We searched and retrieved literature on the topic of medical image processing published on SCI journals in the past 10 years. We then imported the retrieved literature into TDA for data cleanup before data analysis and processing by EXCLE and UCINET to generate tables and figures that could indicate disciplinary correlation and research hotspots from the perspective of bibliometrics. The results indicated that people in Europe and USA were leading researchers on medical image processing with close international cooperation. Many disciplines contributed to the fast development of medical image processing with intense interdisciplinary researches. The papers that we found show recent research hotspots of the algorithm, system, model, image and segmentation in the field of medical image processing. Cluster analysis on key words of high frequency demonstrated complicated clustering relationship.
Integrating visualization toolkit and the capability of interaction, bidirectional communication and graphics rendering which provided by HTML5, we explored and experimented on the feasibility of remote medical image reconstruction and interaction in pure Web. We prompted server-centric method which did not need to download the big medical data to local connections and avoided considering network transmission pressure and the three-dimensional (3D) rendering capability of client hardware. The method integrated remote medical image reconstruction and interaction into Web seamlessly, which was applicable to lower-end computers and mobile devices. Finally, we tested this method in the Internet and achieved real-time effects. This Web-based 3D reconstruction and interaction method, which crosses over internet terminals and performance limited devices, may be useful for remote medical assistant.
In recent years, the technologies based on the electrical properties (EPs) of human tissue, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), have become one of the most popular research subjects in biomedicine. Compared with EIT and MREIT, the magnetic resonance electrical property tomography (MR-EPT) is a new technique using nondestructive EPs method. MR-EPT reconstructs the electrical conductivity and permittivity of the biological tissues based on the radio frequency field of the magnetic resonance imaging (MRI) system. It can obtain an accurate and high resolution image without current injection. In this paper, several methods for the EPs are reviewed, especially the MR-EPT. The theory, advantages and prospects of MR-EPT's are elaborated. The method of specific absorption rate (SAR) based on it is also introduced. MR-EPT is deserved further research and should be given more attention by the researchers. All this evolution based on MREPT can give new energy to the medical diagnosis.
This paper describes a simulation of microwave brain imaging for the detection of hemorrhagic stroke. Firstly, in the research process, the formula of DebyeⅡwas used to study tissues of brain and blood clot so that microwave frequency band was confirmed for imaging. Then a model with electromagnetic characteristics of brain was built on this basis. In addition, an ultra-wideband (UWB) Vivaldi antenna is designed to use for transmitting and receiving microwave signals of widths 1.7 GHz to 4 GHz. Microwave signals were transmitted and received when the antenna revolved around the brain model. Symmetric position de-noising method was used to eliminate the strong background noise signals, and finally confocal imaging method was applied to get brain imaging. Blood clot was distinguished clearly from result of imaging and position error was less than 1 cm.
Through magnetic resonance electrical properties tomography (MR-EPT), electrical conductivity and permittivity of biological tissues could be reconstructed based on radio frequency field of magnetic resonance imaging (MRI) system. High precision and high resolution image could be obtained without current injection. In this study, XFDTD software was used to establish a finite element model of the human breast. Simulation was delivered at the Larmor frequency of 128 MHz by a 16-channel radio frequency coil. Conductivity and permittivity of the mammary tissue was reconstructed according to the B1+ field's amplitude and phase of forward problem. Anti-noise performance of the algorithm was studied by adding noise to B1+ field. The conductivity and permittivity's average relative error between simulation results and dielectric constant was 4.71% and 11.32%, respectively. With a signal-to-noise ratio of >30 dB, the noise added would not affect imaging results. This study demonstrated that high precision and high resolution image could be obtained by MR-EPT without excitation. MR-EPT had excellent feasibility and developing potential in early detection of diseases.
With the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer’s filming work. In this paper, the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described. The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data, which explains the broad application prospect of deep learning in the medical field. This paper developed a set of intelligent quality control system for auxiliary filming, and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals, which effectively verified the feasibility and stability of the quality control system.
ObjectiveTo summarize the current status and update of the use of medical imaging in risk prediction of pancreatic fistula following pancreaticoduodenectomy (PD).MethodA systematic review was performed based on recent literatures regarding the radiological risk factors and risk prediction of pancreatic fistula following PD.ResultsThe risk prediction of pancreatic fistula following PD included preoperative, intraoperative, and postoperative aspects. Visceral obesity was the independent risk factor for clinically relevant postoperative pancreatic fistula (CR-POPF). Radiographically determined sarcopenia had no significant predictive value on CR-POPF. Smaller pancreatic duct diameter and softer pancreatic texture were associated with higher incidence of pancreatic fistula. Besides the surgeons’ subjective intraoperative perception, quantitative assessment of the pancreatic texture based on medical imaging had been reported as well. In addition, the postoperative laboratory results such as drain amylase and serum lipase level on postoperative day 1 could also be used for the evaluation of the risk of pancreatic fistula.ConclusionsRisk prediction of pancreatic fistula following PD has considerable clinical significance, it leads to early identification and early intervention of the risk factors for pancreatic fistula. Medical imaging plays an important role in this field. Results from relevant studies could be used to optimize individualized perioperative management of patients undergoing PD.
Objective To conduct a systematic and comprehensive bibliometric analysis related to medical imaging in coronavirus disease 2019 (COVID-19) published by Chinese authors, and indicate the current status and guidance for coping with the challenges brought by long COVID. Methods Literature records pertaining to COVID-19 imaging were retrieved from the Web of Science (WOS) and Chinese Science Citation Database (CSCD) with a date range of January 1st, 2020 to December 31st, 2022. CiteSpace was used to analyze the popular topics and generate a visual map to further refine research trends. Results The search enrolled 2229 publications (1771 publications from WOS and 458 from CSCD). The monthly volume of publications showed a significant increase followed by a gradual decline, and then maintained a stable level. Clustering analysis of keywords and co-citations revealed that the research hotspots in COVID-19 imaging were clinical features, imaging differential diagnosis, and the application of artificial intelligence. Keyword burst analysis showed that “deep learning” was the most intensive keyword, while “attention mechanism” had the longest burst duration. Conclusions In recent years, the research in the field of COVID-19 imaging has made steady progress. Artificial intelligence has received the most attention. Clinical features, imaging features and artificial intelligence assisted diagnosis are the top of most concerns. The future focus of COVID-19 imaging research may be on improving algorithm models to expand the application of artificial intelligence in disease diagnosis, treatment, and management.