Introduction: Ordered subset expectation maximization (OSEM), is an effective iterative method for SPECT image reconstruction. The aim of this study is the evaluation of the role of system matrix in OSEM image reconstruction method using four different physical beam radiation models with three detection configurations.Methods: SPECT was done with an arc of 180 degree in 32 projections after injection of 2 mCi of 99mTc-pertechnetate in a heart phantom by a Siemens E.Cam gamma camera equipped with LEHR collimator and data were transferred to a PC computer for reconstruction of the images with Mathlab software. The system or probability matrixes were firstly calculated using radiation fraction of pixels for three different detection models with linear, rectangular and divergent FOV, and reduction coefficient of photons from pixels to detectors in four different radiation models of distance independent (DID), inverse distance dependence (IDD) [@1/R], inverse square distance dependence (ISDD),[ @1/R2] and inverse exponential distance dependence(IEDD),[ @exp-R]. In these calculations the detector was assumed at a distance of 842 mm from the phantom center and pixel size was 6.638 mm. The divergent angle in divergent field of view was 2.08 degree. 12 Images of the phantom were reconstructed using system matrixes of 4 different radiation and 3 detection models. Qualities of the images were compared using universal image quality index, UIQI.Results: The results shows negligible although statistically significant difference between contrast and brightness of the images, but it is possible in the organs with constant absorption coefficients such as brain, to use the system matrix with mathematical IEDD radiation model for attenuation correction in SPECT images. It is shown that variation in distance weighting factors in mathematical IEDD radiation model changes the system matrix so that the weights of deeper data decrease in image reconstruction process. Therefore, by this method contrast of the image at different depth can be controlled.Conclusions: Applying different beam radiation models and detection configurations in system matrix has no significant improvements on the image quality. However image contrast at different depth can be controlled by using system matrix derived from different distance weighting factor in mathematical IEDD radiation model.

Background: The image quality of PET/CT is affected by various factors which include technical errors, Biological factors, and Physical factors. The errors due to physical factors can be reduced by using standardized image reconstruction parameters. The choice of reconstruction parameters is a trade‐off between resolution and noise and has a major impact on the performance of clinically important tasks such as lesion detection. Moreover, these image reconstruction parameters are highly specific to manufacturer and scanner type thus making generalizations difficult.Materials and Methods: PET/CT scan of Deluxe Jaszczak phantom filled with water having 74 MBq F‐ 18 FDG was acquired on Biograph mCT (Siemens Healthcare) PET/CT scanner. PET scan was acquired in one bed for 5 minutes. Transverse slices were reconstructed using OSEM reconstruction method by varying FWHM from 1mm to 8 mm and for each FWHM, iterations (IT) varied from 1 to 10. The image quality of 240 mages was assessed by two nuclear medicine physicians based on 4 point scale.Results: At constant iteration, increase in FWHM resulted in smooth images, but gradual decrease in image quality seen because of edge blurring. At constant FWHM, increase in number of iteration improved the contrast in the image but at the cost of inclusion of noise.Conclusion: Optimized value of iteration and FWHM were found to be 4 and 2 mm respectively.

Objective(s): Various iterative reconstruction algorithms in nuclear medicine have been introduced in the last three decades. For each new imaging system, it is wise to select appropriate image reconstruction algorithms and evaluate their performance. In this study, three approaches of image reconstruction were developed for a novel desktop open-gantry SPECT system, PERSPECT, to assess their performance in terms of the quality of the resultant reconstructed images.Methods: In the present work, a proposed image reconstruction algorithm for the PERSPECT, referred to as quasi-simultaneous multiplicative algebraic reconstruction technique (qSMART), together with two popular image reconstruction methods, maximum-likelihood expectation-maximization (MLEM) and ordered-subsets EM (OSEM), were implemented and compared. Analytic and Monte Carlo simulations were applied for data acquisition of various phantoms including a micro-Derenzo phantom. All acquired data were reconstructed by the three algorithms using different number of iterations (1-40). A thorough set of figures-of-merit was utilized to quantitatively compare the generated images.Results: OSEM depicted reconstructed images of higher (or matching) quality in comparison to qSMART. MLEM also reached nearly similar quality as OSEM but at higher number of iterations. The graph of data discrepancy revealed that the ranking of the three approaches in terms of convergence speed is as qSMART, OSEM, and MLEM. Furthermore, bias-versus-noise curves indicated that optimal bias-noise results were achieved using OSEM.Conclusion: The results showed that although qSMART can be applied for image reconstruction if being halted in the early iterations (up to 5), the best achievable quality of images is obtained using the OSEM.

Objective(s): We evaluated edge artifacts in relation to phantom diameter and reconstruction parameters in point spread function (PSF) -based positron emission tomography (PET) image reconstruction.Methods: PET data were acquired from an original cone-shaped phantom filled with 18F solution (21.9 kBq/mL) for 10 min using a Biograph mCT scanner. The images were reconstructed using the baseline ordered subsets expectation maximization (OSEM) algorithm and the OSEM with PSF correction model. The reconstruction parameters included a pixel size of 1.0, 2.0, or 3.0 mm, 1-12 iterations, 24 subsets, and a full width at half maximum (FWHM) of the post-filter Gaussian filter of 1.0, 2.0, or 3.0 mm. We compared both the maximum recovery coefficient (RCmax) and the mean recovery coefficient (RCmean) in the phantom at different diameters.Results: The OSEM images had no edge artifacts, but the OSEM with PSF images had a dense edge delineating the hot phantom at diameters 10 mm or more and a dense spot at the center at diameters of 8 mm or less. The dense edge was clearly observed on images with a small pixel size, a Gaussian filter with a small FWHM, and a high number of iterations. At a phantom diameter of 6-7 mm, the RCmax for the OSEM and OSEM with PSF images was 60% and 140%, respectively (pixel size: 1.0 mm; FWHM of the Gaussian filter: 2.0 mm; iterations: 2). The RCmean of the OSEM with PSF images did not exceed 100%.Conclusion: PSF-based image reconstruction resulted in edge artifacts, the degree of which depends on the pixel size, number of iterations, FWHM of the Gaussian filter, and object size.

Introduction: Quantitative evaluation is recommended to improve diagnostic ability and serial assessment of dopamine transporter (DAT) density scans. We decided to compare the ordered subsets expectation-maximization (OSEM) with filtered back-projection (FBP), and to investigate the impact of different iteration and cut-off frequencies on SBR values. Methods: We retrospectively examined 27 consecutive patients. SPECT reconstruction was performed using OSEM and FBP with Chang’ s attenuation correction (AC). Iterative reconstruction parameters were used with different iterations ranging from 2, 4, 6, 8, and 10 with fixed 10 subsets and different subsets including 5, 10 and 15 with fixed 6 iterations. Reconstruction with FBP were performed with different critical cut-off frequencies of 0. 3, 0. 4 and 0. 5. Results: Comparing SBR derived by OSEM reconstruction with 10 subsets but different iterations revealed statistically significant intraclass correlation (ICC) in both right and left side. There is also no significant difference between different OSEM reconstruction with different subsets and ICC was excellent in all patients. ICC for FBP reconstruction with different cut-off frequency revealed good ICC in all patients. However, lower degree of SBR showed higher decrease in ICC with insignificant and poor correlation in patients with SBR<0. 2. While comparing OSEM and FBP, good correlation was observed in total patients, there was poor correlation between these reconstruction methods in lower SBR values. Conclusion: Our study showed that change in FBP reconstruction parameters can greatly impact the SBR value of 99mTc-TRODAT-1, especially in patients with more severe disease. However, OSEM reconstruction revealed better reproducibility for SBR using different iterations.

Objective(s): The aim of this study was to investigate the effect on standardized uptake value (SUV) measurement variability of the positional relationship between objects of different sizes and the pixel of a positron emission tomography (PET) image. Methods: We used a NEMA IEC body phantom comprising six spheres with diameters of 10, 13, 17, 22, 28, and 37 mm. The phantom was filled with 18F solution and contained target-to-background ratios (TBRs) of 2, 4, and 8. The PET data were acquired for 30 min using a SIGNA PET/MR scanner. The PET images were reconstructed with the ordered subsets expectation maximization (OSEM) algorithm with and without point-spread function (PSF) correction (OSEM + PSF + Filter and OSEM + Filter, respectively). A Gaussian filter of 4 mm full width at half maximum was applied in all reconstructions, except for one model (OSEM + PSF + no Filter). The matrix sizes were 128×128, 192×192, 256×256 and 384×384. Reconstruction was performed by shifting the reconstruction center position by 1 mm in the range 0 to 3 mm in the upward or rightward direction for each parameter. For all reconstructed images, the SUVmax of each hot sphere was measured. To investigate the resulting variation in the SUVmax, the coefficient of variation (CV) of each SUVmax was calculated. Results: The CV of the SUVmax increased as the matrix size and the diameter of the hot sphere decreased in all reconstruction settings. With PSF correction, the CV of SUVmax increased as the TBR increased except when the TBR was 2. The CV of the SUVmax measured in the OSEM + PSF + no Filter images were larger than those measured in the OSEM + PSF + Filter images. The amount of this increase was higher for smaller spheres and larger matrix sizes and was independent of TBR. Conclusions: Shifting the reconstruction center position of the PET image causes variability in SUVmax measurements. To reduce the variability of SUV measurements, it is necessary to use sufficient matrix sizes to satisfy sampling criterion and appropriate filters.