多模态影像学检查在诊断早期强直性脊柱炎骶髂关节疾病中的效果

目的:探究在早期强直性脊柱炎骶髂关节疾病诊断中不同放射影像学检查方法的应用效果.方法:抽取2018年5月-2020年1月本院收治的早期强直性脊柱炎骶髂关节疾病患者65例作为研究对象,所有患者均开展X线、CT、MRI影像学检查,对比三种不同影像学检查方法的检出率、影像学特征.结果:X线、CT、MRI检出率分别为38.46...     

Priorities in optimisation of medical X-ray imaging--a contribution to the debate

A simplistic approach to optimising medical imaging is to use the lowest effective dose to the patient that does not jeopardise a correct diagnosis. With limited resources and over 1000 different types of X-ray examinations, it is not always easy to set the right priorities and to decide how to perform the optimisation. Recent research shows that the 'Rose model' for the detection of specific structures does not hold for realistic backgrounds. A reasonable conclusion regarding methods for optimisation is therefore not to use contrast-detail phantoms. Phantoms producing clinically realistic background images or real clinical images-modified with respect to quantum noise levels-are preferred. The images should be evaluated using visual grading or receiver operating characteristic methods. The quality of many common X-ray investigations, performed with projection techniques, is not limited by quantum noise. For these, the radiation dose to the patient can be lowered without seriously affecting the outcome of the detection task. For computed tomography (CT) investigations, the obscuring effect of anatomical structures and anatomical noise is less pronounced than in projection techniques. For CT, true optimisation in terms of a trade-off between radiation dose and image quality is therefore more likely to be effective. Both the number of CT examinations performed per year and the effective dose per examination are increasing owing to the technical advances in CT--jointly leading to a steady increase in the collective dose from CT examinations. Moreover, the smaller influence of the anatomical background in CT gives a high correlation between detection tasks and radiation dose. Thus, a reasonable view to take on which examinations to optimise is to give priority to CT examinations. The recommended distribution of a full working week for optimisation, based on the relative lifetime risk of lethal cancer from diagnostic X rays and the total collective dose from CT, is to use three out of five days to optimise CT examinations, of which one day should be devoted to paediatric CT.     

IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System

Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient’s data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage.     

Clear-PEM: a dedicated PET camera for improved breast cancer detection

Positron emission mammography (PEM) can offer a non-invasive method for the diagnosis of breast cancer. Metabolic images from PEM using 18F-fluoro-deoxy-glucose, contain unique information not available from conventional morphologic imaging techniques like X-ray radiography. In this work, the concept of Clear-PEM, the system presently developed in the frame of the Crystal Clear Collaboration at CERN, is described. Clear-PEM will be a dedicated scanner, offering better perspectives in terms of position resolution and detection sensitivity.     

Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it is helpful to enhance the working productivity of radiologists. It could take the curve down with the downright contribution of radiologists, rapid detection of COVID-19, and to overcome this current pandemic with the proven efficacy.     

A Novel Technique for Early Detection of COVID-19

COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the disease. Recent research findings have suggested that radiology images, such as X-rays, contain significant information to detect the presence of COVID-19 virus in early stages. However, to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible. Artificial Intelligence (AI) techniques, machine learning in particular, are known to be very helpful in accurately diagnosing many diseases from radiology images. This paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography (CT) scan images. The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network (AIRRCNN), which uses machine learning techniques for classifying data. We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network, because we do not find it being used in the literature. Also, we conduct principal component analysis, which is used for dimensional deduction. Experimental results of our method have demonstrated an accuracy of about 99%, which is regarded to be very efficient.     

Nodule detection in digital chest radiography: effect of system noise

Apart from the image content that is the reproduction of anatomy and possible lesions, an X-ray image also contains system noise due to the limited number of photons and other internal noise sources in the system (image plate artefacts, electronic noise, etc.). The aim of this study was to determine the extent to which the system noise influences the detection of subtle lung nodules in five different regions of the chest. This was done by conducting a receiver operating characteristic (ROC) study with five observers on two different sets of images; clinical chest X-ray images and images of a LucAl phantom at similar dose levels found in the different regions of the chest. In both image types, mathematically simulated nodules (with a full-width-at-fifth-maximum of 10 mm) were added to the images at varying contrast levels. As a measure of the influence of system noise on the detection of subtle lung nodules, the ratio between the contrast needed to obtain an area under the ROC curve of 0.80 in the system noise images to that needed in the clinical images was used. The contrast ratio between system noise images and clinical images ranged from approximately 0.02 (in the hilar region) to 0.18 (in the lower mediastinal region). The maximum difference in contrast needed for the corresponding system noise images, collected at the lowest and the highest dose represented in the anatomical image, was a factor of 2. These results indicate that probably no region in a chest X-ray image is limited by the number of quanta to the detector for the detection of 10 mm lung nodules when a radiation dose corresponding to a system with speed class 200 (leading to a detector dose of approximately 9 muGy behind the parenchyma) is used.     

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-RayImages

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue. In this paper, we develop a novel deep learning architecture to better classify the Covid-19 X-ray images. To do so, we first propose a novel multi-habitat migration artificial bee colony (MHMABC) algorithm to improve the exploitation/exploration of artificial bee colony (ABC) algorithm. After that, we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost. Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters. Furthermore, it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some well-known benchmark datasets.     

高分辨率CT在尘肺早期诊断及抗矽治疗效果评估中的应用

尘肺(pneumoconiosis)是一种严重危害煤矿工人健康的职业病。由于传统X线胸片的诸多局限,不利于尘肺的早期诊断。随着高分辨率CT(High-Resolution CT,HRCT)在临床应用中的普及,越来越多的临床医生将HRCT用于尘肺的诊断,这样不仅可以提高尘肺早期的检出率,也为临床评估尘肺抗矽治疗的效果提供了方法和依据。     

Nanomaterials for X-ray imaging: gold nanoparticle enhancement of X-ray scatter imaging of hepatocellular carcinoma

We present the development of a new imaging technique for the early diagnosis of hepatocellular carcinoma that utilizes surface-modified gold nanoparticles in combination with X-ray imaging. Tissues labeled with these electron-dense particles show enhanced X-ray scattering over normal tissues, distinguishing cells containing gold nanoparticles from cells without gold in X-ray scatter images. Our results suggest that this novel approach could enable the in vivo detection of tumors as small as a few millimeters in size.     

X线、CT与MRI在早期强直性脊柱炎诊断中的价值

目的:探究MRI、CT、X线在早期强直性脊柱炎(AS)中的诊断价值。方法:选择2016.01-2018.12间我院接收的早期AS患者共100例,根据检查方式分成X线组41例、CT组30例及MRI组29例,对三组检查结果进行观察。结果:MRI组检出率明显较X线组和CT组高,MRI在骶髂关节各方面的诊断结果均优于X线组和CT组,差异显著,P<0.05。结论:MRI、CT、X线均可应用于诊断早期AS,但MRI较CT、X线检出率更高,更具优势。     

基于光声原理的火灾复合气体探测技术

监测燃烧过程中产生的气体（主要是CO）来探测火灾逐渐成为火灾探测中的一个重要领域。各种现有的气体传感器灵敏度比较低，不利于火灾的早期报警,利用基于光声原理的复合气体探测技术来进行火灾探测，能极大地提高探测器的灵敏度。将CO和CO2的检测结合起来，可降低探测器的误报率，有利于提高早期报警。     

Patient doses and image quality in digital chest radiology

Chest X-ray examination is one of the most frequently required procedures used in clinical practice. For studying the image quality of different X-ray digital systems and for the control of patient doses during chest radiological examinations, the standard anthropomorphic lung/chest phantom RSD 330 has been used and exposed in different digital modalities available in Slovakia. To compare different techniques of chest examination, a special software has been developed that enables researchers to compare digital imaging and communications in medicine header images from different digital modalities, using a special viewer. In this paper, this special software has been used for an anonymous correspondent audit for testing image quality evaluation by comparing various parameters of chest imaging, evaluated by 84 Slovak radiologists. The results of the comparison have shown that the majority of the participating radiologists felt that the highest image quality is reached with a flat panel, assessed by the entrance surface dose value, which is approximately 75% lower than the diagnostic reference level of chest examination given in the Slovak legislation. Besides the results of the audit, the possibilities of using the software for optimisation, education and training of medical students, radiological assistants, physicists and radiologists in the field of digital radiology will be described.     

Threat Objects Detection in X-ray Images Using an Active Vision Approach

X-ray testing for baggage inspection has been increasingly used at airports, reducing the risk of terrorist crimes and attacks. Nevertheless, this task is still being carried out by human inspectors and with limited technological support. The technology that is being used is not always effective, as it depends mainly on the position of the object of interest, occlusion, and the accumulated experience of the inspector. Due to this problem, we have developed an approach that inspects X-ray images using active vision in order to automatically detect objects that represent a threat. Our method includes three steps: detection of potential threat objects in single views based on the similarity of features and spatial distribution; estimation of the best-next-view using Q-learning; and elimination of false alarms based on multiple view constraints. We tested our algorithm on X-ray images that included handguns and razor blades. In the detection of handguns we registered good results for recall and precision (Re = 67%, Pr = 83%) along with a high performance in the detection of razor blades (Re = 82%, Pr = 100%) taking into consideration 360 inspections in each case. Our results indicate that non-destructive inspection actively using X-ray images, leads to more effective object detection in complex environments, and helps to offset certain levels of occlusion and the internal disorder of baggage.     

X光片和多排螺旋CT、MR对骨关节创伤的诊断对比

目的:分析对比X光片和多排螺旋CT、MR对骨关节创伤的诊断效果。方法:回顾性分析我院在2017年4月-2018年5月接诊的43例骨关节创伤患者,分别进行东芝64排螺旋CT扫描,并用X光片(A组)、三维重组技术处理图片(B组)和动态MR(C组),对比三组骨关节骨折的检出率。结果:60例患者经最后确诊的骨折有70处,A组应用X光片检出51处,检出率为72.9%;B组采用多排螺旋CT三维重组技术检出67处,检出率为95.7%;C组采用动态MR检出70处,检出率为100%。对比三组,差异有统计学意义,P<0.01。结论:动态MR可有效提高骨折的诊断效果,降低漏诊和误诊率,在临床上值得推广应用。     

Implementation of DRLs in the UK

This article describes the system of regulation and practical guidance that has been developed in the UK for implementing the requirement in the EC Medical Exposure Directive that all Member States shall promote the establishment and use of diagnostic reference levels (DRLs) for medical X-ray examinations. In particular, it describes the role of two national patient dose databases maintained by NRPB, which provide important sources of information on which formally adopted numerical values for 'national DRLs' will be based. One database deals with radiographic and fluoroscopic examinations and the recommended 'national reference doses' from the latest review of this database are discussed. The other database deals specifically with computed tomography (CT) examinations, which now account for 50% of the collective dose to the UK population from all medical X rays and are consequently of particular radiation protection concern. The first analysis of this CT database is still underway, but some encouraging indications of a reduction in patient dose for some CT examinations are reported. Progress in formally adopting numerical values for 'national DRLs', as required by the UK regulations, and the provision of authoritative guidance on the implementation of DRLs at the local level, are also discussed.     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.     

The quality criteria concept: an introduction and overview

The Quality Criteria concept has been developed over the past decade in Europe and applied with success for conventional X ray examinations of adult and paediatric patients. This concept has recently been extended to computed tomography, and will also be available for digital radiography in the near future. The aim of the Quality Criteria for diagnostic images is to define a level of performance considered necessary to produce images of standard quality for a particular anatomical region and which could address any clinical indication. The image criteria include anatomical criteria, which relate to the visualisation or critical reproduction of anatomical features and also physical criteria measurable by objective means. The diagnostic reference doses introduced by ICRP 73 are an essential element of the Quality Criteria concept given for examinations on standard-sized patients. The Quality Criteria should provide a logical framework for radiation protection initiatives which links the desired or acceptable outcome, in terms of image quality, of a radiological examination, to the radiographic technique required to produce this outcome and the patient dose which should be achievable.     

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPP-Unet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRES-ASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.     

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.