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.
A new one-time registration method was developed in this research for hand-eye calibration of a surgical robot to simplify the operation process and reduce the preparation time. And a new and practical method is introduced in this research to optimize the end-tool parameters of the surgical robot based on analysis of the error sources in this registration method. In the process with one-time registration method, firstly a marker on the end-tool of the robot was recognized by a fixed binocular camera, and then the orientation and position of the marker were calculated based on the joint parameters of the robot. Secondly the relationship between the camera coordinate system and the robot base coordinate system could be established to complete the hand-eye calibration. Because of manufacturing and assembly errors of robot end-tool, an error equation was established with the transformation matrix between the robot end coordinate system and the robot end-tool coordinate system as the variable. Numerical optimization was employed to optimize end-tool parameters of the robot. The experimental results showed that the one-time registration method could significantly improve the efficiency of the robot hand-eye calibration compared with the existing methods. The parameter optimization method could significantly improve the absolute positioning accuracy of the one-time registration method. The absolute positioning accuracy of the one-time registration method can meet the requirements of the clinical surgery.
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.
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.
Objective The baseline, clinical characteristics, and risk factors were analyzed in the stroke registry program of the Xinjiang Production Constraction Corp’s Hospital aimed to aid the clinical management and stroke prevention. Method A single center prospective method based on Lausanne Stroke Registry was used in this study. Patients generally, past history, living conditions, onset to treatment time, the stroke scale were collected with 1 year follow up. The investigators of follow up were single blinded. Result Eight hundred and ten ischemic stroke patients were included, of which 478 (59.01%) were male, 332 (40.99%) were female. The average age of these patients was 66.50±10.66 years. One year loss rate of follow up was 4.64%. Seven hundred and sixty-nine patients were diagnosis as acute cerebral infarction, 41 patients were TIA. The median time from onset to treatment was 15 hours. Lacunar infarction was the most common type with 334 (43.43%) patients. The average score of the National Institutes of Heath Stroke Scale was 5.55±7.24. The incidence of carotid artery plaque was 82.2%. Conclution Xinjiang region has its own characteristics of stroke with a higher carotid artery plaque rate and thrombolytic therapy ratio. Good stroke registration system could standardize the clinical behavior and promote the continuous improvement of medical quality.
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.
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.
In the process of positron emission tomography (PET) data acquiring, respiratory motion reduces the quality of PET imaging. In this paper, we present a correction method using three level grids B-spline elastic method to correct denoised and reorganized sinograms for respiratory motion correction. Using GATE simulates NCAT respiratory motion model to generate raw data which are used in experiment, the experiment results showed a significantly improved respiratory image with higher quality of PET, and the motion blur and structural information were fixed. The results proved the method of this paper would be effective for the elastic registration.
By dividing the evolution of the U.S. clinical trial registration system into three phases—emergence, inception, and maturity—this study systematically traces its half-century development and reveals the underlying tensions and institutional logic. The U.S. clinical trial registration system is not merely a technical instrument, but a comprehensive institutional platform reconciling the conflicts among scientific rationality, commercial interests, and the public’s right to know. The emergence phase (1971—1985) originated from the establishment and public disclosure of the International Cancer Database to meet cancer research needs and safeguard patients’ survival rights. The inception phase (1986—2004) unfolded against the backdrop of the FDA’s drug approval crisis, with the construction of major disease registration systems breaking the regulatory deadlock and achieving an "incremental revolution". The maturity phase (2004—2016) centered on controlling publication bias and advancing institutionalization and legalization. The 2004 paroxetine incident galvanized global consensus on trial registration, and the 2007 U.S. Congressional mandate marked the pivotal turning point toward a fully mature system. Today, China still faces low registration rates and insufficient legal constraints. Drawing on the U.S. experience, China should prioritize institutional publicness, legal enforceability, and the containment of publication bias to strategically upgrade its clinical trial registration system.