جستجوی مقالات مرتبط با کلیدواژه « medical imaging » در نشریات گروه « پزشکی »

Complexity metrics have been suggested to characterize treatment plans based on machine parameters such as multileaf collimator (MLC) position. Several complexity metrics have been proposed and related to the Intensity-modulated radiation therapy (IMRT) quality assurance results. This study aims to evaluate aperture-based complexity metrics on MLC openings used in clinicaland establish a correlation between plan complexity and the gamma passing rate (GPR) for the IMRT plans. We implemented the aperture-based complexity metric on MLC openings of the IMRT treatment plan for breast  and central nervous system (CNS) cases . The modulation complexity score (MCS), the edge area metric (EAM), the converted area metric (CAM), the circumference/area (CPA), and the ratio monitor unit MU/Gy are evaluated in this study. The complexity score was calculated using Matlab. The MatriXX Evolution was used for dose verification. The dose distribution was  analyzed using the OmniPro-I'mRT program  and the gamma index was assessed using two criteria: 3%/3 mm and 3%/2 mm. The correlation between the calculated complexity score and the GPR  is analyzed using SPSS. The complexity score calculated by MCS, EAM, CAM, CPA, and MU/Gy shows breast plan is more complex than the CNS plan. The results of the correlation test of the complexity metric and GPR show that only the EAM metric shows a good correlation with GPR for both cases. EAM strongly correlates with the gamma pass rate. The MCS, CAM, CPA, and MU/Gy have a weak correlation with the GPR.

Neurodegenerative diseases can make life difficult and lead to death in many cases. They also can be difficult, time-consuming, and costly to diagnose with enough accuracy/certainty. Artificial intelligence (AI) has shown promise in tackling some of the challenges present in medical imaging and is anticipated to become a crucial tool in health care applications in the near future. In particular, deep learning methods have displayed great performance in various subfields of image processing, including but not limited to image segmentation, image synthesis, and image reconstruction. In this paper, many state-of-the-art applications of deep learning models in image processing were reviewed.

Informing patients is one of the important tasks of radiographers that correct performance of this ultimately leads to greater efficiency of the diagnostic process, reduction of costs, greater satisfaction, relief of anxiety, and acceleration of the patient’s recovery process.

This study aimed to determine the views of radiographers regarding the importance of informing patients in various fields of medical imaging.

In this descriptive cross-sectional study, 112/120 (93%) radiographers working in university hospitals of the west of Iran were studied by random sampling method and voluntarily. Data were collected using a researcher-made questionnaire that contains 13 specific questions on a 5- options Likert scale. After confirming the validity of content and reliability by retesting (α = 0.74), data were collected and analyzed using SPSS software version 15.

The knowledge of most radiographers regarding the importance of informing patients in the field of preparation before the examination (94.6%) and radiation protection (84.6%) was highly considered. 58% of radiographers considered informing on radiology equipment and examination procedure and 86.6% of radiographers considered informing on performing positions insignificant. Themajority of radiographers considered informing patients in the field of preparation before the examination (80.4%), radiation protection (87.5%), and performing positions (95.5%) as their duty, while 40.1% of radiographers considered patient education about the equipment of radiology department and examination procedure is doctor’s duty and a specialized matter.

Radiographers considered it important to inform patients about preparation before examination and radiation protection while patient educating about imaging equipment and how to perform the exam had no effect on the quality of the images produced. they stated informing patients about radiology equipment and examination procedure is the doctor’s duty.

Medical imaging is currently revolutionizing the diagnosis and treatment of a variety of diseases. Several imaging modalities have been developed based on advances in science and engineering. The impact of these imaging tools has been further improved with the advent of various modern chemistries, leading to the development of contrast agents that serve further to localize the detection of diseased tissues. Several researchers are recently involved in engineering contrast agents that can generate contrast differences between tissues in multiple imaging modalities, enabling cross-referenced determination of anomalies. To establish these multimodal imaging agents, nanovectors have gained significance due to their key physicochemical properties.  The major focus of this review is on the engineering strategies of nanovectors for multimodal medical imaging. The review conceives the basic principles, major parameters, and limitations of imaging modalities, namely, magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging at the beginning. Drawbacks of traditional contrast agents and the demand for new contrast agents are established. The importance of multimodal imaging and the need for a single contrast agent for these imaging applications are elaborated. Finally, the advantages, limitations, and design considerations of nanovectors based on magnetic and metallic nanoparticles with surface modifications to reduce toxicity and enable targeted delivery as multimodal imaging agents are also emphasized.

to assess the knowledge and perception of nuclear medicine by radiologists in French-speaking sub-Saharan Africa.

cross-sectional study conducted from April 8 to June 7 2020 including radiologists practicing in French-speaking sub-Saharan African countries. Data were collected electronically via a google form.

Of the 142 radiologists surveyed, 45.8% had already completed an internship in Europe, 3.52% in a nuclear medicine department and 72.54% had a nuclear medicine department in their country of practice. Among these radiologists, 21.13% knew the three main techniques of nuclear medicine and only 9.15% knew that nuclear medicine allows functional, metabolic and molecular studies. On average, 56.8% were aware of clinical indications for the main fields of nuclear medicine. In 47.18% of cases, they thought that scintigraphic imaging was more irradiating than radiological imaging, 71.1% knew about hybrid imaging techniques, 43.66% had read a scientific article on nuclear medicine, 4.93% had attended a nuclear medicine conference and 28.9% had recommended a scintigraphic imaging examination in their report. Half of them would like to see nuclear medicine and radiology merged into a single specialty and 95.77% considered it essential to create a nuclear medicine department in their country.

The level of knowledge of radiologists in French-speaking subSaharan Africa about nuclear medicine was, on the whole, unsatisfactory with a generally encouraging perception.

Application of nanoparticles have in the core of researchers attention for both imaging and therapy of cancers. This review article aimed to prepare an outline on recent applications of iodine nanoparticles (INPs) as theranostic agents in both diagnosis and therapies. Among various strategies are used in treatment of cancers, radiotherapy with radiopharmaceutical agents especially radioisotope of iodine displays satisfactory results for numerous types of cancers. In recent years, new investigations were done in order to develop the novel structure of INPs. These nanoprobes could act as efficient theranostic purposes. Iodine nanoparticles may be applied in nuclear medicine imaging and may be effective with mega voltage (MV) photons in cancer therapy, but this remains to be tested with different cancer cells. By using INPs, effective steps can be taken in the future in both diagnosis and treatment of cancers. This review emphasized the recent research findings on the application of INPs in medical imaging and therapeutic of cancers. The current challenges and the perspectives for their future applications were also represented and discussed.

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.

Fourier domain Optical coherence tomography (OCT) is a widely used high-resolution optical imaging technique. It is useful for various applications in medical imaging, such as ophthalmology (e.g. retinal imaging for diagnosing complications like glaucoma or macular degeneration), dermatology, oncology, and cardiology. The ability to noninvasively measure both the refractive index and thickness of biological tissues could have various medical applications and enable earlier disease detection. For example, observing changes in the refractive index can help distinguish between tissues with normal or abnormal function.

In this study, the theoretical framework for simultaneous measurement of the refractive index and physical thickness of multilayer systems is proposed and tested for two different samples, each having three layers, a glass/NaCl solution/glass sample and a glass/sugar solution/glass sample. The whole signal processing procedure and the experimental setup are described.

The refractive index and thickness of salt water and sugar water samples in the Fourier-domain OCT (FD-OCT) system were obtained. The resulting data were compared with reference measurements and showed a deviation of about 1% for the samples.

We tested the proposed framework for the simultaneous extraction of the refractive index and thickness of multilayer systems of salt water and sugar water from its FD-OCT data. We showed that the measured parameters were in agreement with reference amounts

Automated segmentation of abnormal tissues in medical images is considered as an essential part of those computer-aided detection and diagnosis systems which analyze medical images. However, automated segmentation of abnormalities is a challenging task due to the limitations of imaging technologies and complex structure of abnormalities, including low contrast between normal and abnormal tissues, shape diversity, appearance inhomogeneity, and the vague boundaries of abnormalities. Therefore, more intelligent segmentation techniques are required to tackle these challenges.

In this study, a method, which is called MMTDNN, is proposed to segment and detect medical image abnormalities. MMTDNN, as a multi-view learning machine, utilizes convolutional neural networks in a massive training strategy. Moreover, the proposed method has four phases of preprocessing, view generation, pixel-level segmentation, and post-processing. The International Symposium on Biomedical Imaging (ISBI)-2016 dataset is used for the evaluation of the proposed method.

The performance of the proposed method has been evaluated on the task of skin lesion segmentation as one of the challenging applications of abnormal tissue segmentation. Both qualitative and quantitative results demonstrate outstanding performance. Meanwhile, the accuracy of 0.973, the Jaccard index of 0.876, and the Dice similarity coefficient of 0.931 have been achieved.

In conclusion, the experimental result demonstrates that the proposed method outperforms state-of-the-art methods of skin lesion segmentation.

Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization. In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format. The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images. The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment.

Medical imaging modalities are used for different types of cancer detection and diagnosis. Recently, there have been a lot of studies on developing novel nanoparticles as new medical imaging contrast agents for the early detection of cancer. The aim of this review article is to categorize the medical imaging modalities accompanying with using nanoparticles to improve potential imaging for cancer detection and hence valuable therapy in the future. Nowadays, nanoparticles are becoming potentially transformative tools for cancer detection for a wide range of imaging modalities, including computed tomography (CT), magnetic resonance imaging, single photon emission CT, positron emission tomography, ultrasound, and optical imaging. The study results seen in the recent literature provided and discussed the diagnostic performance of imaging modalities for cancer detections and their future directions. With knowledge of the correlation between the application of nanoparticles and medical imaging modalities and with the development of targeted contrast agents or nanoprobes, they may provide better cancer diagnosis in the future.

Electrical impedance of tissues on low frequencies includes useful information about functional and structural changes in tissues. This property is used in Electrical Impedance Tomography (EIT) imaging modality for the detection of lesions in tissues.
The goal of this article is to study changes in electrical impedance of tissues in the presence of gold nanoparticles.
Spherical gold nanoparticles with size of 20-25 nm were synthesized with Turkevich method. Size distribution and shape of nanoparticles were characterized by transmission electron microscopy (TEM). Electrical impedance of two types of phantoms (chicken fat and muscle paste tissues) was measured by 4-electrode method with and without gold nanoparticles.
Results demonstrate a reduction in electrical impedance of tissues in the presence of gold nanoparticles. However, this reduction is not the same for fat and muscle tissues. Reductions in resistive impedance are for fat and muscle tissues on the frequency of 1 KHz, respectively. A reduction in electrical impedance is accompanied by a rise in electrical conductance leading to increase in EIT signal.
As signal enhancement is different for fat and muscle tissues; presence of gold nanoparticles could be used to improve EIT image contrast.

Appendicitis is the most common emergency surgical admission regardless of the age group. In case of delay in diagnosis, serious complications may occur, needing immediate actions. Complications of acute appendicitis like perforation, peritonitis, and sepsis are to justify massive negative appendectomies. In this study, it has been tried to collect data about negative appendectomy during last 5 years in Modarres hospital, Shahid Beheshti University of Medical sciences, Tehran, Iran, based on demographics and correlation with diagnostic studies before operation. This study is important in terms of economy and health care aspects.
Data were collected about all patients appendectomized during 5 years between 2012 and 2017 at Modarres hospital. There were 1454 records, of which 108 records were excluded from the study due to the lack of enough data, interval appendectomy, and clean appendectomy. Finally, end 1346 appendectomies were assessed.
There were 275 (20.4%) and 1071 (79.6%) negative appendectomy and suppurative appendicitis, respectively. Preoperative ultrasonography (US) was performed on 753 (56.0%) patients, of which 472 (62.7%) and 281 (37.3%) cases were coordinated with and different from pathologic records, respectively. Preoperative computed tomography (CT) scan was performed for 316 (23.5%) patients, which matched and differed pathologic records in 280 (88.6%) and 36 (11.4%) cases, respectively.
Diagnosis of appendicitis should still mainly be based on history, and clinical and laboratory data.

The limited spatial resolution of the gamma camera hinders the absolute quantification of planar images of small structures. The imaged structures are affected by partial volume effects (PVEs), which can spread activity and lead to underestimation of the regional distribution. The use of optimum planar parameters reduces the impact of the limited spatial resolution of the gamma camera and the statistical noise inherent to low photon count, thus improving quantification. In this study, we aimed to determine the optimum planar imaging parameters for small structures.
A thyroid protocol was used to acquire planar images of the spheres A, B, and C (16 mm, 12 mm, and 11 mm in diameter, respectively) whilst filled with a targeted activity concentration of technetium-99m. One sphere was mounted at the centre of the Jaszczak Phantom and the other two adjacent to its walls using capillary stems fitted on the spheres. The phantom was filled with distilled water. The targeted activity concentrations used were 74 kBq/mL, 100 kBq/mL, 150 kBq/mL, and 300 kBq/mL. Images of the same count per pixel were acquired on 64 64, 128 128, 256 256, 512 512, and 1024 1024 pixels using a vertical detector mounted 5 cm above the phantom. All the images were quantified using ImageJ software, version 1.48a, Java 1.70_51 [64-bit].
The optimum planar imaging parameters established were a matrix size of 128 128 pixels and technetium-99m solution of activity concentration of 300 kBq/ml.
The use of optimal imaging parameters reduces the impact of PVEs, leading to improved quantitative accuracy.

Teaching styles and methods have been constantly changing in the recent years. In the 1980s and 90s, the world was introduced to various developed devices, such as smart boards and early generation smart phones that had an immediate innovative effect on education. These advancements has resulted in a considerable improvement in the current educational techniques. The innovations in medical techniques and their quality is of paramount importance. The aim of this study was to provide an innovative and useful software on radiological applications and contribute to the related literature.
The Medical Imaging Teacher (MIT) program was created and the prototype application was implemented on the Android platform for free use. The programing and testing of the usability of the application were performed by the users of the Google Play Store.
In this program, we have developed new software to simulate the X-ray images of the body by considering the peak kilovoltage (kVp), milliamperage per second (mAs), and film focus distance (FFD) values. The application has been downloaded more than 1,000 times without paid advertising. We enrolled 131 participants, who made comments and gave 4.8 points on average.
It can be concluded that with innovative digital programs, such as the MIT, the medical machine-based learning and medical applications have risen to new levels. The software on medical testing and examination is gaining increasing popularity among the health-related applications for smart phones.

Agent technology is an emerging and promising research area in software technology, which increasingly contributes to the development of value-added information systems for large healthcare organizations due to its capability to automatically modify themselves in response to changes in their operating environment. Multi agent system (MAS) approach is the construction of a complex system as a set of entities, called agents, interacting among them in order to perform the system tasks. Patie Maes, the director of MIT media lab, deems that agent is “a computer system, which locates in dynamic and complex environment, can autonomically sense the environment and act accordingly to complete its tasks or goals”. Agent is a computing entity with four features of autonomy, reactivity, interaction and initiative. A MAS is composed of a large number of agents and other computational artifacts. These agents are goal-oriented components, that is, they are modeled as entities that pursue goals and choose for execution those actions that will potentially contribute to satisfy them. These choices depend on their information about the environment, past experiences and themselves. Agents are also social because they need to interact with other agents to achieve the satisfaction of goals, and these interactions are modeled in terms of information, requests and informs. In healthcare, medical imaging is a service-oriented, data-intensive and technology-driven environment; therefore it requires combining many computerized systems with different user environments to provide quality efficient services in less time. In such environments, MAS is well-suited to describe the interactions between agents (e.g., information exchanged, related tasks or reasons for that interaction) and the way in which the agent adapts to changes in it (e.g., new roles or goals) or its environment. Medical imaging is a science but also a discipline of action that often requires a decision. The complexity of decision-making, especially in medical imaging, comes from the uncertainty, for example the uncertainty of knowledge, uncertainty about the protocols and the uncertainty of the procedure used. A multi-agent system (MAS) for decision-aiding support uses and combines distributed and heterogeneous systems to ease the extraction of useful information in order to select appropriate protocol or procedure.