摘要:目的:探討5·12汶川8.0級地震中顱面部外傷的影像學表現特點。方法: 回顧性分析自2008年5月12~31日因地震顱面部外傷在我院行CT、MRI檢查的傷員251例,其中CT檢查248例,MRI檢查16例。結果:放射檢查陽性162例,陽性率為64.54%,以40～49歲年齡組最多,為53例,其中男性41例。在放射檢查陽性中,多發傷112例(約69.13%),多類型顱面部外傷同時并存103例(約63.58%)。主要損傷發生率依次為軟組織損傷(35.50%),骨折(22.94%),腦挫裂傷(21.21%),硬膜下及硬膜外血腫(10.40%),其它(共約9.92%)。結論: 地震造成顱面部外傷人群主要為40～49歲中年男性,多發傷、多類型顱面部外傷多見,并以軟組織損傷、骨折、腦挫裂傷、硬膜下及硬膜外血腫較常見。Abstract: Objective: To describe the imaging features of head and face injured patients after the Wenchuan earthquake. Methods: The radiological information of 251 victims who were suspicious of head and face injury and underwent CT or MRI examinations from 12 May to 31 May 2008 was analysed retrospectively. There were 248 and 16 cases underwent CT or MRI examinations respectively. Results: One hundred and sixtytwo cases(64.54% )were positive. There were 53 cases in the 4049 years old age group, of which 41 were male. In patients with positive findings, 112 cases (about 63.58%) were comprised of several types of head and face injury. The incidence of the main injury type included: soft tissue injury (35.50%), fracture (22.94%), cerebral contusion (21.21%), subdural and epidural hematoma (12.40%), others (9.92%). Conclusions: The males with head and face injury in 4049 years old group were the major injured people in this earthquake. Head and face injury accompanied by multiple system injuries, the existence of several types of head and face injury at the same time were common. Among all the injury types, soft tissue injury, fracture, contusion, subdural and epidural hematoma were relatively commom.
【Abstract】ObjectiveTo study the advances in use of imaging in the evaluation of living donor liver. Methods The literatures in recent years on the use of imaging in evaluation of living donor liver were reviewed. ResultsPreoperative computed tomography (CT) and magnetic resonance imaging (MRI) in the donor allowed accurate determination of liver volume and rough determination of macrovesicular hepatic steatosis of the liver. CT angiography could assess the anatomy of hepatic artery, portal vein and hepatic veins. Intraoperative cholangiography allowed detection of the anatomy of the biliary tree. ConclusionImaging techniques are widely used in the evaluation of liver volume, vasculature and biliary system in the living donor liver.
In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.
ObjectiveTo summarize the current research progress in the prediction of the efficacy of neoadjuvant therapy of breast cancer based on the application of artificial intelligence (AI) and radiomics. MethodThe researches on the application of AI and radiomics in neoadjuvant therapy of breast cancer in recent 5 years at home and abroad were searched in CNKI, Google Scholar, Wanfang database and PubMed database, and the related research progress was reviewed. ResultsAI had developed rapidly in the field of medical imaging, and molybdenum target, ultrasound and magnetic resonance imaging combined with AI had been deepened and expanded in different degrees in the application research of breast cancer diagnosis and treatment. In the research of molybdenum target combined with AI, the high sensitivity of molybdenum target to microcalcification was mostly used to improve the accuracy of early detection and diagnosis of breast cancer, so as to achieve the clinical purpose of early detection and diagnosis. However, in terms of prediction of neoadjuvant efficacy research of breast cancer, ultrasound and magnetic resonance imaging combined with AI were more prevalent, and their popularity remained unabated. ConclusionIn the monitoring of neoadjuvant therapy for breast cancer, the use of properly designed AI and radiomics models can give full play to its role in the predicting the curative effect of neoadjuvant therapy, and help to guide doctors in clinical diagnosis and treatment and evaluate the prognosis of breast cancer patients.
Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.
