Nowadays, Positron Emission Tomography/Computed Tomography (PET/CT) Imaging is widely used for Quantitative oncologic Imaging and illustrated as a robust Imaging modality in diagnostic, recurrence evaluation, treatment planning, and patient follow-up. As in recent treatment strategies, personalized patient Imaging is emphasized, the accurate Quantitative analysis of PET/CT Imaging seems a super important issue. However, it is well known that the data acquisition procedure and choice of a proper reconstruction algorithm can be led to accurate patient-specific interpretation and quantification. The administration of activity, scan time per bed position, and the size of overlaps for bed positions are important parameters to reach the acceptable image quality. However, Quantitative PET Imaging highly depends on reconstruction algorithms, so currently the majority of researchers and companies focus on improving the reconstruction algorithms using new techniques such as deep learning and noise modeling...

Introduction: Skin cancer is one of the most common types of malignancy worldwide. Human skin naturally contains several endogenous fluorophores, as potential sources can emit inherent fluorescence, called intrinsic autofluorescence (AF). The melanin endogenous fluorophore in the basal cell layer of the epidermis seems to have a strong autofluorescence signal among other ones in the skin. This pilot study aimed to investigate the feasibility of the detection of autofluorescence signals in the A375 human melanoma cell line in the cell culture stage using the FluoVision optical Imaging system. Methods: The human skin melanoma cell line (A375) donated as a gift from Switzerland (University Hospital Basel) was cultured. For the Imaging of the A375 human melanoma cell sample in this pilot study, the FluoVision optical Imaging device (Tajhiz Afarinan Noori Parseh Co) was applied. The proposed clustering image processing code was developed based on the K-mean segmentation method, using MATLAB software (version 16). Results: The quantification of color pixels in the color bar along with the intensity score of the autofluorescence signal ranged between 0 and 70 was written in the image processing code execution and a threshold higher than 40%, proportional to the ratio of autofluorescent cells. The percentage of the signal of A375 autofluorescent melanoma cells in the 3 studied cell samples was calculated as 3. 11%± 0. 6. Conclusion: This Imaging method has the advantage of no need for fluorophore labels over the existing fluorescence Imaging methods, and it can be regarded as one of the important choices of label-free Imaging for this A375 melanoma cell line containing the intrinsic endogenous fluorophore in cell studies.

Background: Butterworth, Gaussian, Hamming, Hanning, and Parzen are commonly used SPECT filters during filtered back-projection (FBP) reconstruction, which greatly affect the quality and size accuracy of image. Materials and Methods: This study involved a cardiac phantom in which 1.10 cm thick cold defect was inserted into its myocardium wall and filled with 4.0 mCi/ml (0.148 MBq/ml) 99mTc concentration. The cardiac insert was then put into a cylindrical tank which was filled with 1.2 mCi/ml (0.044 MBq/ml) 99mTc concentration as background. 272 combinations of filter parameters were selected from those filters and applied on image. The measurements of count in myocardium, background, and defect regions of interest (ROI) were performed on each filtered image. Those measurements were then used to calculate contrast, signal-to-noise ratio (SNR), and defect size. For every filter, each criterion was graded (1 to 100) and then summed at their specific setting for total comparison. Results: The results show that, the different filter types produced myocardial image with different contrast, SNR, and defect size. For contrast and SNR, Gaussian filter was the best, while Parzen filter was the best in producing accurate defect size. However, Butterworth filter was found the best for trade off between contrast, SNR, and defect size accuracy.Conclusion: Selection of filter should consider the type of analysis, whether qualitatively or Quantitatively. Qualitative analysis depends on image quality which is denoted by high contrast and SNR, thus Gaussian filter was suggested. Instead, the Butterworth filter was suggested for Quantitative analysis as it is greatly dependent on both, image quality and size accuracy. 

Background: Dynamic contrast enhanced-magnetic resonance Imaging (DCE-MRI) is a relatively new Imaging technique that provides additional information on vascularity and permeability of the uterine tissues to improve decision making in patient management. Objectives: The purpose of this study was to obtain the Quantitative DCE-MRI parameters of symptomatic uterine fibroids and compare to the parameters obtained from normal myometrium, endometrium, and cervix. Patients and Methods: Nineteen patients (aged 40 5 years) with the clinical diagnosis of uterine fibroids were recruited. After routine MRI pelvis scan, DCE-MRI was performed using T1 volumetric liver acquisition with volume acceleration (LAVA) sequence. The data were post-processed using an independent DCE-MRI analysis software. ROI was drawn on the fibroid and the Quantitative DCE-MRI parameters i. e. Ktrans (volume transfer between the blood plasma and extracellular fluid), Kep [flux rate constant between the extracellular extravascular space (EES) and blood plasma], Ve (EES fractional volume), and semi-Quantitative parameters i. e. TTP (time to peak), and IAUGC (initial area under the gadolinium concentration-time curve) were determined. The patients then underwent hysterectomy and uterine fibroids were confirmed in all the patients by histopathology results. In the control group, fifteen healthy volunteers (aged 32 6 years) who did not have uterine fibroid confirmed by sonography examination were recruited. The volunteers underwent the same DCE-MRI scan as the patients and the Quantitative DCE-MRI parameters of the normalmyometrium, endometrium and cervix were obtained. The DCE-MRI parameters from these two groups were then compared. Results: The median Ktrans in the myometrium, endometrium and cervix of the healthy volunteers were 0. 26  0. 13, 0. 20  0. 15, and 0. 30  0. 13 min-1, respectively. No statistical significant difference was found in all the DCE-MRI parameters between the myometrium, endometrium and cervix of the healthy volunteers. The median Ktrans in fibroids was 0. 50  0. 25 min-1. There were statistically significant differences (P < 0. 05) found in Ktrans and Ve between the normal myometrium and fibroid tissue. Conclusions: The Ktrans and Ve of fibroid tissue were statistically significantly higher than those obtained from the normal myometrium, whereby the suggested cut-off values were 0. 47min-1 (sensitivity 64. 7%, specificity 92. 3%) and 0. 62 (sensitivity 70. 6%, specificity 78. 6%), respectively. Quantitative DCE-MRI findings from this study may provide a foundation for assessment of other uterine pathologies.