ABSTRACT
Background: Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images. Methods: We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias. Results: Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches. Conclusions: We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.
ABSTRACT
Introduction: Hirsutism, mainly due to polycystic ovary syndrome (PCOS), causes stress, anxiety, and depression in females. Laser-assisted hair removal (LAHR) is currently accepted as a good treatment option for hirsutism. The goal of the current study was to ascertain how LAHR affected the degree of hirsutism, quality of life, and depression in hirsute females. Methods: A single-arm before/after clinical trial was designed and performed in the Razi hospital Laser Clinic over a 15-month period. All hirsute females visiting the Razi hospital laser clinic were enrolled and received three sessions of LAHR every 4-6 weeks if they were interested and signed an informed consent form. Before the commencement of LAHR and six to eight weeks after the last session, the Ferriman-Gallwey score (hirsutism severity), Beck score (depression index) and DLQI score (quality of life index) were calculated and stored. Results: There were 80 subjects in all. The mean ± SD of the Ferriman-Gallwey score was reduced from 7.05 ± 2.27 to 4.91 ± 2.41, P < 0.001. The mean ± SD of the Beck depression score was reduced from 13.3 ± 8.7 to 10.2 ± 8.4, P < 0.001, and the mean ± SD of the DLQI score decreased from 5.6 ± 5.2 to 3.5 ± 2.3, P < 0.001. No significant complications were reported. Conclusion: LAHR can improve hirsutism-related depression and degradation of quality of life, as well as hirsutism physical signs.
ABSTRACT
Psoriasis is a chronic and inflammatory dermatologic disease. Psoriasis may predispose to cardiovascular disease and diabetes. However, the role of tumor necrosis factor (TNF) inhibitor in mediating this risk is controversial. Regarding frequent use of infliximab in psoriasis, and the hypothesis that anti TNF-α treatment may increase Body Mass Index (BMI) and alter lipid profile in these patients, the aim of this study was to assess changes in BMI and Lipid Profile and level of leptin in Psoriatic Patients under Treatment of Standard Protocol of Infliximab in a 24 week period. This study was accomplished as a before-after study. Twenty-seven psoriatic patients were included, and standard infliximab therapy was applied. All patients underwent 3 times of blood collection and in each session; LDL, HDL, Total Cholesterol, Triglycerides, Leptin, and PASI score were measured at the start of the study and at the 12th and 24th week of follow-up. Twenty-five patients consisted of 18 (72%) male and 7 (28%) female subjects were evaluated. The mean age of the patients was 36.91±13.31 years. PASI score demonstrated significant decrease after 24 weeks; however, BMI and HDL and leptin showed a significant increase during treatment. Significant negative correlation was seen between Leptin and PASI score changes (r=0.331, P=0.042). HDL and BMI had the most correlations with leptin (positive correlation) and PASI score (negative correlation). Results demonstrated a dramatic decrease in PASI, increase in BMI and HDL and increased in leptin; somewhat correlated to each other. These results suggest that patients taking infliximab should take more care of their weight and lipid profile, while on treatment.
