Search

Over the last two decades, breast cancer remains the main cause of cancer deaths in women. To treat this type of cancer, radiation therapy (RT) has proved to be efficient. RT for breast cancer is, however, challenged by intrafractional motion caused by respiration. The problem is more severe for the left-sided breast cancer due to the proximity to the heart as an organ-at-risk. While particle therapy results in superior dose characteristics than conventional RT, due to the physics of particle interactions in the body, particle therapy is more sensitive to target motion.

Purpose: Accurate and fast multiorgan segmentation is essential in image‐based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time‐consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks. Methods: The pre‐trained p2p‐U‐Net‐GAN and HighRes3D architectures were fine‐tuned with PET‐only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL‐assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)‐derived S‐values to be used for internal dosimetry. Results: A fair comparison with manual low‐dose CT‐aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89–0.93, 0.98–0.99, and 0.87–0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S‐values varied between 0.1% and 6% with a maximum attributed to the stomach. Conclusion: The findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL‐based methods provide automated and fast segmentation of whole‐body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F‐FDG‐PET image segmentation. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Applied Geology is the property of Shahid Chamran University of Ahvaz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Quantitative accuracy in SPECT is mainly affected by collimator penetration and scattering, particularly for high-energy imaging. Lofthole collimation offers superior performance in terms of penetration and scattering. Methods: In this research, the GATE Monte Carlo simulator was exploited to calculate edge penetration and scattering in the lofthole collimator using an inair and in-phantom point source of Tc-99m and I-123. The performance of the lofthole was then compared to that of a pinhole. Both lofthole and pinhole collimators were assumed to have the same geometry including an aperture diameter of 3.04 mm and an opening angle of 75°. Furthermore, the angular distribution of the scattering and penetration were investigated for a multilofthole collimator. Results: The results show that penetration, scattering, and sensitivity are all a function of the photon energy. The penetration and scattering of the pinhole are about 4% higher than that of the lofthole collimator, for Tc-99m SPECT. Compared to the Tc-99m, I-123 SPECT suffers from approximately 1.5- and 1.42-fold higher penetration and scatter fractions, respectively, for lofthole aperture. Moreover, the lofthole collimator presents a higher sensitivity compared with the pinhole (0.030 versus 0.023 for the Tc-99m SPECT). In addition, the findings exhibit a reduction in sensitivity by increasing the photon incidence angle. Both scattering and penetration fractions illustrate a decreasing trend across the angle of incidence. Conclusion: Compared to pinhole, the lofthole offers superior performance in terms of scattering and penetration for both low- and high-energy SPECT imaging. [ABSTRACT FROM AUTHOR]

Introduction: Brain tumors pose a significant challenge in the field of oncology, and radiation therapy plays a crucial role in their treatment. Accurate modeling of radiation dose distribution is essential for optimizing treatment plans and improving patient outcomes. In this study, we utilized the Monte Carlo N - Particle (MCNP) code to investigate the effect of radiation dose on the treatment of brain tumors. Methods: We developed a comprehensive MCNP model to simulate the interaction of radiation with brain tissue. The model incorporated anatomical details, such as tissue heterogeneity and tumor characteristics, to accurately represent the complex tumor microenvironment. We employed various radiation sources, including photon and electron beams, to deliver therapeutic doses to the tumor region. The treatment plans were optimized based on prescribed dose, tumor size, and critical organ constraints. Results: The MCNP simulations provided detailed information on the radiation dose distribution within the brain tumor and surrounding healthy tissues. We analyzed the impact of different radiation doses on tumor control probability (TCP) and normal tissue complication probability (NTCP). Our results demonstrated a dose - dependent relationship between radiation dose and tumor control, with higher doses resulting in increased TCP. However, escalating the radiation dose beyond a certain threshold led to a higher risk of normal tissue complications. Conclusions: The findings of this study highlight the importance of accurate radiation dose planning in brain tumor treatment. The utilization of MCNP code allowed for precise modeling of radiation interactions and provided insights into the dose - response relationship. Optimizing treatment plans to maximize tumor control while minimizing normal tissue toxicity is crucial for improving patient outcomes. Further investigations should focus on refining treatment protocols and exploring advanced radiation delivery techniques to enhance therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Introduction: It is difficult to distinguish between malignant and benign bone lesions mainly due to similar imaging appearance, particularly in the early stages. It is well - known that bone cancer (or sarcoma) most commonly affects the pelvis and/or the long bones such as the tibia, femur, and upper arm. This study aims to classify bone tissue into 3 - class (normal, benign, and malignant) based on X-ray radiographs of the proximal femur/pelvis. Materials: A total of 507 proximal femur/pelvis X-ray radiographs of both men and women were acquired and collected. The dataset includes 167, 148, and 192 malignant, benign, and healthy bone scans, respectively. The dataset was pre - processed to reduce the imaging noise, resampling, and also thresholding. A multiclass deep convolutional neural network (CNN) model was exploited and trained to classify the bone radiographs. The performance of the CNN architecture was also compared with that of the two expert radiologists. Results: The confusion matrix shows an encouraging performance of the proposed deep neural network. Compared with two expert radiologists, the CNN architecture results in superior performance in terms of average sensitivity (0.822 vs 0.751) and specificity (0.912 vs 0.889). The area under the receiver operating characteristic curve (ROC AUC) is 0.853 indicating promising diagnostic accuracy of the proposed network. Conclusions: The findings confirm that the proposed deep CNN model accurately classifies the bone tissue in proximal femur/pelvis X-ray radiographs and therefore can be the method of choice for early and accurate diagnosis of bone cancer. [ABSTRACT FROM AUTHOR]

Introduction: It is well-recognized that collimator-detector response (CDR) is the main image blurring factor in SPECT. In this research, we compensated the images for CDR in molecular SPECT by using STIR reconstruction framework. Methods: To assess resolution recovery capability of the STIR, a phantom containing five point sources along with a micro Derenzo phantom were investigated. Influence of the lesion size on SPECT quantification was addressed by calculating recovery coefficients (RCs) as well as spill-over ratios (SORs) for reconstructed NEMA image-quality phantom. Impact of the resolution modeling on noise properties was also studied. The RCs were then compared with those of experimentally obtained. In all cases, the images were iteratively reconstructed using an OSEM algorithm with 4 subsets and 32 subiterations. Results: CDR compensation gives rise to a significant drop in tomographic resolution from 2.45 mm to 1.55 mm. RC for hot rods of the NEMA IQ phantom monotonically grows as rod diameter increases, and results in an improvement of the RC up to a factor of 1.24 for the 5-mm rod diameter. PSF modeling also leads to a reduction in SOR from 0.24 to 0.16 averaged for the two cold cylinders. As a consequence of resolution recovery, a 15.5% overshoot near sharp edges imposing Gibbs ringing artifact occurs. In addition, a blobby noise texture is also observed. Furthermore, STIR results are consistent with the experimental ones. Conclusion: Our findings demonstrate that resolution recovery is required for quantitative molecular SPECT imaging, and CDR compensation by the STIR framework offers superior SPECT image quality. [ABSTRACT FROM AUTHOR]

Radiographic imaging has a significant role in the timely diagnosis of the diseases of neonates in intensive care units. The estimation of the dose received by the infants undergoing radiographic examination is of great importance, due to greater more radiosensitivity and longer life expectancy of the neonates and premature babies. In this study, the values of entrance skin dose (ESD), dose area products (DAPs), energy imparted (EI), whole-body dose, effective dose and risk of childhood cancer were estimated using three methods including direct method [using thermoluminescence dosimetry (TLD) chips], indirect method (using tube output) and Monte Carlo (MC) method (using MCNP4C code). In the first step, the ESD of the neonates was directly measured using TLD-100 chips. Fifty neonates, mostly premature, with different weights and gestational ages in five hospitals mostly suffering from respiratory distress syndrome and pneumonia were involved in this study. In the second step, the values of ESD to neonates were indirectly obtained from the tube output in different imaging techniques. The imaging room, incubator, neonates and other components were then simulated in order to obtain the ESD values using the MCNP4C code. Finally, the values of ESD assessed by the three methods were used for calculation of DAP, EI, whole-body dose, effective dose and risk of childhood cancer. The results indicate that the mean ESD per radiograph estimated by the direct, indirect and MC methods are 56.6±4.1, 50.1±3.1 and 54.5±3.3 μGy, respectively. The mean risk of childhood cancer estimated in this study varied between 4.21×10−7 and 2.72×10−6. [ABSTRACT FROM PUBLISHER]