Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography (CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.
Based on previous evidence-based researches and teaching experience, our team conducted literature and book review, and summarized 4 requirements, 1) effect measure calculation and conversion, 2) registration of evidence-based research, 3) evidence-based research database and 4) quality evaluation tools and reporting guidelines. We developed an online platform of evidence-based medicine research helper using the front-end and back-end technology, which can be accessed using www.ebm-helper.cn. Currently, the online tool has included 46 scenarios for effect measure calculation and conversion, introduction of 7 evidence-based research registration platforms, 26 commonly used databases for evidence-based research and 29 quality evaluation tools and reporting guidelines. This online tool can help researchers to solve specific problems encountered in different stages of evidence-based medicine research. Promoting the application of this platform in evidence-based medicine will help researchers to use the tool scientifically and improve research efficiency.
The medical image registration between preoperative three-dimensional (3D) scan data and intraoperative two-dimensional (2D) image is a key technology in the surgical navigation. Most previous methods need to generate 2D digitally reconstructed radiographs (DRR) images from the 3D scan volume data, then use conventional image similarity function for comparison. This procedure includes a large amount of calculation and is difficult to archive real-time processing. In this paper, with using geometric feature and image density mixed characteristics, we proposed a new similarity measure function for fast 2D-3D registration of preoperative CT and intraoperative X-ray images. This algorithm is easy to implement, and the calculation process is very short, while the resulting registration accuracy can meet the clinical use. In addition, the entire calculation process is very suitable for highly parallel numerical calculation by using the algorithm based on CUDA hardware acceleration to satisfy the requirement of real-time application in surgery.
Non-rigid medical image registration is a popular subject in the research areas of the medical image and has an important clinical value. In this paper we put forward an improved algorithm of Demons, together with the conservation of gray model and local structure tensor conservation model, to construct a new energy function processing multi-modal registration problem. We then applied the L-BFGS algorithm to optimize the energy function and solve complex three-dimensional data optimization problem. And finally we used the multi-scale hierarchical refinement ideas to solve large deformation registration. The experimental results showed that the proposed algorithm for large deformation and multi-modal three-dimensional medical image registration had good effects.
Objective To analyze the current research status, characteristics and development trends of traditional medicine-related clinical trials registration, and to provide ideas and directions for further development of traditional medicine clinical trials. Methods The International Traditional Medicine Clinical Trial Registry (ITMCTR) database was searched by computer from inception to June 30, 2024, with unlimited trial registration status, to collect all the clinical trials on traditional medicine, and analyze the basic information of the trials, the diseases studied and the interventions. Results A total of 4 349 clinical trials related to traditional medicine were included, with the number of registrations peaking in the second half of 2020, and showing a steady upward trend after 2023. The trial sponsors of the study covered 9 countries and a total of 34 provinces/autonomous regions/municipalities in China, led by Beijing, Shanghai, Guangdong, Sichuan, and Zhejiang provinces, accounting for 69.72% of the total. The financial support for the studies was dominated by local government funds in various provinces and cities, accounting for 29.66%. Disease types studied were mainly circulatory system diseases, musculoskeletal system or connective tissue diseases, and tumor diseases, accounting for 29.91% of the total. A total of 3 751 (86.3%) clinical trials were interventional studies, of which randomized parallel control was predominant, and 213 large-sample studies with a sample size of more than 1 000 cases were included. A total of 20 types of interventions were involved, of which 1 114 (29.86%) clinical trials utilized oral prescription of herbal medicine interventions. Conclusion Clinical trial enrollment in traditional medicine has increased overall, but with significant geographic unevenness. Oral herbal soup/granule intervention studies are the mainstream hotspots. It is recommended to strengthen international cooperation, enrich the types of interventions, refine the trial design, and raise the awareness of researchers about the registration of high-quality traditional medicine clinical trials.
Complete three-dimensional (3D) tooth model provides essential information to assist orthodontists for diagnosis and treatment planning. Currently, 3D tooth model is mainly obtained by segmentation and reconstruction from dental computed tomography (CT) images. However, the accuracy of 3D tooth model reconstructed from dental CT images is low and not applicable for invisalign design. And another serious problem also occurs,i.e. frequentative dental CT scan during different intervals of orthodontic treatment often leads to radiation to the patients. Hence, this paper proposed a method to reconstruct tooth model based on fusion of dental CT images and laser-scanned images. A complete 3D tooth model was reconstructed with the registration and fusion between the root reconstructed from dental CT images and the crown reconstructed from laser-scanned images. The crown of the complete 3D tooth model reconstructed with the proposed method has higher accuracy. Moreover, in order to reconstruct complete 3D tooth model of each orthodontic treatment interval, only one pre-treatment CT scan is needed and in the orthodontic treatment process only the laser-scan is required. Therefore, radiation to the patients can be reduced significantly.
Neurosurgery navigation system, which is expensive and complicated to operate, has a low penetration rate, and is only found in some large medical institutions. In order to meet the needs of other small and medium-sized medical institutions for neurosurgical navigation systems, the scalp localization system of neurosurgery based on augmented reality (AR) theory was developed. AR technology is used to fuse virtual world images with real images. The system integrates computed tomography (CT) or magnetic resonance imaging (MRI) with the patient's head in real life to achieve the scalp positioning. This article focuses on the key points of Digital Imaging and Communications in Medicine (DICOM) standard, three-dimensional (3D) reconstruction, and AR image layer fusion in medical image visualization. This research shows that the system is suitable for a variety of mobile phones, can achieve two-dimensional (2D) image display, 3D rendering and clinical scalp positioning application, which has a certain significance for the auxiliary neurosurgical head surface positioning.
Mitral valve disease is one of the most popular heart valve diseases. Precise positioning and displaying of the valve characteristics is necessary for the minimally invasive mitral valve repairing procedures. This paper presents a multi-resolution elastic registration method to compute the deformation functions constructed from cubic B-splines in three dimensional ultrasound images, in which the objective functional to be optimized was generated by maximum likelihood method based on the probabilistic distribution of the ultrasound speckle noise. The algorithm was then applied to register the mitral valve voxels. Numerical results proved the effectiveness of the algorithm.