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.

Background & Aims: Nowadays, imaging of the blood supply of the heart muscle by single photon emission computed tomography (SPECT: Single Photon Emission Computed Tomography) due to its non-invasive nature and providing information with physiological value and low cost compared to the valuable angiography method. It is highly diagnostic. But these images undergo changes and artifacts under the influence of factors, the result of which is the reduction of the diagnostic accuracy of the images and false positive cases. During the detection process, several physical effects such as attenuation, scattering, and collimator response function affect the frequency of emitted photons,this leads to the destruction of the contrast and as a result of reducing the quantitative and qualitative accuracy of the images. Attenuation, as the most destructive factor of SPECT images, reduces the quality of SPECT images of heart blood supply and reduces the sensitivity of tests related to the diagnosis of coronary artery diseases, and for non-uniform environments, especially in nuclear imaging of chest areas. And the heart is necessary to produce a map of patient attenuation coefficients. The existence of scattered photons is also one of the main factors of error in quantization,the detection of scattered events affects the contrast of the lesions and causes the lack of image resolution and signal-to-noise ratio. Therefore, to correct the attenuation and scattering of the rays in the heart images quantitatively and qualitatively, patterns are needed in SPECT systems. Due to the importance of the topic, various research groups around the world have presented their research and results on correcting the effect of scattering of rays and also correcting the effect of weakening the rays. If there was no limitation of energy resolution, it was easily possible to identify the scattered rays and prevent them from being recorded in the image. Because we know that scattered rays lose energy. Because gamma rays are single energy and their energy amount is completely known. Therefore, each photon with less energy will represent scattered rays, but due to the limited energy resolution of the gamma camera, a range is usually considered on the sides of the main energy, which is called the energy window. It is assumed that the photons recorded in this energy range are primary photons, but in fact, many photons scattered in the body are also recorded in this window. These scattered rays do not carry correct spatial information and lead to a decrease in image resolution and contrast and quantization errors in the image. In nuclear medicine, instead of researching and examining the patient or processing the image of the patient, simulated images can be examined. Simulators can provide information about each of the image destruction factors. The purpose of this research is to propose a new method for scattering correction, in this research, a combination of Monte Carlo and modeling is used for the rapid production of scattered views, and in the proposed method, the two-matrix method is used, this method At the stage of generating mathematical views, dispersion is added and this problem leads to the removal of scattered rays. As a result, an image is reconstructed that is free from the effects of attenuation and non-ideal dispersion and leads to an increase in contrast and improvement of power. Detecting waste, increasing the signal-to-noise ratio, and increasing the accuracy of quantification. Methods: In this study, the effect of applying attenuation and dispersion correction using two energy windows (DEW) and three energy windows (TEW) methods in cardiac aspect imaging was investigated and evaluated, and to simulate cardiac aspect imaging, a special code similar to SAR Monte Carlo GATE was used as the SPECT imaging system and XCAT digital phantom with activity distribution and realistic attenuation map was used to model the human trunk. Results: Comparison of image contrast improvement in different modes of attenuation and dispersion correction shows that the highest image contrast is obtained from the (TEW1+AC) method with an average increase of 25% and MSE in different modes of attenuation correction. And the dispersion compared to the reference image was reduced from 51. 5% to 54. 5%. Compared to the reference image, MSE decreased from 1. 4 in Un_Cor to 1. 15, 1. 13, 1. 12, and 1. 14 in AC+TEW1, AC+DEW, AC, and AC+TEW2, respectively, and the signal-to-noise ratio (SNR) increased up to 71% in all methods of applying dispersion correction along with attenuation correction compared to applying attenuation correction (AC). Conclusion: In this study, the effect of attenuation and dispersion correction in 5 non-correction modes, with attenuation correction, attenuation, and dispersion correction using two-window and three-window methods with triangular approximation and three-window with trapezoidal approximation on We evaluated XCAT phantom simulated images and heart muscle perfusion images by SPECT method and 4 different parameters were used to compare and evaluate the images, including profile, contrast, mean squared error (MSE) and signal to noise. According to the results of the quantification of reconstructed images, it is possible to apply dispersion correction along with attenuation correction.

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: Motion of the patient during myocardial perfusion SPECT could potentially results in false perfusion defects. The effect of different reconstruction methods on these artifacts is not studied. Clarification of the relation between the extent, severity and duration of motion with the resultant artifacts may be helpful in designing special soft wares for motion correction. This study is a preliminary evaluation of motion artifacts in different reconstruction methods. Materials and Methods: Normal myocardial perfusion SPECT from a patient with low risk of CAD and no motion during imaging according to the review of cinematic images by 3 nuclear medicine specialists is selected. The scan was acquired one hour after IV injection of 740MBq of Tc-99m-MIBI through 180 degrees from RAO 45 to LPO 45, in 32 frames and 30 seconds per frame. The original images were moved artificially in frames 7, 16 and 24 (as initial, mid and late portion of imaging) from 1 to 3 pixels and for 30-90 seconds using VISION software. Also this artificial motion was applied in X and Y axis and in negative and positive directions. One hundred forty four images were produced and all were reconstructed using filtered back projection (FBP) and iterative (OSEM) reconstructions and interpreted visually and semi-quantitatively (using 17 segment model with 5 point scoring). Results: Applying one, two and three pixels motion converted normal scan to abnormal image in 2.8%, 25% and 39.6% respectively using FBP and in 18.8% , 43.2% and 68.8% respectively using OSEM reconstruction.(P<0.001). Correlation of interpretation (Kappa) for two sets of images (FBP vs OSEM) was 0.488. Summed score (SS) for each scan reflects the intensity and severity of the defects. Overall, mean summed score was 5.25 in FBP and 8.08 in OSEM reconstruction (P<0.05). Mean SS in returning motions for 1, 2 and 3 frames(corresponding to 30, 60 and 90 seconds) were 2.52, 6 and 8.1 using FBP respectively and 3.85 , 8.77 and 11.58 in OSEM respectively (ANOVA, P<0.001). With non-returning motions no normal image was noted in both reconstruction methods. Conclusion: This study showed that OSEM reconstruction is more sensitive for motion artifacts compared to FBP. Also severity and extent of the defects were larger in OSEM reconstruction compared to FBP. Severity of the artifacts is increased with increasing duration of motion (or number of frames involved).

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.

Background: The present study aimed to investigate the agreement and convergence of left ventricular dyssynchrony parameters extracted from phase analysis using GSPECT images under different conditions of filtration and reconstruction. Methods: The study population included 120 consecutive patients with normal or abnormal GSPECT MPI. All patients underwent a 2-day rest and stress sestamibi GSPECT MPI. The GSPECT images were reconstructed and processed using reconstruction methods including filtered back projection (FBP) with Butterworth (cutoff =0. 45, order =5) and Metz (cutoff =0. 9, order =6) filters and ordered-subset expectation maximization (OSEM) (subset=4, 16,iteration =8) with Gaussian filters. Phase analysis (PA) parameters were evaluated globally and regionally (anterior, inferior, septum, lateral, and apex) in patients with normal MPI and those with abnormal MPI. Results: According to intraclass correlation (ICC) analysis, there was a significant and robust convergence between the OSEM (4, 8) and OSEM (16, 8) reconstruction algorithms, both of which were with Gaussian filters (P<0. 001). Furthermore, there was a significant and robust convergence between FBP and Butterworth (cutoff =0. 45, order =5) and between FBP and Metz (cutoff =0. 9, order =6) in measuring PA parameters (P<0. 001). Conclusions: The findings indicated that PA parameter values obtained from GSPECT MPI data with the FBP and OSEM image reconstruction methods were strongly correlated. However, the values of normal patients were dependent on the reconstruction technique. Therefore, reconstruction methods should not be used interchangeably.

Background: The present study aims to assess the impact of various image reconstruction methods in 18F-FDG PET/CT imaging on the quantification performance of the proposed technique for joint compensation of respiratory motion and partial volume effects (PVEs) in patients with non-small cell lung cancer. Materials and Methods: An image-based deconvolution technique was proposed, incorporating wavelet-based denoising within the Lucy-Richardson algorithm to jointly compensate for PVEs and respiratory motion. The method was evaluated using data from 15 patients with 60 non-small cell lung cancer. In these patients, the lesions were classified by size, location and Signal-to-Background Ratios (SBR). In each study, PET images were reconstructed using four different methods: OSEM with timeof-flight (TOF) information, OSEM with point spread function modelling (PSF), OSEM with both TOF and PSF (TOFPSF), and OSEM without PSF or TOF (OSEM). The Contrast to Noise Ratio (CNR), Coefficient of Variation (COV) and Standardized Uptake Values (SUV) were measured within the lesions and compared to images that were not processed using the joint-compensation technique. Furthermore, variabilities arising due to the choice of the reconstruction methods were assessed. Results: Processing the images using the proposed technique yielded significantly higher CNR and SUV, particularly in small spheres, for all the reconstruction methods and all the SBRs (P<0. 05). Overall, the incorporation of wavelet-based denoising within the Lucy Richardson algorithm improved COV and CNR in all the cases (P<0. 05(. In the patient data, the median values of the relative difference (%) of CNR for the compensated images in comparison to the uncompensated images were 40. 9%, 41. 2%, 45. 3% and 40. 8% for OSEM, PSF, TOF, and TOFPSF, respectively, in the small lesions (equivalent diameter <15 mm), 31. 0%, 25. 9%, 34. 1% and 28. 2% in the average-sized lesions (equivalent diameter<30 mm), 35. 7%, 33. 7%, 37. 8% and 33. 2% in the lesions in the lower lung lobes, 33. 5%, 31. 0%, 35. 7% and 30. 6% in the lesions in the upper lung lobes, 39. 7%, 37. 9%, 45. 1% and 39. 0% in the low-SBR lesions and 28. 8%, 27. 8%, 34. 8% and 25. 7% in the high-SBR lesions. Changes in motion amplitude, target size and SBRs in the patient data resulted in significant inter-method differences in the images reconstructed using different methods. Specifically, in a small target size, quantitative accuracy was highly dependent on the choice of the reconstruction method. Conclusion: Our results showed that joint compensation, and incorporation of wavelet-based denoising, yielded improved quantification from PET images. Quantitative accuracy is greatly affected by SBR, lesion size, breathing motion amplitude, as well as the choice of the reconstruction protocols. Overall, the choice of reconstruction algorithm combined with compensation method needs to be determined carefully.