Objective To introduce the imaging modalities used for the evaluation of postoperative complications of orthotopic liver transplantation (OLT) and to present the imaging findings of these complications. Methods The literatures related to the imaging methods and imaging manifestations of OLT complications were reviewed. Results Ultrasound was the initial imaging technique used for the detection of complications in the early postoperative period. Spiral CT and MRI yielded more accurate and comprehensive evaluation of postoperative complications in later stage. So far, there had been no specific imaging findings to suggest rejection reaction. The spectrum of imaging manifestations of OLT complications, such as vascular complications, biliary complications, liver parenchymal complications, and so on, were summarized and illustrated. Conclusion Imaging examination (especially ultrasound, spiral CT and MRI ) plays an important role in the evaluation of postoperative complications of OLT.
Objective To explore the clinical value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid （Gd-EOB-DTPA） enhanced magnetic resonance （MR） imaging for cirrhosis-related nodules. Methods Nineteen patients who were suspected cirrhosis with lesions of liver were prospectively included for Gd-EOB-DTPA enhanced MR imaging test between Nov. 2011 and Jan. 2013. The hepatobiliary phase （HBP） images were taken in 20 minutes after agents’ injection. The images were diagnosed independently in two groups： group A， including the plain phase and dynamic phase images； group B， including plain phase， dynamic phase， and HBP phase images. The signal intensity （SI） of lesions in HBP images， background liver SI， and background noise standard deviation were measured by using a circular region of interest， then the lesion signal to noise ratio （SNR） and contrast signal to noise ratio （CNR） were calculated. Results Nineteen patients had 25 tumors in all， including 18 hepatocelluar carcinoma （HCC） and 7 regenerative nodule （RN） or dysplastic nodule （DN）， with the diameter ranged from 0.6 cm to 3.2 cm （average 1.3 cm） . Sixteen HCC manifested hypo SI relative to the normal liver， while 2 HCC manifested hyper SI at HBP. Five HCC had cystic necrosis with the necrotic area， and there were no enhancement in artery phase， while performed flocculent enhancement at HBP. Six RN or DN showed hyper SI while another 1 showed iso SI to background liver at HBP. The diagnostic accuracy rates of group A and group B were 80.0% （20/25） and 92.0% （23/25）. SNR of RN or DN at HBP was 132.90±17.21， and of HCC was 114.35±19.27， while the CNR of RN or DN was 19.47±8.20， and of HCC was 112.15±33.52. Conclusion Gd-EOB-DTPA enhanced MR imaging can improve the diagnosis capacity of cirrhosis-related nodules， so as to develop more accurate and reasonable treatment options.
In the field of artificial intelligence (AI) medical imaging, data annotation is a key factor in all AI development. In the traditional manual annotation process, there are prominent problems such as difficult data acquisition, high manual labor intensity, strong professionalism and low labeling quality. Therefore, an intelligent multimodal medical image annotation system is urgently needed to meet the requirements of labeling. Based on the image cloud, West China Hospital of Sichuan University collected the multimodal image data of hospital and allied hospitals, and designed a multi-modal image annotation system through information technology, which integrated various image processing algorithms and AI models to simplify the image data annotation. With the construction of annotation system, the efficiency of data labeling in the hospitals is improved, which provides necessary data support for the AI image research and related industry construction in the hospital, so as to promote the implementation of artificial intelligence industry related to medical images in the hospital.
Since January 2020, due to the epidemic of coronavirus disease 2019, all universities in China have postponed their studies or even suspend their studies. In response to the teaching policy of “suspending class, but keeping teaching and learning” , college teachers have rapidly changed into online teaching mode. However, how to ensure the quality and effect of online teaching still needs further exploration. Through analyzing the course characteristics of medical imaging diagnostics and students’ learning situations, this study discusses how to design detailed online teaching projects and improve the teaching quality and how to select online software suitable for the course. A questionnaire survey was conducted to evaluate the effect of online teaching during the spring course in 2020, selecting a total of 297 clinical and other undergraduate students of grade 2017 from West China School of Medicine of Sichuan University. The results showed that the detailed online teaching programs including “video learning” “distance teaching” “periodic examination” “weakness tutorial” were helpful to the learning process agreed by the majority of students. During the epidemic period, online teaching method can help students master the content of medical imaging diagnosis. In the era of Internet, the “online+offline” teaching mode is expected to be popularized in the future.
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.
Currently, the medical imaging methods based on artificial intelligence are developing rapidly, and the related literature reports are increasing year by year. However, there is no special reporting standard, and the reporting of the results is not standardized. In order to improve the report quality of this kind of research and help readers and evaluators evaluate the quality of this kind of research more scientifically, a checklist for artificial intelligence in medical imaging (CLAIM) was put forward abroad. This paper introduces the content of CLAIM and explains its items.
Central lung cancer is a common disease in clinic which usually occurs above the segmental bronchus. It is commonly accompanied by bronchial stenosis or obstruction, which can easily lead to atelectasis. Accurately distinguishing lung cancer from atelectasis is important for tumor staging, delineating the radiotherapy target area, and evaluating treatment efficacy. This article reviews domestic and foreign literatures on how to define the boundary between central lung cancer and atelectasis based on multimodal images, aiming to summarize the experiences and propose the prospects.
The task of automatic generation of medical image reports faces various challenges, such as diverse types of diseases and a lack of professionalism and fluency in report descriptions. To address these issues, this paper proposes a multimodal medical imaging report based on memory drive method (mMIRmd). Firstly, a hierarchical vision transformer using shifted windows (Swin-Transformer) is utilized to extract multi-perspective visual features of patient medical images, and semantic features of textual medical history information are extracted using bidirectional encoder representations from transformers (BERT). Subsequently, the visual and semantic features are integrated to enhance the model's ability to recognize different disease types. Furthermore, a medical text pre-trained word vector dictionary is employed to encode labels of visual features, thereby enhancing the professionalism of the generated reports. Finally, a memory driven module is introduced in the decoder, addressing long-distance dependencies in medical image data. This study is validated on the chest X-ray dataset collected at Indiana University (IU X-Ray) and the medical information mart for intensive care chest x-ray (MIMIC-CXR) released by the Massachusetts Institute of Technology and Massachusetts General Hospital. Experimental results indicate that the proposed method can better focus on the affected areas, improve the accuracy and fluency of report generation, and assist radiologists in quickly completing medical image report writing.
Objective To elaborate on the statistical analysis methods for evaluating the accuracy of imaging diagnostic tests in a multiple-reader multiple-case (MRMC) design through formula derivation and real cases. Methods This study consisted of two parts: theoretical derivation and a real case study. The theoretical part discussed in detail the principles and procedures of MRMC statistical analysis methods, particularly the Obuchowski-Rockette (OR) and Dorfman-Berbaum-Metz (DBM) methods. The real case included 100 subjects, of whom 67 had disease. Four readers interpreted all the cases based on both traditional film imaging methods and digital imaging methods. OR and DBM methods were employed for data analysis. Results The real case showed that the OR and DBM methods had a high degree of consistency, with only slight differences in the confidence intervals. Conclusion It is recommended to use the OR and DBM methods for the statistical analysis of imaging diagnostic test accuracy, ensuring that the impact of reader factors on the evaluation results is fully considered. The results from the OR and DBM methods are relatively similar; when applying these methods in practice, one should consider the specific characteristics of the data and the research design to choose the appropriate analysis method. Besides, there are still challenges when applying the OR and DBM methods, such as software implementation and missing data handling, which require further exploration.