Search results for: radiography

Authors: Sahar Heidary, Ramin Ghasemi Shayan

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The recent coronavirus disease 2019 (COVID-19) occurrence has carried considerabletrials to the world health system, comprising the training of dental and maxillofacial radiology (DMFR). DMFR will keep avital role in healthcare throughout this disaster. Severe acute breathing disease coronavirus 2 (SARS-CoV-2), the virus producing the current coronavirus disease 2019 (COVID-19) pandemic, is not only extremely contagious but can make solemn consequences in susceptible persons comprising dental patients and dental health care personnel (DHCPs). Reactions to COVID-19 have been available by the Cores for Infection Switch and Inhibition and the American Dental Association, but a more detailed answer is necessary for the harmless preparation of oral and maxillofacial radiology. Our goal is to evaluation the existing information just how the illness threatens patients and DHCPs and how to define which patients are possible to be SARS-CoV-2 infected; study how the usage of private shielding utensils and contamination control measures based on recent top observes, and knowledge can decrease the danger of virus spread in radiologic trials; and scrutinize how intraoral radiography, with its actually superior danger of scattering the infection, might be changed by extraoralradiographic methods for definite diagnostic jobs. In the pandemic, teleradiology has been extensively recycled for diagnostic determinations of COVID-19 patients, for discussions with radiologists in crisis cases, or managing of distance among radiology clinics. Dentists can have the digital radiographic images of their emergency patients through online service area also by electronic message or messaging applications to view in their smart phones, laptops, or other electronic devices.

Keywords: radiology, dental, oral, COVID-19, infection

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Authors: Denise Levy, Helen J. Khoury

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Nuclear technology is a controversial issue among a great share of the Brazilian population. Misinformation and common wrong beliefs confuse public’s perceptions and the scientific community is expected to offer a wider perspective on the benefits and risks resulting from ionizing radiation in everyday life. Attentive to the need of new approaches between science and society, the Nuclear Energy Museum, in northeast Brazil, is an initiative created to communicate the growing impact of the beneficial applications of nuclear technology in medicine, industry, agriculture and electric power generation. Providing accessible scientific information, the museum offers a rich learning environment, making use of different educational strategies, such as films, interactive panels and multimedia learning tools, which not only increase the enjoyment of visitors, but also maximize their learning potential. Developed according to modern active learning instructional strategies, multimedia materials are designed to present the increasingly role of nuclear science in modern life, transforming science education into a meaningful learning experience. In year 2016, nine different interactive computer-based activities were developed, presenting curiosities about ionizing radiation in different landmarks around the world, such as radiocarbon dating works in Egypt, nuclear power generation in France and X-radiography of famous paintings in Italy. Feedback surveys have reported a high level of visitors’ satisfaction, proving the high quality experience in learning nuclear science at the museum. The Nuclear Energy Museum is the first and, up to the present time, the only permanent museum in Brazil devoted entirely to nuclear science.

Keywords: nuclear technology, multimedia learning tools, science museum, society and education

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Authors: Gabrielle O'Donoghue

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Background: Pre-operative radiographs facilitate assessment and treatment planning in minor oral surgery. Quality assurance for dental radiography advocates the As Low As Reasonably Achievable (ALARA) principle in collecting accurate diagnostic information. Aims: To audit the quality of digital intraoral periapicals (IOPAs) taken prior to dental extractions in a metropolitan general dental practice setting. Standards: The National Radiological Protection Board (NRPB) guidance outlines three grades of radiograph quality: excellent (Grade 1 > 70% of total exposures), diagnostically acceptable (Grade 2 <20%), and unacceptable (Grade 3 <10%). Methodology: A study of pre-operative radiographs taken prior to dental extractions across 12 private general dental practices in a large metropolitan area by 44 practitioners. A total of 725 extractions were assessed, allowing 258 IOPAs to be reviewed in one audit cycle. Results: First cycle: Of 258 IOPAs: 223(86.4%) scored Grade 1, 27(10.5%) Grade 2, and 8(3.1%) Grade 3. The standard was met. 35 dental extractions were performed without an available pre-operative radiograph. Action Plan & Recommendations: Results were distributed to all staff and a continuous professional development evening organized to outline recommendations to improve image quality. A second audit cycle is proposed at a six-month interval to review the recommendations and appraise results. Conclusion: The overall standard of radiographs met the published guidelines. A significant improvement in the number of procedures undertaken without pre-operative imaging is expected at a six-month interval period. An investigation into undiagnostic imaging and associated adverse patient outcomes is being considered. Maintenance of the standards achieved is predicted in the second audit cycle to ensure consistent high quality imaging.

Keywords: audit, oral radiology, oral surgery, periapical radiographs, quality assurance

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Authors: Verena M. Moock, Darien E. Arce Chávez, Mariana M. Espejel González, Leopoldo Ruíz-Huerta, Crescencio García-Segundo

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Uncertainty analysis in industrial computed tomography is commonly related to metrological trace tools, which offer precision measurements of external part features. Unfortunately, there is no such reference tool for internal measurements to profit from the unique imaging potential of X-rays. Uncertainty approximations for computed tomography are still based on general aspects of the industrial machine and do not adapt to acquisition parameters or part characteristics. The present study investigates the impact of the acquisition time on the dimensional uncertainty measuring a stainless steel cylinder with a circular tomography scan. The authors develop the figure difference method for X-ray radiography to evaluate the volumetric differences introduced within the projected absorption maps of the metal workpiece. The dimensional uncertainty is dominantly influenced by photon energy dissipated as heat causing the thermal expansion of the metal, as monitored by an infrared camera within the industrial tomograph. With the proposed methodology, we are able to show evolving temperature differences throughout the tomography acquisition. This is an early study showing that the number of projections in computer tomography induces dimensional error due to energy absorption. The error magnitude would depend on the thermal properties of the sample and the acquisition parameters by placing apparent non-uniform unwanted volumetric expansion. We introduce infrared imaging for the experimental display of metrological uncertainty in a particular metal part of symmetric geometry. We assess that the current results are of fundamental value to reach the balance between the number of projections and uncertainty tolerance when performing analysis with X-ray dimensional exploration in precision measurements with industrial tomography.

Keywords: computed tomography, digital metrology, infrared imaging, thermal expansion

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Authors: Liyun Chang, Cheng-Hsiang Tsai

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To ensure the radiation precisely delivering to the target of cancer patients, the linear accelerator equipped with the pretreatment on-board imaging system is introduced and through it the patient setup is verified before the daily treatment. New generation radiotherapy using beam-intensity modulation, usually associated the treatment with steep dose gradients, claimed to have achieved both a higher degree of dose conformation in the targets and a further reduction of toxicity in normal tissues. However, this benefit is counterproductive if the beam is delivered imprecisely. To avoid shooting critical organs or normal tissues rather than the target, it is very important to carry out the quality assurance (QA) of this on-board imaging system. The QA of the On-Board Imager® (OBI) system of one Varian Clinac-iX linear accelerator was performed through our procedures modified from a relevant report and AAPM TG142. Two image modalities, 2D radiography and 3D cone-beam computed tomography (CBCT), of the OBI system were examined. The daily and monthly QA was executed for five months in the categories of safety, geometrical accuracy and image quality. A marker phantom and a blade calibration plate were used for the QA of geometrical accuracy, while the Leeds phantom and Catphan 504 phantom were used in the QA of radiographic and CBCT image quality, respectively. The reference images were generated through a GE LightSpeed CT simulator with an ADAC Pinnacle treatment planning system. Finally, the image quality was analyzed via an OsiriX medical imaging system. For the geometrical accuracy test, the average deviations of the OBI isocenter in each direction are less than 0.6 mm with uncertainties less than 0.2 mm, while all the other items have the displacements less than 1 mm. For radiographic image quality, the spatial resolution is 1.6 lp/cm with contrasts less than 2.2%. The spatial resolution, low contrast, and HU homogenous of CBCT are larger than 6 lp/cm, less than 1% and within 20 HU, respectively. All tests are within the criteria, except the HU value of Teflon measured with the full fan mode exceeding the suggested value that could be due to itself high HU value and needed to be rechecked. The OBI system in our facility was then demonstrated to be reliable with stable image quality. The QA of OBI system is really necessary to achieve the best treatment for a patient.

Keywords: CBCT, image quality, quality assurance, OBI

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Authors: Salahud Din, Saima Masood, Hafsa Zaneb, Habib-Ur Rehman, Imad Khan, Muqader Shah

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The present study was carried out on the gross anatomy, biometery and radiographic analysis of tarsal bones in twenty specimens of adult chinkara (Gazella bennettii). The desired bones were collected from the graveyards present in the locality of the different safari parks and zoos in Pakistan. To observe the edges and articulations between the bones, the radiographic images were acquired in craniocaudals and mediolateral views of the intact limbs. The gross and radiographic studies of the tarsus of adult Chinkara were carried out in University of Veterinary and Animal Sciences, Lahore, Pakistan. The tarsus of chinkara comprised of five bones both grossly and radiographically, settled in three transverse rows: tibial and fibular tarsal in the proximal, central and fourth fused tarsal in the middle row, the first, second and third fused tarsal in the distal row. The fibular tarsal was the largest and longest bone of the hock, situated on the lateral side and had a bulbous tuber calcis 'point of the hock' at the proximal extremity which projects upward and backward. The average maximum height and breadth for fibular tarsal was 5.61 ± 0.23 cm and 2.06 ± 0.13 cm, respectively. The tibial tarsal bones were the 2nd largest bone of the proximal row and lie on the medial side of the tarsus bears trochlea at either end. The average maximum height and breadth for tibial tarsal was 2.79 ± 0.05 cm and 1.74 ± 0.01 cm, respectively. The central and the fourth tarsals were fused to form a large bone which extends across the entire width of the tarsus and articulates with all bones of the tarsus. A nutrient foramen was present in the center of the non auricular area, more prominent on the ventral surface. The average maximum height and breadth for central and fourth fused tarsal was 1.51 ± 0.13 cm and 2.08 ± 0.07 cm, respectively. The first tarsal was a quadrilateral piece of bone placed on the poteriomedial surface of the hock. The greatest length and maximum breadth of the first tarsal was 0.94 ± 0.01 cm and 1.01 ± 0.01 cm, respectively. The second and third fused tarsal bone resembles the central but was smaller and triangular in outline. It was situated between the central above and the large metatarsal bone below. The greatest length and maximum breadth of second and third fused tarsal was 0.98 ± 0.01 cm and 1.49 ± 0.01 cm.

Keywords: chinkara, morphometry, radiography, tarsal bone

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Authors: Naridchaya Aberdour, Joanna Kao, Anne Miller, Timothy Shore, Richard Maher, Zhixin Liu

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Background: COVID-19 has and continues to be a strain on healthcare globally, with the number of patients requiring hospitalization exceeding the level of medical support available in many countries. As chest x-rays are the primary respiratory radiological investigation, the Radiological Assessment of Lung Edema (RALE) score was used to quantify the extent of pulmonary infection on baseline imaging. Assessment of RALE score's reproducibility and associations with clinical outcome parameters were then evaluated to determine implications for patient management and prognosis. Methods: A retrospective study was performed with the inclusion of patients testing positive for COVID-19 on nasopharyngeal swab within a single Local Health District in Sydney, Australia and baseline x-ray imaging acquired between January to June 2020. Two independent Radiologists viewed the studies and calculated the RALE scores. Clinical outcome parameters were collected and statistical analysis was performed to assess RALE score reproducibility and possible associations with clinical outcomes. Results: A total of 78 patients met inclusion criteria with the age range of 4 to 91 years old. RALE score concordance between the two independent Radiologists was excellent (interclass correlation coefficient = 0.93, 95% CI = 0.88-0.95, p<0.005). Binomial logistics regression identified a positive correlation with hospital admission (1.87 OR, 95% CI= 1.3-2.6, p<0.005), oxygen requirement (1.48 OR, 95% CI= 1.2-1.8, p<0.005) and invasive ventilation (1.2 OR, 95% CI= 1.0-1.3, p<0.005) for each 1-point increase in RALE score. For each one year increased in age, there was a negative correlation with recovery (0.05 OR, 95% CI= 0.92-1.0, p<0.01). RALE scores above three were positively associated with hospitalization (Youden Index 0.61, sensitivity 0.73, specificity 0.89) and above six were positively associated with ICU admission (Youden Index 0.67, sensitivity 0.91, specificity 0.78). Conclusion: The RALE score can be used as a surrogate to quantify the extent of COVID-19 infection and has an excellent inter-observer agreement. The RALE score could be used to prognosticate and identify patients at high risk of deterioration. Threshold values may also be applied to predict the likelihood of hospital and ICU admission.

Keywords: chest radiography, coronavirus, COVID-19, RALE score

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Authors: Mehmet Budak, Mehmet Fatih Ustundag

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Objectives: In this research, we studied the extent of exposure to physical violence and committing violence in patients diagnosed with schizophrenia in comparison to a control group consisting of patients with psychiatric diseases other than psychotic and mood disorders. Method: Between August 2019 and October 2019, a total of 100 hospitalized patients diagnosed with schizophrenia (clinically in remission, Brief Psychiatric Rate Scale < 30) were sequentially studied while undergoing inpatient treatment at Erenkoy Mental Health Training and Research Hospital. From the outpatient clinic, 50 patients with psychiatric disorders other than psychotic disorders or mood disorders were consecutively included as a control group. All participants were evaluated by the sociodemographic data that also questions the history of violence, physical examination, bilateral comparative hand, and forearm anterior-posterior and lateral radiography. Results: While 59% of patients with schizophrenia and 28% of the control group stated that they were exposed to physical violence at least once in a lifetime (p < 0,001); a defensive wound or fracture was detected in 29% of patients with schizophrenia and 2% of the control group (p < 0.001). On the other hand, 61% of patients diagnosed with schizophrenia, and 32% of the control group expressed that they committed physical violence at least once in a lifetime (p: 0.001). A self-destructive wound or fracture was detected in 53% of the patients with schizophrenia and 24% of the control group (p: 0,001). In the schizophrenia group, the rate of committing physical violence is higher in those with substance use compared to those without substance use (p:0.049). Also, wounds and bone fractures (boxer’s fracture) resulting from self-injury are more common in schizophrenia patients with substance use (p:0,002). In the schizophrenia group, defensive wounds and parry fractures (which are located in the hand, forearm, and arm usually occur as a result of a trial to shield the face against an aggressive attack and are known to be the indicators of interpersonal violence) are higher in those with substance use compared to those who do not (p:0,007). Conclusion: This study shows that exposure to physical violence and the rate of violence is higher in patients with schizophrenia compared to the control group. It is observed that schizophrenia patients who are stigmatized as being aggressive are more exposed to violence. Substance use in schizophrenia patients increases both exposure to physical violence and the use of physical violence. Physical examination and anamnesis that question violence are important tools to reveal the exposure to violence in patients. Furthermore, some specific bone fractures and wounds could be used to detect victimization even after plenty of time passes.

Keywords: fracture, physical violence, schizophrenia, substance use

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Maris Eugênia Dela Rosa, Ana Luiza Menegatti Pavan, Marcela De Oliveira, Diana Rodrigues De Pina, Luis Carlos Vulcano

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In medical veterinary as well as in human medicine, radiological study is essential for a safe diagnosis in clinical practice. Thus, the quality of radiographic image is crucial. In last year’s there has been an increasing substitution of image acquisition screen-film systems for computed radiology equipment (CR) without technical charts adequacy. Furthermore, to carry out a radiographic examination in veterinary patient is required human assistance for restraint this, which can compromise image quality by generating dose increasing to the animal, for Occupationally Exposed and also the increased cost to the institution. The image optimization procedure and construction of radiographic techniques are performed with the use of homogeneous phantoms. In this study, we sought to develop a homogeneous phantom of canine chest to be applied to the optimization of these images for the CR system. In carrying out the simulator was created a database with retrospectives chest images of computed tomography (CT) of the Veterinary Hospital of the Faculty of Veterinary Medicine and Animal Science - UNESP (FMVZ / Botucatu). Images were divided into four groups according to the animal weight employing classification by sizes proposed by Hoskins & Goldston. The thickness of biological tissues were quantified in a 80 animals, separated in groups of 20 animals according to their weights: (S) Small - equal to or less than 9.0 kg, (M) Medium - between 9.0 and 23.0 kg, (L) Large – between 23.1 and 40.0kg and (G) Giant – over 40.1 kg. Mean weight for group (S) was 6.5±2.0 kg, (M) 15.0±5.0 kg, (L) 32.0±5.5 kg and (G) 50.0 ±12.0 kg. An algorithm was developed in Matlab in order to classify and quantify biological tissues present in CT images and convert them in simulator materials. To classify tissues presents, the membership functions were created from the retrospective CT scans according to the type of tissue (adipose, muscle, bone trabecular or cortical and lung tissue). After conversion of the biologic tissue thickness in equivalent material thicknesses (acrylic simulating soft tissues, bone tissues simulated by aluminum and air to the lung) were obtained four different homogeneous phantoms, with (S) 5 cm of acrylic, 0,14 cm of aluminum and 1,8 cm of air; (M) 8,7 cm of acrylic, 0,2 cm of aluminum and 2,4 cm of air; (L) 10,6 cm of acrylic, 0,27 cm of aluminum and 3,1 cm of air and (G) 14,8 cm of acrylic, 0,33 cm of aluminum and 3,8 cm of air. The developed canine homogeneous phantom is a practical tool, which will be employed in future, works to optimize veterinary X-ray procedures.

Keywords: radiation protection, phantom, veterinary radiology, computed radiography

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Priya Kulkarni, Soumya Koppikar, Narendrakumar Wagh, Dhanshri Ingle, Onkar Lande, Abhay Harsulkar

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Cartilage is an extracellular matrix composed of aggrecan, which imparts it with a great tensile strength, stiffness and resilience. Disruption in cartilage metabolism leading to progressive degeneration is a characteristic feature of Osteoarthritis (OA). The process involves enzymatic depolymerisation of cartilage specific proteoglycan, releasing free glycosaminoglycan (GAG). This released GAG in synovial fluid (SF) of knee joint serves as a direct measure of cartilage loss, however, limited due to its invasive nature. Western Ontario and McMaster Universities Arthritis Index (WOMAC) is widely used for assessing pain, stiffness and physical-functions in OA patients. The scale is comprised of three subscales namely, pain, stiffness and physical-function, intends to measure patient’s perspective of disease severity as well as efficacy of prescribed treatment. Twenty SF samples obtained from OA patients were analysed for their GAG values in SF using DMMB based assay. LK 1.0 vernacular version was used to attain WOMAC scale. The results were evaluated using SAS University software (Edition 1.0) for statistical significance. All OA patients revealed higher GAG values compared to the control value of 78.4±30.1µg/ml (obtained from our non-OA patients). Average WOMAC calculated was 51.3 while pain, stiffness and function estimated were 9.7, 3.9 and 37.7, respectively. Interestingly, a strong statistical correlation was established between WOMAC function subscale and GAG (p = 0.0102). This subscale is based on day-to-day activities like stair-use, bending, walking, getting in/out of car, rising from bed. However, pain and stiffness subscale did not show correlation with any of the studied markers and endorsed the atypical inflammation in OA pathology. On one side, where knee pain showed poor correlation with GAG, it is often noted that radiography is insensitive to cartilage degenerative changes; thus OA remains undiagnosed for long. Moreover, active cartilage degradation phase remains elusive to both, patient and clinician. Through analysis of large number of OA patients we have established a close association of Kellgren-Lawrence grades and increased cartilage loss. A direct attempt to correlate WOMAC and radiographic progression of OA with various biomarkers has not been attempted so far. We found a good correlation in GAG levels in SF and the function subscale.

Keywords: cartilage, Glycosaminoglycan, synovial fluid, western ontario and McMaster Universities Arthritis Index

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Authors: Umedov Mekhroz, Griaznova Svetlana

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In connection with the growth of oncological diseases, the design of centers for diagnostics and the production of radiopharmaceuticals is the most relevant area of healthcare facilities. The design of new nuclear medicine centers should be carried out from the standpoint of solving the following tasks: the availability of medical care, functionality, environmental friendliness, sustainable development, improving the safety of drugs, the use of which requires special care, reducing the rate of environmental pollution, ensuring comfortable conditions for the internal microclimate, adaptability. The purpose of this article is to substantiate architectural and planning solutions, formulate recommendations and principles for the design of nuclear medicine centers and determine the connections between the production and medical functions of a building. The advantages of combining the production of radiopharmaceuticals and the department of medical care: less radiation activity is accumulated, the cost of the final product is lower, and there is no need to hire a transport company with a special license for transportation. A medical imaging department is a structural unit of a medical institution in which diagnostic procedures are carried out in order to gain an idea of the internal structure of various organs of the body for clinical analysis. Depending on the needs of a particular institution, the department may include various rooms that provide medical imaging using radiography, ultrasound diagnostics, and the phenomenon of nuclear magnetic resonance. The production of radiopharmaceuticals is an object intended for the production of a pharmaceutical substance containing a radionuclide and intended for introduction into the human body or laboratory animal for the purpose of diagnosis, evaluation of the effectiveness of treatment, or for biomedical research. The research methodology includes the following subjects: study and generalization of international experience in scientific research, literature, standards, teaching aids, and design materials on the topic of research; An integrated approach to the study of existing international experience of PET / CT scan centers and the production of radiopharmaceuticals; Elaboration of graphical analysis and diagrams based on the system analysis of the processed information; Identification of methods and principles of functional zoning of nuclear medicine centers. The result of the research is the identification of the design principles of nuclear medicine centers with the functions of the production of radiopharmaceuticals and the department of medical imaging. This research will be applied to the design and construction of healthcare facilities in the field of nuclear medicine.

Keywords: architectural planning solutions, functional zoning, nuclear medicine, PET/CT scan, production of radiopharmaceuticals, radiotherapy

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Authors: Nyathi Mpumelelo; Moeng Thabiso Maria

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BACKGROUND AND OBJECTIVES: Radiopharmaceuticals are used for diagnosis, treatment, staging and follow up of various diseases. However, there is concern that the ionizing radiation (gamma rays, α and ß particles) emitted by radiopharmaceuticals may result in exposure of radiographers and nurses with limited knowledge of the principles of radiation protection and safety, raising the risk of cancer induction. This study aimed at investigation radiation safety awareness levels among radiographers and nurses at a small tertiary hospital in South Africa. METHODS: An analytical cross-sectional study. A validated two-part questionnaire was implemented to consenting radiographers and nurses working in a Nuclear Medicine Department. Part 1 gathered demographic information (age, gender, work experience, attendance to/or passing ionizing radiation protection courses). Part 2 covered questions related to knowledge and awareness of radiation protection principles. RESULTS: Six radiographers and five nurses participated (27% males and 73% females). The mean age was 45 years (age range 20-60 years). The study revealed that neither professional development courses nor radiation protection courses are offered at the Nuclear Medicine Department understudy. However, 6/6 (100%) radiographers exhibited a high level of awareness of radiation safety principles on handling and working with radiopharmaceuticals which correlated to their years of experience. As for nurses, 4/5 (80%) showed limited knowledge and awareness of radiation protection principles irrespective of the number of years in the profession. CONCLUSION: Despite their major role of caring for patients undergoing diagnostic and therapeutic treatments, the nurses showed limited knowledge of ionizing radiation and associated side effects. This was not surprising since they never received any formal basic radiation safety course. These findings were not unique to this Centre. A study conducted in a Kuwaiti Radiology Department also established that the vast majority of nurses did not understand the risks of working with ionizing radiation. Similarly, nurses in an Australian hospital exhibited knowledge limitations. However, nursing managers did provide the necessary radiation safety training when requested. In Guatemala and Saudi Arabia, where there was shortage of professional radiographers, nurses underwent radiography training, a course that equipped them with basic radiation safety principles. The radiographers in the Centre understudy unlike others in various parts of the world demonstrated substantial knowledge and awareness on radiation protection. Radiations safety courses attended when an opportunity arose played a critical role in their awareness. The knowledge and awareness levels of these radiographers were comparable to their counterparts in Sudan. However, it was much more above that of their counterparts in Jordan, Nigeria, Nepal and Iran who were found to have limited awareness and inadequate knowledge on radiation dose. Formal radiation safety and awareness courses and workshops can play a crucial role in raising the awareness of nurses and radiographers on radiation safety for their personal benefit and that of their patients.

Keywords: radiation safety, radiation awareness, training, nuclear medicine

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Authors: Jinlu Ruan, Liang Chen, Bo Liu, Xiaoping Ouyang, Zhichao Zhu, Zhongbing Zhang, Shiyi He, Mengxuan Xu

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Plastic scintillators play an important role in the measurement of a mixed neutron/gamma pulsed radiation, neutron radiography and pulse shape discrimination technology. In some research, these luminescent properties are necessary that photons produced by the interactions between a plastic scintillator and radiations can be detected as much as possible by the photoelectric detectors and more photons can be emitted from the scintillators along a specific direction where detectors are located. Unfortunately, a majority of these photons produced are trapped in the plastic scintillators due to the total internal reflection (TIR), because there is a significant light-trapping effect when the incident angle of internal scintillation light is larger than the critical angle. Some of these photons trapped in the scintillator may be absorbed by the scintillator itself and the others are emitted from the edges of the scintillator. This makes the light extraction of plastic scintillators very low. Moreover, only a small portion of the photons emitted from the scintillator easily can be detected by detectors effectively, because the distribution of the emission directions of this portion of photons exhibits approximate Lambertian angular profile following a cosine emission law. Therefore, enhancing the light extraction efficiency and adjusting the emission angular profile become the keys for improving the number of photons detected by the detectors. In recent years, photonic crystal structures have been covered on inorganic scintillators to enhance the light extraction efficiency and adjust the angular profile of scintillation light successfully. However, that, preparation methods of photonic crystals will deteriorate performance of plastic scintillators and even destroy the plastic scintillators, makes the investigation on preparation methods of photonic crystals for plastic scintillators and luminescent properties of plastic scintillators with photonic crystal structures inadequate. Although we have successfully made photonic crystal structures covered on the surface of plastic scintillators by a modified self-assembly technique and achieved a great enhance of light extraction efficiency without evident angular-dependence for the angular profile of scintillation light, the preparation of photonic crystal structures with large area (the diameter is larger than 6cm) and perfect periodic structure is still difficult. In this paper, large area photonic crystals on the surface of scintillators were prepared by nanoimprint lithography firstly, and then a conformal layer with material of high refractive index on the surface of photonic crystal by atomic layer deposition technique in order to enhance the stability of photonic crystal structures and increase the number of leaky modes for improving the light extraction efficiency. The luminescent properties of the plastic scintillator with photonic crystals prepared by the mentioned method are compared with those of the plastic scintillator without photonic crystal. The results indicate that the number of photons detected by detectors is increased by the enhanced light extraction efficiency and the angular profile of scintillation light exhibits evident angular-dependence for the scintillator with photonic crystals. The mentioned preparation of photonic crystals is beneficial to scintillation detection applications and lays an important technique foundation for the plastic scintillators to meet special requirements under different application backgrounds.

Keywords: angular profile, atomic layer deposition, light extraction efficiency, plastic scintillator, photonic crystal

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

Abstract:

Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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