.

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A major step towards quantitative SPECT imaging may be achieved if attenuation, scatter and blurring effects are accounted for in the reconstruction process. Here we consider an approach which simultaneously estimates the unknown attenuation coefficient and the emission source using the emission data only. This leads to an inverse mathematical problem which could no longer be solved via iterative procedures like the well-known EM-algorithm. Instead, a regularization approach based on nonlinear optimization techniques is used. We present a successful strategy of the analytic type, and we test it in a simulated case study.

Single photon emission computed tomography imaging suffers from poor spatial resolution and high statistical noise. Consequently, the contrast of small structures is reduced, the visual detection of defects is limited and precise quantification is difficult. To improve the contrast, it is possible to include the spatially variant point spread function of the detection system into the iterative reconstruction algorithm. This kind of method is well known to be effective, but time consuming. We have developed a faster method to account for the spatial resolution loss in three dimensions, based on a postreconstruction restoration method. The method uses two steps. First, a noncorrected iterative ordered subsets expectation maximization (OSEM) reconstruction is performed and, in the second step, a three-dimensional (3D) iterative maximum likelihood expectation maximization (ML-EM) a posteriori spatial restoration of the reconstructed volume is done. In this paper, we compare to the standard OSEM-3D method, in three studies (two in simulation and one from experimental data). In the two first studies, contrast, noise, and visual detection of defects are studied. In the third study, a quantitative analysis is performed from data obtained with an anthropomorphic striatal phantom filled with 123-I. From the simulations, we demonstrate that contrast as a function of noise and lesion detectability are very similar for both OSEM-3D and OSEM-R methods. In the experimental study, we obtained very similar values of activity-quantification ratios for different regions in the brain. The advantage of OSEM-R compared to OSEM-3D is a substantial gain of processing time. This gain depends on several factors. In a typical situation, for a 128 x 128 acquisition of 120 projections, OSEM-R is 13 or 25 times faster than OSEM-3D, depending on the calculation method used in the iterative restoration. In this paper, the OSEM-R method is tested with the approximation of depth independent resolution. For the striatum this approximation is appropriate, but for other clinical situations we will need to include a spatially varying response. Such a response is already included in OSEM-3D.

Objective: To assess the feasibility of synthesis of O-(2-[ 18 F]-fluoroethyl)-L-tyrosine (FET), a new positron emission tomographic (PET) tracer described in several studies but not yet considered standard in management of glioma, in routine practice and to determine FET uptake in a homogeneous group of patients with suspected high-grade glioma.Design: Prospective nonrandomized trial.Patients: Twelve patients with suspicion of high-grade glioma.Results: The mean (SD) FET uptake ratio was 3.15 (0.72) for the 12 patients and 3.16(0.75) for the 11 patients with glioblastoma. Conclusion:The initial results are promising and indicate that FET PET is a valuable and applicable tool for the imaging of high-grade glioma.