Computed Tomography
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2022 ◽  
Vol 269 ◽  
pp. 129-133
Zain Alfanek ◽  
Abigail Herzog ◽  
Nathan Taylor ◽  
Hanna Jensen ◽  
Avi Bhavaraju ◽  

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 17-17
Richard Stephen Sheppard ◽  
Stefani Beale ◽  
Janet Joseph ◽  
Sai Santhoshini Achi ◽  
Abosede Showunmi ◽  

17 Background: While the National Lung Screening Trial (NLST) has shown a relative reduction in mortality from lung cancer with the application of the United States Preventative Services Task Force (USPSTF) guidelines for the use of Low-Dose Computed Tomography (LDCT) in a select high risk population, many studies have shown that the rate of screening has been below the national average in minority population. Furthermore, lung cancer mortality still appears to be disproportionately higher amongst minority populations. With this study, we aim to evaluate the attitudes, beliefs and values towards lung cancer screening with LDCT in a predominantly Black and Hispanic population in our outpatient clinic. Methods: A survey was conducted over a 3-month period in our outpatient department at an urban inner-city safety net hospital. We included high risk smokers, aged 50 to 80 years who reported no evidence of symptoms. The survey consisted of 20 questions; these included utilizing the Health Belief Model to assess beliefs on perceived susceptibility, severity, benefits and barriers to screening, questions exploring fears of cancer screening and questions assessing overall willingness to undergo lung cancer screening with LDCT. We also included a question on the willingness of participants to engage in educational sessions with regards to lung cancer screening and risk reduction. Results were collected and analyzed via univariate logistic regression model to compare patient populations. Results: 67 patients participated in our survey. 62% were Black, 34% were Hispanic and 4% were Asian/Pacific Islanders. The mean age of our population was 64.5 years and they had an average of 27.2 pack-years of smoking. Issues related to insurance coverage and co-pay were identified as the most significant concern with regards to the unwillingness to undergo screening (p < 0.05). Other concerns identified were the fear of a positive screening result, fear of radiation exposure and lack of understanding of the association with smoking history and lung cancer (p = 0.12). All participants responded yes to being open to be educated on reducing their risk of lung cancer (p < 0.05). Conclusions: While many factors still exist with regards to lung cancer screening in minority populations, the cost of medical care, fear of radiation exposure and anxiety were identified as potential barriers to willingness to screen. Structured educational programs were identified as a possible measure that can be implemented to address these factors, with the potential to increase the willingness to undergo screening in a high risk minority population.

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Xueling Wang ◽  
Xianmin Meng ◽  
Shu Yan

This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, control, and experimental groups, whose images were treated by the manual segmentation method, threshold segmentation algorithm, and full convolutional neural network (FCNN) DL algorithm, respectively. Then, effects of different methods on oral lesion CBCT image recognition and segmentation were analyzed. The results showed that there was no substantial difference in the number of patients with different types of oral lesions among three groups ( P > 0.05 ). The accuracy of lesion segmentation in the experimental group was as high as 98.3%, while those of the blank group and control group were 78.4% and 62.1%, respectively. The accuracy of segmentation of CBCT images in the blank group and control group was considerably inferior to the experimental group ( P < 0.05 ). The segmentation effect on the lesion and the lesion model in the experimental group and control group was evidently superior to the blank group ( P < 0.05 ). In short, the image segmentation accuracy of the FCNN DL method was better than the traditional manual segmentation and threshold segmentation algorithms. Applying the DL segmentation algorithm to CBCT images of oral lesions can accurately identify and segment the lesions.

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Libing Zhu ◽  
Jianxun Zhao ◽  
Xincheng Xiang ◽  
Yu Zhou ◽  
Xiangang Wang

The geometrical shape of the TRISO-coated particle is closely related to its performance and safety. In this paper, models were set up to study the failure fraction of TRISO particle, considering the real asphericity induced by manufacturing uncertainties. TRISO is simplified as a pressure vessel model, and micro X-ray CT was employed to detect the real geometrical shape. Key geometrical parameters, thickness and volume of the real particle, were then obtained with the 3D measurement method and input into PANAMA code (a German code for fuel performance simulation). Release fraction of fission gas and failure fraction of the TRISO-coated particle were revised with the aforementioned parameters with more accuracy and compared with those of the spherical particle. Obvious increment of failure fraction of the particle is found, which may contribute to the release of fission products.

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Yangdong Lin ◽  
Miao He

In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance R from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 951
Alia Zouaghi ◽  
Dhafer Hadded ◽  
Mesbahi Meryam ◽  
Yazid Benzarti ◽  
Mona Cherif ◽  

Pneumatosis cystoid intestinalis is a rare disease reported in the literature affecting 0.03% of the population. It has a variety of causes and its manifestation may change widely. It usually presents as a marginal finding resulting from various gastrointestinal pathologies. In the acute complicated form of pneumatosis intestinalis, management is challenging for physicians and surgeons. We present a case of a 60-year-old patient who was admitted to our surgical department with a symptomatology suggestive of small bowel occlusion. Computed tomography demonstrated ileal volvulus associated with parietal signs suffering and pneumoperitoneum. An emergent exploratory laparoscopy followed by conversion was performed demonstrating segmental ileal pneumatosis intestinalis secondary to a small bowel volvulus due to an inflammatory appendix wrapping around the distal ileum. Further, detorsion, retrograde draining, and appendectomy were performed because there were no signs of necrosis and the appendix was pathological. The postoperative course was uneventful. This case is exceedingly rare in the literature, because it was featured by the ileal volvulus due to appendicitis.This case report emphasizes the importance of surgical procedures in the management of symptomatic pneumatosis intestinalis.

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