Medical Imaging Equipment Research Articles

Retracted: Maintenance and Management Technology of Medical Imaging Equipment Based on Deep Learning.

[This retracts the article DOI: 10.1155/2022/6361098.].

A Spatio-Temporal Analysis of OECD Member Countries’ Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference

Determinants of health care quality and efficiency are of importance to researchers, policy-makers, and public health officials as they allow for improved human capital and resource allocation as well as long-term fiscal planning. Statistical analyses used to understand determinants have neglected to explicitly discuss how missing data are handled, and consequently, previous research has been limited in inferential capability. We study OECD health care data and highlight the importance of transparency in the assumptions grounding the treatment of data missingness. Attention is drawn to the variation in ordinary least squares coefficient estimates and performance resulting from different imputation methods, and how this variation can undermine statistical inference. We also suggest that parametric regression models used previously are limited and potentially ill-suited for analysis of OECD data due to the inability to deal with both spatial and temporal autocorrelation. We propose the use of an alternative method in geographically and temporally weighted regression. A spatio-temporal analysis of health care system efficiency and quality of care across OECD member countries is performed using four proxy variables. Through a forward selection procedure, medical imaging equipment in a country is identified as a key determinant of quality of care and health outcomes, while government and compulsory health insurance expenditure per capita is identified as a key determinant of health care system efficiency.

Assessing the Level of Understanding (Knowledge) and Awareness of Diagnostic Imaging Students in Ghana on Artificial Intelligence and Its Applications in Medical Imaging.

Recent advancements in technology have propelled the applications of artificial intelligence (AI) in various sectors, including healthcare. Medical imaging has benefited from AI by reducing radiation risks through algorithms used in examinations, referral protocols, and scan justification. This research work assessed the level of knowledge and awareness of 225 second- to fourth-year medical imaging students from public universities in Ghana about AI and its prospects in medical imaging. This was a cross-sectional quantitative study design that used a closed-ended questionnaire with dichotomous questions, designed on Google Forms, and distributed to students through their various class WhatsApp platforms. Responses were entered into an Excel spreadsheet and analyzed with the Statistical Package for the Social Sciences (SPSS) software version 25.0 and Microsoft Excel 2016 version. The response rate was 80.44% (181/225), out of which 97 (53.6%) were male, 82 (45.3%) were female, and 2 (1.1%) preferred not to disclose their gender. Among these, 133 (73.5%) knew that AI had been incorporated into current imaging modalities, and 143 (79.0%) were aware of AI's emergence in medical imaging. However, only 97 (53.6%) were aware of the gradual emergence of AI in the radiography industry in Ghana. Furthermore, 160 people (88.4%) expressed an interest in learning more about AI and its applications in medical imaging. Less than one-third (32%) knew about the general basic application of AI in patient positioning and protocol selection. And nearly two-thirds (65%) either felt threatened or unsure about their job security due to the incorporation of AI technology in medical imaging equipment. Less than half (38% and 43%) of the participants acknowledged that current clinical internships helped them appreciate the role of AI in medical imaging or increase their level of knowledge in AI, respectively. Discussion. Generally, the findings indicate that medical imaging students have fair knowledge about AI and its prospects in medical imaging but lack in-depth knowledge. However, they lacked the requisite awareness of AI's emergence in radiography practice in Ghana. They also showed a lack of knowledge of some general basic applications of AI in modern imaging equipment. Additionally, they showed some level of misconception about the role AI plays in the job of the radiographer. Decision-makers should implement educational policies that integrate AI education into the current medical imaging curriculum to prepare students for the future. Students should also be practically exposed to the various incorporations of AI technology in current medical imaging equipment.

Assessment of Unjustified Brain Ct Requests in The Emergency Room of The Public Health Care System in Palestine Case Study: Hebron Governmental Hospital

Computed tomography (CT), which gives doctors a precise cross-sectional image that clearly shows the features of the organs, is one of the most crucial pieces of medical imaging equipment. In contrast to other equipment, the CT scan may expose the patient to a very high dose of radiation. As a result of the hazards associated with imaging patients, the use of CT images must be restricted, especially when imaging the head. The aim of the study is to the evaluation of the percentage of unjustified brain CT requests at the emergency rooms of the governmental health sector in Palestine. The 1957-built Hebron Governmental Hospital (HGH) was chosen as a research study case with a clinical capacity of 237 beds and 596 staff members; it is one of the key Palestinian government hospitals currently operating in the Hebron Governorate of the West Bank-Palestine. Samples were taken from the hospital's emergency room between the first of the year and the end of the year in 2021. 6152 or so brain CT images were gathered. Following that, 500 instances were picked for the investigation. Radiologists were asked to assess 100 of these 500 cases to determine whether the patient needed a scan or not. According to the study, 78% of brain tomography images were for unjustifiable reasons. Additionally, it was revealed by the study's results that unnecessary head CT scans were more common than those from retrospective investigations. In particular, non-specialized doctors who request cross-sectional brain imaging for no apparent reason are to blame for this because of their lack of knowledge.

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach.

Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient-monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph-theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyberrisk.

The progress of radiomics in thyroid nodules.

Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits and significantly enhancing the ability of medical imaging identification and prediction. The literature indicates that radiomics has a high potential in identifying and predicting thyroid nodules. So in this article, we explain the development, definition, and workflow of radiomics. And then, we summarize the applications of various imaging techniques in identifying benign and malignant thyroid nodules, predicting invasiveness and metastasis of thyroid lymph nodes, forecasting the prognosis of thyroid malignancies, and some new advances in molecular level and deep learning. The shortcomings of this technique are also summarized, and future development prospects are provided.

Assessment of the awareness of radiation protection and related concepts among nursing staff mainly working in diagnostic imaging units, cath-labs and operation theatres in Sri Lanka: A survey-based study

IntroductionThis study was carried out to evaluate the awareness of radiation protection, radiation types, medical imaging equipment and radiation effects among nurses for the first time in Sri Lanka. Since nurses are involved as direct caregivers in diagnostic and therapeutic radiography and radiation treatment procedures, they need to have a good understanding of the areas mentioned above. MethodsA self-administered questionnaire was used to collect data from 391 nurses working in hospitals, clinics, and other healthcare settings. Forwarded questions gathered the participants' demographic details and assessed their awareness of radiation protection, medical imaging equipment, radiation type and radiation effects. The average score per awareness area for each demographic characteristic was calculated based on the responses. Additionally, the percentage of participants who scored above 50 and 75 was calculated for each awareness area. ResultsThe majority were female participants (81.1%) and possessed a diploma in nursing (66.0%) with 10.8 years of average work experience. 92.3%, 74.7%, 69.8% and 22.3% of the participants scored more than 50 marks for the questions related to radiation protection, medical imaging equipment, radiation type and radiation effects, respectively. The level of nursing education and prior training in radiation protection significantly influenced all awareness areas, whereas participants with a graduate qualification in nursing and with prior radiation protection training scored the highest average marks. ConclusionBased on the scores obtained, the Sri Lankan nurses have satisfactory awareness of the essential concepts related to radiation types, medical imaging equipment and radiation protection. However, there is a significant lacking of awareness of radiation effects. This can be attributed to the fact that most participants did not have any formal training in radiation protection. The results implicate that proper training in radiation protection can significantly influence awareness of radiation protection and related concepts. Therefore, it is a timely requirement to initiate short awareness programs and continuing education programs on radiation protection for nurses working in specialised radiation units. Implications for practiceThe study suggests the necessity of initiating continuous education programs for nursing staff radiation protection to overcome the awareness gaps.

A Big Data Approach to Explore Medical Imaging Repositories Based on DICOM

Medical imaging repositories based on the DICOM format such as Picture Archiving and Communication Systems have a huge potential from a big data perspective. The study reported by this paper aimed to verify on how the big data lifecycle processes, from production to consumption might be implemented to take advantage of the DICOM standard and respective data sources. After identifying the different processes (i.e., data collection, integration, filtering, anonymization and enrichment, and knowledge extraction) the study demonstrate their implementation using an open-source application able to access the DICOM metadata independently of the medical imaging modalities and equipment manufacturers.

Guest Editorial Skin Image Analysis in the Age of Deep Learning

The papers in this special section focus on skin image analysis using deep learning applications. Skin is the largest organ of the human body, and is the first area of a patient assessed by clinical staff. The skin delivers numerous insights into a patient’s underlying health: for example, pale or blue skin suggests respiratory issues, unusually yellowish skin can signal hepatic issues, or certain rashes can be indicative of autoimmune issues. Dermatological complaints are the most prevalent reason that patients seek primary care, and images of the skin are the most easily captured form of medical image in healthcare. However, certain serious skin diseases are not reliably diagnosed by primary care. Out of all medical imaging datasets, skin images are the most similar to other standard computer vision datasets. However, significant and unique challenges still exist in this domain. In addition, there are remarkable visual similarities among skin diseases, and compared to other medical imaging domains, varying genetics, disease states, imaging equipment, and imaging conditions can significantly alter the appearance of the skin, making automated analysis in this domain highly challenging.

Transition from Analog to Digital Imaging for Image Guided Surgeries

C-arms and Cath-labs are sophisticated medical imaging equipment that finds extensive use in various operating room settings for image guided surgeries. Cath-labs are generally used for cardio-vascular procedures like angiography, angioplasty, and complex neurosurgeries. Mobile C-arms find usage in predominantly orthopaedic, urology, spine and general surgeries.

Weathering a demand shock: The impact of prior vertical scope on post‐shock firm response

AbstractResearch SummaryWe examine how and why pre‐existing vertical scope may cause differences in product market exit rates after sudden and exogenous decreases in demand. Our empirical context is the U.S. medical diagnostic imaging industry (2004–2009), wherein a major Medicare reform created a derived demand shock to equipment manufacturers. Using a difference‐in‐difference‐in‐difference design, we find integrated firms were more likely to exit than nonintegrated firms. Building on the literature conceptualizing firms' pre‐shock vertical scope as a representation of existing resources and governance choices, we explain that integrated and nonintegrated firms responded differently by leveraging their own distinctive capabilities. Our qualitative insights suggest that higher market exit of integrated firms was driven by their higher adjustment costs due to frictions across strategies for demand management versus cost reduction.Managerial SummaryThis study investigates whether having a dedicated sales force or utilizing third‐party distributors can help mitigate the adverse effect of an abrupt demand decrease on manufacturers. In the context of the US medical imaging equipment industry affected by the 2005 Deficit Reduction Act, we show that all firms implemented strategies for demand management, cost reduction, and product portfolio reconfiguration in response. Manufacturers using external distributors experienced fewer frictions across these strategies than those with an internal sales force and were less likely to exit. Thus, a firms' vertical scope can impact how they cope with environmental changes.

Research and Application of Seismic Performance of Medical Imaging Equipment

In view of the shortage of research on the seismic performance of medical imaging equipment, this paper investigates and summarizes the seismic regulatory requirements and seismic tests of medical imaging equipment, and focuses on the parameter selection, detection steps, result evaluation and detection equipment requirements of seismic detection of medical imaging equipment. The seismic test data of medical imaging equipment with various installation modes are analyzed, and the seismic performance of medical imaging equipment is analyzed and summarized.

Computed Tomography and Other Imaging Modalities in Pediatric Congenital Heart Disease

Congenital heart defects (CHD) are the most common congenital disabilities. Early and accurate diagnosis of coronary heart disease is very important for patients to get timely and effective treatment. In recent years, the accuracy of coronary heart disease diagnosis has been greatly improved with the development of medical imaging equipment and technology. At present, the clinical application of echocardiogram (echo), cardiovascular magnetic resonance (CMR) and computed tomography angiography (CTA) in coronary heart disease anatomy and function has increased significantly, which plays an important role in preoperative diagnosis, intraoperative monitoring, and postoperative recovery evaluation. However, each imaging technique has its indications. Providing the best examination plan for patients requires clinicians and radiologists’ close cooperation. Therefore, this study reviewed the imaging techniques for diagnosing coronary heart disease.

Purchasing high-cost medical devices and equipment in hospitals: a systematic review

ObjectivesTo systematically review academic literature for studies on any processes, procedures, methods or approaches to purchasing high-cost medical devices and equipment within hospitals in high-income countries.MethodsOn 13 August 2020, we...

Evaluation of Airflow Sensitivity to the Truncation Level of a Realistic Human Airway Model in an Accurate Numerical Simulation.

Background: The truncation level of human airways is an influential factor in the analysis of respiratory flow in numerical simulations. Due to computational limitations and limited resolution of diagnostic medical imaging equipment, a truncated geometry of airways is always investigated.Objective: This study aimed to employ image-based geometries with zero generation and 5th-generation truncation levels and assess bronchial airways truncation’s effect on tracheal airflow characteristics.Material and Methods: In this numerical study, computational fluid dynamics was employed to solve the respiratory flow in a realistic human airway model using the large eddy simulation technique coupling with the wall-adapting local eddy-viscosity (WALE) sub-grid scale model. The accuracy of numerical simulations was ensured by examining the large eddy simulation index of quality and Kolmogorov’s K-5/3 law.Results: The turbulent kinetic energy along the trachea has increased abnormally in the geometry with the zero-generation truncation level, and more severe fluctuations occurred in the velocity field of this geometry, which increased the tendency of each point to rotate. Compared to the extended model, the airflow’s more chaotic behavior prevented larger-scale vortices from forming in the geometry with the zero-generation truncation level. Larger-scale vortices in the extended model caused the primary flow passing next to the vortices to accelerate more intensely, increasing the wall shear stress peaks in this geometry.Conclusion: Eliminating the bronchial airways caused changes in tracheal airflow characteristics.

Australian medical imaging and world war one.

Twenty years after the birth of medical imaging from Röntgen's 1895 discovery, military authorities understood the advantage of visualising injuries of wounded soldiers and monitoring their treatment. In World War One, medical imaging equipment was difficult to use and had to be operated in challenging environments. The most common use of x-rays was the imaging of metallic foreign bodies such as bullets and shrapnel lodged within a soldier's body. The need to diagnose, manage war injuries and return soldiers to battle, led to medical imaging innovations including alternate means to record an image, better x-ray tubes and an early form of tomography. Such technological advancements were made by scientists serving their respective countries. With information sourced from the Australian War Memorial archives, this paper also focusses on the experiences of an Australian wartime radiographer. This investigation demonstrates the importance, sacrifice and skills of men and women who took on the difficult task of medical imaging in the first truly world-based conflict. It highlights how a new profession and associated technology emerged as an important tool in military medicine. Importantly, our profession's history within the context of military history should be preserved, while also honouring the legacy of individuals who contributed.

Minimally-invasive and non-invasive flexible devices for robust characterizations of deep tissues

<p indent="0mm">Deep-tissue signals often have a strong correlation with disease. Clinical detection and diagnosis of deep tissue mostly rely on large-scale instruments such as medical imaging equipment, which is costly and not conducive to long-term monitoring. Different from bulky and large-scale equipment, minimally invasive and non-invasive flexible devices can be worn in the body for a long time to achieve continuous acquisition of deep tissue signals, which is conducive to early diagnosis of diseases and precise and personalized treatment. In this review, we discuss the detection mechanisms of physiological and biochemical signals in deep tissues, focusing on the device fabrication methods, key performance indicators, and wireless data transmission techniques. Finally, several grand challenges and feasible solutions in this field are presented.

Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method.

With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.

Performance Evaluation System Based on Online Monitoring of Internet of Things

Research on performance evaluation system based on online monitoring of Internet of things technology, taking medical imaging equipment as an example. Build the performance management platform of the Internet of things through four aspects: data acquisition, network architecture, CPU, and display output. Select 100 medical imaging devices in clinical use in the hospital and divide them into the control group (50 sets) and the observation group (50 sets) according to different management modes. The control group adopts conventional performance management mode, and the observation group implements Internet of things performance management mode. Compare the differences of equipment operation performance, cost-effectiveness, and service effect between the two groups. The experimental results showed that the equipment utilization rate, operation rate, inspection schedule balance, and technician schedule balance of the observation group were better than those of the control group, and the difference was statistically significant ( P &lt; 0.05). The quarterly cost-benefit increase of x-ray examination equipment, CT equipment, Mr equipment, interventional radiotherapy equipment, color ultrasound instrument, and nuclear medicine equipment in the observation group was higher than that in the control group ( P &lt; 0.05). The satisfaction of imaging doctors, technicians, engineers, and patients with the equipment service quality of the observation group was higher than that of the control group; the difference was statistically significant ( P &lt; 0.05). It is proved that the Internet of things performance management platform of medical imaging equipment can solve the problems of untimely information collection and unscientific scheduling during equipment operation, realize the real-time display and statistics of equipment operation performance, and improve the operation efficiency and service level of medical imaging equipment.

National inventory of authorized diagnostic imaging equipment in Ghana: data as of September 2020.

Introductionto address the challenge of inadequate and non-equitable distribution of diagnostic imaging equipment, countries are encouraged to evaluate the distribution of installed systems and undertake adequate monitoring to ensure equitability. Ghana´s medical imaging resources have been analyzed in this study and evaluated against the status in other countries.Methodsdata on registered medical imaging equipment were retrieved from the database of the Nuclear Regulatory Authority and analyzed. The equipment/population ratio was mapped out graphically for the 16 regions of Ghana. Comparison of the equipment/population ratio was made with the situation in other countries.Resultssix hundred and seventy-four diagnostic imaging equipment units from 266 medical imaging facilities (2.5 units/facility), comprising computed tomography (CT), general X-ray, dental X-ray, single-photon emission computed tomography (SPECT) gamma camera, fluoroscopy, mammography and magnetic resonance imaging (MRI) were surveyed nationally. None of the imaging systems measured above the Organization for Economic Co-operation and Development (OECD) average imaging units per million populations (u/mp). The overall equipment/population ratio estimated nationally was 21.4 u/mp. Majority of the imaging systems were general X-ray, installed in the Greater Accra and Ashanti regions. The regional estimates of equipment/population ratios were Greater Accra (49.6 u/mp), Ashanti (22.4 u/mp), Western (21.4 u/mp), Eastern (20.6 u/mp), Bono East (20.0 u/mp), Bono (19.2 u/mp), Volta (17.9 u/mp), Upper West (16.7 u/mp), Oti (12.5 u/mp), Central (11.9 u/mp), Northern (8.9 u/mp), Ahafo (8.9 u/mp), Upper East (6.9 u/mp), Western North (6.7 u/mp), Savannah (5.5 u/mp) and North-East (1.7 u/mp).Conclusionmedical imaging equipment shortfall exist across all imaging modalities in Ghana. A wide inter-regional disparity in the distribution of medical imaging equipment exists contrary to WHO´s recommendation for equitable distribution. A concerted national plan will be needed to address the disparity.