Background: Ondo Estimating the health effect of 222Rn progeny deposited on inner surfaces of airways regions is of great interest because 222Rn progeny are considered the major contributors in imparted energy to lung structures. Materials and Methods: In this study, (CT) scan of a healthy, non-smoking Syrian volunteer male, 3D-Slicer 4.7.0 medical image processing software, Solidworks mechanical design software and MCNPX 2.5.B code were used to create the geometry and to evaluate the absorbed fraction and specific energy due to alpha particles emitted by inhaled radon progeny in nuclei and layers of sensitive cells in the epithelium of human trachea-bronchial tree. Absorbed fraction (AF) and specific energy were determined using Micro-dosimetry approach and airway tube wall as proposed by ICRP (1994), and NRC (1991). Results: Absorbed fractions (AFs) and specific energy of alpha particles were calculated for each generation from 1st to 15th. Comparison of average AFs values in sensitive layers was carried out with ICRP66 airway model where some significant differences were found due to dimensions differences between both models. Furthermore, AFs of cell nuclei had the same trend of those for layers, where the highest values were for 7.69 MeV alpha particles in BB region and the opposite in bb region.  Conclusion: Interactions of alpha particles with secretory and basal cells show significant differences which can influence dose weightings. Comparisons with ICRP66 data reveal the influence of geometry and target cells distribution on absorbed fraction and specific energy values.

Background: Single photon emission computed tomography (SPECT)-alone imaging using the Tc-99m radiopharmaceutical labeled with methylene diphosphonate or similar analogs is usually employed to diagnose metastatic bone and is typically followed by complementary magnetic resonance (MR) imaging for support in clinical decision-making. In this study, two attenuation map generation approaches from MR and SPECT non-attenuation corrected (SPECT-nonAC) images were evaluated in the context of quantitative SPECT imaging. Materials and Methods: The 2class-MR attenuation map was generated via segmenting an MR image into air and soft tissue. Likewise, SPECT-nonAC was segmented into background air and soft tissue to generate a 2class-SPECT attenuation map. The reference attenuation map was generated through manual bone segmentation from an MR image to develop a 3class-bone attenuation map. Standard uptake value (SUV) bias was calculated using the different attenuation maps on 50 vertebrae from normal patients and 16 vertebrae from metastatic patients. Results: The 2class-MR approach resulted in -16% and -8% SUV bias in normal and metastatic groups, respectively, while 2class-SPECT led to 33% and 26% SUV underestimation for the normal and metastatic patient groups, respectively. Conclusion: The 2class-SPECT approach led to a significant underestimation of SUV due to the uncertainty of body contour delineation. However, the 2class-MR approach resulted in less than -9% SUV bias in metastatic patients, demonstrating its potential to support quantitative SPECT imaging.
 

Background: Currently, MRI-only radiotherapy (RT) eliminates some of the concerns about using CT images in RT chains such as the registration of MR images to a separate CT, extra dose delivery, and the additional cost of repeated imaging. However, one remaining challenge is that the signal intensities of MRI are not related to the attenuation coefficient of the biological tissue. This work compares the performance of two state-of-the-art deep learning models; a generative adversarial network (GAN) and a residual network (ResNet) for synthetic CTs (sCT) generation from MR images. Materials and Methods: The brain MR and CT images of 86 participants were analyzed. GAN and ResNet models were implemented for the generation of synthetic CTs from the 3D T1-weighted MR images using a six-fold cross-validation scheme. The resulting sCTs were compared, considering the CT images as a reference using standard metrics such as the mean absolute error (MAE), peak signal-to-noise-ratio (PSNR) and the structural similarity index (SSIM). Results: Overall, the ResNet model exhibited higher accuracy in relation to the delineation of brain tissues. The ResNet model estimated the CT values for the entire head region with an MAE of 114.1±27.5 HU compared to MAE=-10.9±147.0 HU obtained from the GAN model. Moreover, both models offered comparable SSIM and PSNR values, although the ResNet method exhibited a slightly superior performance over the GAN method. Conclusion: We compared two state-of-the-art deep learning models for the task of MR-based sCT generation. The ResNet model exhibited superior results, thus demonstrating its potential to be used for the challenge of synthetic CT generation in PET/MR AC and MR-only RT planning.

Background: 99m-Tc Ethyl-Cysteinate-Dimer SPECT and MR imaging play a significant role in diagnosing anosmia. In this study, two-tissue class and three-tissue class attenuation maps (2C-MR and 3C-MR) obtained from MR images were compared with CT-based attenuation correction (CTAC). Afterward, the presence of hypo-perfusion in brain lobes was evaluated in SPECT images. Materials and Methods: The 2C-MRAC map was generated through segmentation of T1-W MR images into air and soft-tissue, while in the 3C-MRAC map, the cortical bone was also considered. For investigating MRAC approaches, the difference between activity concentration (ACC) values was estimated in 144 volumes of interest. Ten normal and fourteen anosmic patients were compared by calculating the average normalized count and standard uptake value ratio parameters in the brain lobes. Results: The comparison between attenuation correction strategies represented that MRAC images resulted in underestimation of the ACC values which was more substantial in the cortical area rather than in central regions (maximum 9% vs. 6% for 2C-MR and maximum 5.5% vs. 3.5% for 3C-MR). Nevertheless, there was a strong correlation between the MRAC and CTAC methods with a correlation coefficient of 0.7 for both 2C-MR and 3C-MR. The statistical analysis between normal and affected groups indicated the hypo-perfusion in the cortex of Lh_frontal, Rh and Lh_temporal lobes with p-values < 0.05. Conclusions: Using MRAC resulted in underestimation of activity concentration which was partly eliminated by considering the cortical bone in the 3C-MR attenuation map. Hypo-perfusion was perceived in Frontal and Temporal lobes in SPECT-MRAC images of the anosmic group.