Introduction: Scattered photons are one of the main causes of degrading the contrast of lesions and resolution in SPECT imaging of the heart that result in error in quantification. The usual technique for the rejection of scattered photons is through energy windowing. However, because of limited energy resolution of current scintillation cameras it is impossible to avoid scatter photons from detection. Modeling of Compton scattering through finding suitable functions was proposed in this study. These functions were used for scatter correction through deconvolution in next step. Materials and Methods: Monte Carlo simulation was used for creating projections of three different activity sources: a line source passed through left ventricle, a point source placed in left ventricle and finally real activity distribution of Tc-99m in torso organs. All of these sources were placed in a digital attenuation phantom which modeled a real patient body. Images of primary and scattered photons were acquired separately. Convolution and 2D deconvolution in Fourier domain was applied for estimating the primary projections through total ones. Results: In the first step, scatter and total images were modeled as convolution of a modified exponential function with primary image. The best exponent value was determined for each of 64 views (0.115 to 0.150 according to heart to detector distance). In the next step, these functions were used for scatter correction through deconvolution. Sum of the square differences between the primary and scatter corrected images were decreased considerably, myocardium to cavity contrast increased for all of 64 views (34% ±10%). Good agreement between the real primary and scatter corrected images were also found. Discussion and Conclusion: These results indicate that deconvolution technique for the scatter compensation can significantly reduce the degrading roles of scattering in quantitative SPECT imaging.

Introduction: It is a common protocol to use 201Tl for the rest and 99mTc for the stress cardiac SPECT imaging. Theoretically, both types of imaging may be performed simultaneously using different energy windows for each radionuclide. However, a potential limitation is the cross-contamination of scattered photons from 99mTc and collimator X-rays into the 201Tl energy window. We used a middle energy window method to correct this cross-contamination.Material and Methods: Using NCAT, a typical software torso phantom was generated. An extremely thin line source of 99mTc activity was placed inside the cardiac region of the phantom and no activity in the other parts. The SimSET Monte Carlo simulator was used to image the phantom in different energy windows. To find the relationship between projections in different energy windows, deconvolution theory was used. We investigated the ability of the suggested functions in three steps: Monte Carlo simulation, phantom experiment and clinical study. In the last step, SPECT images of eleven patients who had angiographic data were acquired in different energy windows. All of these images were compared by determining the contrast between a defect or left ventricle cavity and the myocardium.Results: We found a new 2D kernel which had an exponential pattern with a much higher center. This function was used for modeling 99mTc down scatter distribution from the middle window image. X-ray distribution in the 201Tl window was also modeled as the 99mTc photo peak image convolved by a Gaussian function. Significant improvements in the contrasts of the simultaneous dual 201Tl images were found in each step before and after reconstruction. In comparison with other similar methods, better results were acquired using our suggested functions.Conclusion: Our results showed contrast improvement in thallium images after correction, however, many other parameters should be evaluated for clinical approaches. There are many advantages in simultaneous dual isotope imaging. It halves imaging time and reduces patient waiting time and discomfort. Identical rest/stress registration of images also facilitates physicists’ motion or attenuation corrections and physicians’ image interpretation.

Introduction: An efficient method of tomographic imaging in nuclear medicine is positron emission tomography (PET). Compared to SPECT, PET has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. However, high noise levels in the image limit its diagnostic utility. Noise removal in nuclear medicine is traditionally based on Fourier decomposition of images. This method is based on frequency components, irrespective of the spatial location of the noise or signal. The wavelet transform presents a solution by providing information on the frequency content while retaining spatial information. This alleviates the shortcoming of the Fourier transform and thus, wavelet transform has been extensively used for noise reduction, edge detection and compression.Materials and Methods: In this research, we used the SimSET software to simulate PET images of the NCAT phantom. The images were acquired using 250 million counts in a 128×128 matrix. For the reference image, we acquired an image with high counts (6 billion). Then, we reconstructed these images using our own software developed in MATLAB. After image reconstruction, a 250 million counts image (noisy image) and a reference image were normalized and then root-mean-square error (RMSE) was used to compare the images. Next, we wrote and applied de-noising programs. These programs were based on using 54 different wavelets and 4 methods. De-noised images were compared with the reference image using RMSE.Results: Our results indicate that the Stationary Wavelet Transform and Global Thresholding are more efficient at noise reduction compared to the other methods that we investigated.Discussion: The wavelet transform is a useful method for de-noising of simulated PET images. Noise reduction using this transform and loss of high-frequency information are simultaneous with each other. It seems that we should attend to the mutual agreement between noise reduction and the visual quality of the image.

Introduction: Poor sensitivity and poor signal to noise ratio because of low injected thallium dose and presence of scattered photons are the main problems in using thallium in scintigraphic imaging of the heart. Scattered photons are the main cause of degrading the contrast and resolution in SPECT imaging that result in error in quantification. Thallium decay is very complicated and photons are emitted in a wide range of energies of 68-82 keV. It seems possible to achieve better primary to scattered radiation ratio and better image sensitivity simultaneously if the energy window setting is carefully selected.Methods: This investigation was performed in three steps: Monte Carlo simulation, phantom experiment and clinical study. In simulation step, the new 4D digital NCAT phantom was used to simulate the distribution of activity 201Tl) in patient torso organs. The same phantom was used to simulate the attenuation coefficient of different organs of the typical patient's body. Two small defects on different parts of left ventricle also were generated for further quantitative and qualitative analysis. The simulations were performed using the SimSET simulator to generate images of such patient. The emissions arising from TI-201 decay were simulated in four steps using the energies and relative abundances. Energy spectra for primary and scatter photons were calculated. Changing the center and width of energy windows, optimum energy window characteristics were determined. In next step jaszczak phantom was prepared and used for SPECT imaging in different energy windows. In last step SPECT images of 7 patients who had angiographic data were acquired in different energy windows. All of these images were compared qualitatively by four nuclear medicine physicians independently.Results: The optimum energy window was determined as a wider asymmetric window (77keV~30%) that its center is not placed on photo-peak of energy spectrum. This window increased the primary counts rate and PTSR considerably as compared with the conventional symmetric energy window (67keV~~ -%). In a comparison which performed between clinical images acquired in suggested 77-30% window with conventional 67-20% window, a considerable increase was found in myocardial to defect contrast (1.541±0.368)and myocardial to cavity contrast (1.171±0.099). A negligible increase was also found in total counts of images using this window.Conclusion: We found that conventional symmetric energy window (67keV±10%) couldn't be a suitable choice for thallium heart imaging; furthermore three energy windows, 73keV-30%, 75keV-30% and 77keV-30%, were determined as optimum window options. For further analysis the images from such windows were compared in each three steps of this investigation. In all steps conventional symmetric energy window (67keV-20%) was introduced as the worst case and the asymmetric 77keV-30% was determined as the most suitable.

Introduction: Myocardial SPECT imaging is usually performed acquiring 32 views in 180 degree with equal steps of 5.625 degrees. Acquiring more images requires spending more time or injection of more activity to the patients. An idea to improve the quality reconstructed images without acquiring extra images is producing the extra images interpolating the data between adjacent projections. The aim of present study was investigation the feasibility of this idea.Methods: Obviously such investigation cannot be performed on real patient's data. Therefore, data were simulated using NCAT digital phantom and SimSET Monte Carlo code. The imaging was performed as usual, acquiring 32 views from right anterior oblique to left posterior oblique. The data were interpolated to construct 5 images between adjacent projections convert it into 187 projections. The simulation was performed again acquiring 187 images as the reference. The conventional, interpolated and reference data set were reconstructed and compared for improvement and degradation in quality of final images. The above procedure was repeated for phantoms representing different types of heart disease, different cardiac size and different count densities.Results: The results showed that Hermit interpolation technique produces better quality images comparing to other interpolation methods tested. Results also confirmed that streak artifacts decreases, signal to noise ratio and contrast increased due to increasing the number of samples.Conclusion: These results indicate that the physical properties of reconstructed images improve significantly.This directly must improve the lesions delectability of images. However the matter is still under investigation.

Introduction: In this study the evaluation of a Platelet-based Maximum Penalized Likelihood Estimation (MPLE) for denoising SPECT images was performed and compared with other denoising methods such as Wavelets or Butterworth filtration. Platelet-based MPLE factorization as a multiscale decomposition approach has been already proposed for better edges and surfaces representation due to Poisson noise and inherent smoothness of this kind of images.Methods: We applied this approach on both simulated and real SPECT images. Monte Carlo simulations were generated with the SimSET package to model the physical processes and instrumentation used in emission imaging. Cardiac, brain and NEMA phantom SPECT images were obtained using a single-head, Argus model SPECT system. The performance of this method has been evaluated both qualitatively and quantitatively with power spectrum, SNR and noise level measurements on simulated and real SPECT images.Results: For NEMA phantom images, the measured noise levels before (Mb) and after (Ma) denoising with Platelet-based MPLE approach were Mb=2.1732, Ma=0.1399. In patient study for 32 cardiac SPECT images, the difference between noise level and SNR before and after the approach were (Mb=3.7607, SNRb=9.7762, Ma=0.7374, SNRa=41.0848) respectively. Thus the Coefficient of variance (C.V) of SNR values for denoised images with this algorithm as compared with Butterworth filter, (145/33%) was found. For 32 brain SPECT images the Coefficient Variance of SNR values, (196/17%) was obtained.Conclusion: Our results shows that, Platelet-based MPLE is a useful method for denoising SPECT images considering better homogenous image, improvements in SNR, better radioactive uptake in target organ and reduction of interfering activity from background radiation in comparison to that of other conventional denoising methods.

Introduction: Motion detection in nuclear medicine imaging because of restrictions of images such as high level of noise and fading of organs boundary that are inherent characteristics of images, have insufficiency. These restrictions can cause to decrease precision of motion detection especially in two methods of center of gravity and cross correlation in renographics images. That in this research we used approximation part of decomposed image with wavelet transform, for increase precision of motion detection or decrease minimum amount of detectable motion.Methods: In this study 4D NCAT phantom was used to generate a typical human torso and the SimSET Monte Carlo nuclear medicine (NM) simulator was used to generate phantoms images. Dynamic images were adjusted to construct 180 phantoms that each representing a 10 second duration.5 type of phantom and 3 type of motion direction was simulated and we used 7 families of wavelet for decomposition of images. Motion detection algorithms, center of gravity and cross-correlation, (were implemented in Matlab 7.1 environment) were used for motion determination in renal imaging data and Finally minimum amount of detectable motion for each of them was measured.Results: results show that 2 families of wavelet Daubechies and Reversbio in first level of decomposition can significantly (p-value<0.05) increase precision of motion detection in two algorithms of center of gravity and cross correlation. Also results show these two methods can determine direction of motion.Conclusion: results show that use of approximation part of decomposed imaged with wavelet transform can increase precision of motion detection in renal dynamic imaging. However for two families of wavelet and in first level of decomposition we were to fulfill this aim. But it showed the positive effect of wavelet transform for increase precision of motion detection algorithms in nuclear medicine imaging.

Introduction: One of the major problems in the development of nuclear medicine images is the presence of noise. The noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a Fourier transform environment. The main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times information as well. This problem becomes more serious when the most important signals are non-stationary. The aim of this research is to evaluate the effect of wavelet transform on nuclear medicine image. Materials and Methods: A brain phantom-like Hoffman phantom with 4 layers was used. Planar images were acquired from the phantom, with a total count of 107 (as reference image) and from each layer separately. The counts of planar images in each layer were increased from 100 to 700 kilo counts in seven steps. Different levels of white (Gaussian) noise was added to the reference images using MATLAB software. Simulation images using SimSET software and NCAT phantom were also produced. Wavelet transform, Butterworth, Metz, Hanning(3×3) and Wiener filters were applied on all images. The Universal Image Quality Index (UIQI) was used to evaluate the image quality.Result: Wavelet transform can increase the value UIQI (UIQI = 0.7352). The effect of wavelet transform on the improvement of image quality is much better than Butterworth (UIQI = 0.3556) and Metz (UIQI = 0.3493) but almost the same as the effect of Wiener (UIQI = 0.7626) and Hanning (3*3) (UIQI = 0.8017). Discussion and Conclusion: Although wavelet transform is not the best method of reducing noise level, but its use can reduce the noise level like filters do. Assessment of commonly used thresholds as well as new design of a special threshold for wavelet transform in nuclear medicine images can improve the performance of this transform.