Introduction: In SPECT, usually a large number of scattered photons are detected.Therefore the reconstructed image without scatter compensation has degraded image quality and biased quantization. While the efforts made to compensate the scatter effect, none of them can perform fast and accurate scatter compensation in non-uniform scattering objects.Methods: and materials: A class of scatter compensation methods, called reconstruction-based scatter compensation method, RBSC, is based on modeling scatter effects in the transition matrix used in iterative reconstruction methods. The accuracy of this method is dependent upon the accuracy of scatter model used.Beekman et al 1997 have shown that RBSC methods results in images with less variance when compared with subtraction-based scatter compensation methods. The main disadvantage of RBSC methods is that the scatter models tend to be very computationally intensive. In this paper we would present a mathematical approach for further reducing the calculations and time of reconstructions using subtraction in the iterative reconstruction methods. In this algorithm scattering contributions of each pixel, from activity of 27 neighbor pixels in 3 slices, which are along the detector is estimated for all detector bins, using Klein-Nishina formula. These data are stored in a certain file and can be used in all iterations in RBSC process. The iterations start with an uncorrected image which is estimated using MLEM formula and then it is corrected subsequently for scattering using: Where fj represents one pixel in the image space, gi is the measured SPECT emission data with detector, and ai j is the coefficient that represents contribution of image pixel j to detector i. Index l denotes pixels number j in projection bin i, so that summation over l makes the projector, and the summation over i the backprojector. SCl is the scattering contributions of pixel j from neighbors\' pixels on slice s to detector i, which is calculated from K-N formula as below: Results: The result on heart phantom and patient images showed that the proposed algorithm significantly improves the contrast and resolution of images. The RAM of the computer and time of reconstruction is dependent to the image size.Conclusions: The proposed algorithm effectively compensates scattering effects in SPECT images and is capable to modify clinical images using a pc in routine clinical activity.

In clinical trials, Computed Tomography (CT) is widely used for diagnosis and treatment guidance. With the increasing use of CT in clinical practice, the issue of high radiation dose has become a significant concern. One way to reduce the dose in CT is by utilizing sparse view imaging. However, sparse view imaging often leads to artifacts in the reconstructed images due to the lack of data. This paper aims to examine and evaluate image reconstruction methods to introduce effective algorithms for sparse view studies. Common image reconstruction algorithms such as Maximum Likelihood Expectation Maximization (MLEM), Algebraic Reconstruction Technique (ART), and Filtered Back Projection (FBP) were reviewed. FBP and MLEM algorithms perform well when there is complete data, but due to the high speed of the FBP algorithm, it is best suited for such cases. However, when data is limited, FBP performs poorly, leading to a comparison between the ART and MLEM algorithms. The results indicate that MLEM performs better in sparse view studies. Quantitative parameters such as Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity Index (SSIM) were evaluated to assess the results. The findings suggest that FBP and MLEM algorithms perform better when data is complete, while MLEM algorithm excels in sparse view studies.

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): In SPECT, the sinogram contains scatter and lack of attenuated counts that degrade the reconstructed image quality and quantity. Many techniques for attenuation and scatter correction have been proposed. An acceptable method of correction is to incorporate effects into an iterative statistical reconstruction. Here, we propose new Maximum Likelihood Expectation Maximization (MLEM) formula to correct scattering and attenuating photons during reconstruction.Materials and Methods: In this work, scatters are estimated through Klein-Nishina formula in all iterations and CT images are used for accurate attenuation correction. Reconstructed images resulted from different MLEM reconstruction formula have been compared considering profile agreement, contrast, mean square error, signal-to-noise ratio, contrast-to-noise ratio and computational time.Results: The proposed formula has a good profile agreement, increased contrast, signal-to-noise (SNR) & contrast-to-noise ratio (CNR), computational time and decreased mean square error (MSE) compared with uncorrected images and/or images from conventional formula.Conclusion: In conclusion, by applying the proposed formula we were able to correct attenuation and scatter via MLEM and improve the image quality, which is a necessary step for both qualitative and quantitative SPECT images.

Accurate detection and localization of radioactive materials is crucial in nuclear imaging, especially given the growing threat of attacks and proliferation of nuclear power plants. A dependable imaging system capable of detecting gamma rays at varying distances is essential for timely identification and localization of radiation sources. Coded aperture imaging (CAI) is a promising method due to its ability to capture more photons and produce higher quality images, albeit more complex. The resolution of decoded images can be improved with the maximum likelihood expectation maximization (MLEM) algorithm. In this study, the GATE code was used to simulate a gamma camera with mosaic MURA coded apertures, with a NaI(Tl) detector measuring 27 × 27 × 2 cm³, to image sources of cesium-137 and americium-241. An angular resolution of about 7 degrees was achieved at a distance of 3 meters. estimated minimum equivalent dose for imaging at a distance of 10 meters using the cesium-137 source was 0.179 nSv/hr (with an activity of 6 microcuries). The signal-to-noise ratio at this distance was equal to 3.10, but after applying the MLEM algorithm, it increased to 17.96. The importance of this research lies in its potential applications for nuclear disarmament, border monitoring, and decontamination, where the location of radioactive sources is often unknown.

Boron Neutron Capture Therapy (BNCT) is one of the promising treatment methods for some malignant tumors such as Glioblastoma Multiforme (GBM). One of the requirements of BNCT treatment is the accurate and real-time boron-concentration monitoring to ensure the efficacy of treatment and no leakage of boron. An accurate method for real-time calculation of the boron dose distribution mapping during irradiation is Single Photon Emission Computed Tomography (SPECT), in which the determination of boron distribution is based on the detection of 478 keV prompt gamma-rays generated through thermal neutron capturing by B-10. Tehran Research Reactor (TRR) is the only possible source for BNCT in Iran, so as the first approach, this BNCT-SPECT system has been evaluated for TRR. In this paper, an imaging system of BNCT-SPECT including four arrays of collimator/detector has been designed for real-time dosimetry as well as B-10 concentration distribution map in the phantom that placed in front of the therapeutic neutron beam using the Monte Carlo simulation code MCNP6. Maximum Likelihood Expectation Maximization (MLEM) method has been used for image reconstruction which results 1 cm spatial resolution.

In this study, the quality measurement of a plaster structure armed with steel rods and an internal air cavity was carried out. The scanner system is equipped with a cesium-137 gamma radioactive source with 661. 7 keV photopeak energy and a linear array CdWO4 scintillating crystal of detector with 8 x 8 mm2 pixel size. The analysis of the internal structure of the sample was performed in two simulation and experimental phases. MCNPX Monte Carlo code was used to perform the simulation phase. MLEM and Back Projection methods were used to reconstruct the images. The simulation results were compared with the experimental outputs, their performance in the 3D display inside this phantom was investigated. The results showed that the MLEM reconstruction method is more efficient than the back projection reconstruction method both in the simulation phase and in the experimental phase in providing internal images. The results showed that with modeling, it is possible to evaluate the tomographical pictures of the manufactured samples, and before setting the experimental, it can be used for different materials and geometries.

Background: Photon attenuation as an inevitable physical phenomenon influences on the diagnostic information of SPECT images and results to errors in accuracy of quantitative measurements. This can be corrected via different physical or mathematical approaches. As the correction equation in mathematical approaches is nonlinear, in this study a new method of linearization called ‘Piece Wise Linearization’ (PWL) is introduced and to substantiate its validity for SPECT image reconstructions, a phantom study is performed. Material and Methods: A SPECT scan of a homemade heart phantom filled with 2 mCi 99mTc was acquired by dual head Siemens E.Cam gamma camera equipped with LEHR collimator. Row data of the scan were transferred in DICOM format to a pc computer for reconstruction of the images using MLEM iterative algorithm in Matlab software. Result: Attenuation map of the phantom m(x) were derived using PWL with linear optimization approach. Based on that, the attenuation corrected SPECT image of the phantom were reconstructed and compared with non-corrected image, using MLEM iterative algorithm. Comparison of the corrected and non-corrected images confirmed with CT attenuation correction method. Conclusion: Attenuation correction in SPECT image can be achieved successfully, using emission data and piecewise linearization with linear optimization approach. The corrected image of ¦(x) and attenuation map m(x) of the heart phantom using this approach promise acceptable image quality for diagnostic clinical use.