Objective To know the current status of multinational clinical trials (MNCTs) in East Asia, and to find the characters of MNCTs in countries/regions. Methods We downloaded the trial records of East Asia on May 8, 2008 from ClinicalTrials.gov and analyzed the data. Results The number of clinical trials sponsored by industry was 125 in China Mainland, 196 in Taiwan, 134 in Hong Kong, 264 in Korea, and 231 in Japan, respectively. Of the total 654 clinical trials in East Asia, 307 (47%) trials were MNCTs, most of which were conducted by Euro-American pharmaceutical companies, such as Pfizer, AstraZeneca, GlaxoSmithKline, Sanofi-Aventis and Bristol-Myers Squibb. Main therapeutic areas were cancer, followed by CNS diseases, cardiovascular diseases, infectious diseases, diabetes mellitus and respiratory diseases. Trials in phaseⅢwere 198 (65%), in phaseⅣ32 (10%), others in phaseⅡorⅠ. One hundred and ninety trials (62%) were double-blind clinical trials, about half of them using placebo. The characters of clinical trials in China were: ① Most of MNCTs were large scale trials with big sample size and many study sites; ② Most of local trials were phase Ⅲ trials; ③ There were no phase Ⅰ trials. The characters in Taiwan, Hong Kong and Korea were: 1) Most of the trials (84% in Taiwan and 93% in Hong Kong, 72% in Korea) were MNCTs, 2) A lot of large scale trials were conducted with each other. The characters of clinical trials in Japan were: ① MNCTs were only 17%, ② Large scale trials were fewer. Conclusion In East Asia, MNCTs are developing because of the initiation of the Europe and America pharmaceutical giants. It seems that the regulation in each country influence the development pattern of East Asia.
Objective To investigate the number of Chinese clinical trials and the completeness of registered information on the source of their funding. Methods We searched the five primary registers in the World Health Organization’s International Clinical Trial Registration Platform to identify Chinese clinical trials, calculated the number Chinese clinical trials with specific funding and evaluated the completeness of the information on the source of this funding. Results We identified 383 registered Chinese clinical trials, of which 219 (27 trials per year on average) were registered in clinicaltrials.gov, 94 in the Chinese Clinical Trial Register Center (113 per year on average), 62 in controlled-trials.com (12.4 per year on average) and 8 (1.6 per year on average) in the Australian and New Zealand Clinical Trials Registry. 360 trials had some information on their source of funding: 230 from the mainland of China (62 funded by colleges/universities, 47 by national/local organizations, 47 by the Ministry of Science and Technology, 34 by hospitals, 28 by commercial organizations, 9 by international foundations, and 3 by the Ministry of Health), 117 from Hongkong and 13 from Taiwan. The information in the registers on the source of funding was incomplete. Conclusion The number of funded Chinese clinical trials in these registers is too small. The registrations should be improved to improve the completeness of information on the source of funding. It is important to disseminate the importance of registering clinical trials and doing so in a local register to promote the transparency and accessibility of trial registration.
Medical image registration is very challenging due to the various imaging modality, image quality, wide inter-patients variability, and intra-patient variability with disease progressing of medical images, with strict requirement for robustness. Inspired by semantic model, especially the recent tremendous progress in computer vision tasks under bag-of-visual-word framework, we set up a novel semantic model to match medical images. Since most of medical images have poor contrast, small dynamic range, and involving only intensities and so on, the traditional visual word models do not perform very well. To benefit from the advantages from the relative works, we proposed a novel visual word model named directional visual words, which performs better on medical images. Then we applied this model to do medical registration. In our experiment, the critical anatomical structures were first manually specified by experts. Then we adopted the directional visual word, the strategy of spatial pyramid searching from coarse to fine, and the k-means algorithm to help us locating the positions of the key structures accurately. Sequentially, we shall register corresponding images by the areas around these positions. The results of the experiments which were performed on real cardiac images showed that our method could achieve high registration accuracy in some specific areas.
ObjectivesTo analyze the development of acupuncture registered trials based on WHO international clinical trial registration platform (ICTRP) in the past 5 years.MethodsWHO ICTRP database was electronically searched to collect acupuncture-related clinical trials registered from January 1st, 2014 to December 31st, 2018. Two reviewers independently screened items, extracted data, and descriptive analysis was performed for the included trials.ResultsThe results showed that there were 1 556 registered clinical trials on acupuncture, and the most registered year was 2017. China was in the main country in applying for acupuncture-related clinical trials, however, the most registered unit was Kyung Hee University in Korea. The trials were mainly interventional research, mostly used randomized, blinded methods, and design modes were mainly based on parallel trials. In clinical trial phase, the majority were in the clinical trial period of treatment of new technologies. The field of clinical research was expected to be on pain in the future.ConclusionsAlthough acupuncture research is currently in a good stage of development, it should still value on the quality and innovative training of relevant trials, strengthen Chinese ties with other countries, focus on regional, domestic and international cooperation, expand research types, and enhance acupuncture applicability.
Objective To evaluate the quality of the registration information for trials sponsored by China registered in the WHO International Clinical Trial Registration Platform (ICTRP) primary registries or other registries that meet the requirements of the International Committee Medical Journal Editor (ICMJE). Methods We assessed the registration information for trials registered in the 9 WHO primary registries and one other registry that met the requirements of ICJME as of 15 October 2008. We analyzed the trial registration data set in each registry and assessed the registration quality against the WHO Trial Registration Data Set (TRDS). We also evaluated the quality of the information in the Source(s) of Monetary or Material Support section, using a specially prepared scale. Results The entries in four registries met the 20 items of the WHO TRDS. These were the Chinese Clinical Trial Registration Center (ChiCR), Australian New Zealand Clinical Trials Registry (NZCTR), Clinical Trials Registry – India (CTRI), and Sri Lanka Clinical Trials Registry (SLCTR). Registration quality varied among the different registries. For example, using the Scale of TRDS, the NZCTR scoreda median of 19 points, ChiCTR (median = 18 points), ISRCTN.org (median = 17 points), and Clinical trials.org (median = 12 points). The data on monetary or material support for ChiCTR and ISRCTN.org were relatively complete and the score on our Scale for the Completeness of Funding Registration Quality ranged from ChiCTR (median = 7 points), ISRCTN.org (median = 6 points), NZCTR (median = 3 points) to clinicaltrials.gov (median = 2 points). Conclusion Further improvements are needed in both the quantity and quality of trial registration. This could be achieved by full completion of the 20 items of the WHO TRDS. Future research should assess ways to ensure the quality and scope of research registration and the role of mandatory registration of funded research.
We applied Demons and accelerated Demons elastic registration algorithm in radiotherapy cone beam CT (CBCT) images, We provided software support for real-time understanding of organ changes during radiotherapy. We wrote a 3D CBCT image elastic registration program using Matlab software, and we tested and verified the images of two patients with cervical cancer 3D CBCT images for elastic registration, based on the classic Demons algorithm, minimum mean square error (MSE) decreased 59.7%, correlation coefficient (CC) increased 11.0%. While for the accelerated demons algorithm, MSE decreased 40.1%, CC increased 7.2%. The experimental verification with two methods of demons algorithm obtained the desired results, but the small difference appeared to be lack of precision, and the total registration time was a little long. All these problems need to be further improved for accuracy and reducing of time.
Deformable image registration plays a crucial role in medical image analysis. Despite various advanced registration models having been proposed, achieving accurate and efficient deformable registration remains challenging. Leveraging the recent outstanding performance of Mamba in computer vision, we introduced a novel model called MCRDP-Net. MCRDP-Net adapted a dual-stream network architecture that combined Mamba blocks and convolutional blocks to simultaneously extract global and local information from fixed and moving images. In the decoding stage, we employed a pyramid network structure to obtain high-resolution deformation fields, achieving efficient and precise registration. The effectiveness of MCRDP-Net was validated on public brain registration datasets, OASIS and IXI. Experimental results demonstrated significant advantages of MCRDP-Net in medical image registration, with DSC, HD95, and ASD reaching 0.815, 8.123, and 0.521 on the OASIS dataset and 0.773, 7.786, and 0.871 on the IXI dataset. In summary, MCRDP-Net demonstrates superior performance in deformable image registration, proving its potential in medical image analysis. It effectively enhances the accuracy and efficiency of registration, providing strong support for subsequent medical research and applications.
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.
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.
