Registration Scheme Research Articles

SPECT and CT misregistration reduction in [99mTc]Tc-MAA SPECT/CT for precision liver radioembolization treatment planning.

Respiration and body movement induce misregistration between static [99mTc]Tc-MAA SPECT and CT, causing lung shunting fraction (LSF) and tumor-to-normal liver ratio (TNR) errors for 90Y radioembolization planning. We aim to alleviate the misregistration between [99mTc]Tc-MAA SPECT and CT using two registration schemes on simulation and clinical data. In the simulation study, 70 XCAT phantoms were modeled. The SIMIND Monte Carlo program and OS-EM algorithm were used for projection generation and reconstruction, respectively. Low-dose CT (LDCT) at end-inspiration was simulated for attenuation correction (AC), lungs and liver segmentation, while contrast-enhanced CT (CECT) was simulated for tumor and perfused liver segmentation. In the clinical study, 16 patient data including [99mTc]Tc-MAA SPECT/LDCT and CECT with observed SPECT and CT mismatch were analyzed. Two liver-based registration schemes were studied: SPECT registered to LDCT/CECT and vice versa. Mean count density (MCD) of different volumes-of-interest (VOIs), normalized mutual information (NMI), LSF, TNR, and maximum injected activity (MIA) based on the partition model before and after registration were compared. Wilcoxon signed-rank test was performed. In the simulation study, compared to before registration, registrations significantly reduced estimation errors of MCD of all VOIs, LSF (Scheme 1: - 100.28%, Scheme 2: - 101.59%), and TNR (Scheme 1: - 7.00%, Scheme 2: - 5.67%), as well as MIA (Scheme 1: - 3.22%, Scheme 2: - 2.40%). In the clinical study, Scheme 1 reduced 33.68% LSF and increased 14.75% TNR, while Scheme 2 reduced 38.88% LSF and increased 6.28% TNR compared to before registration. One patient may change from 90Y radioembolization untreatable to treatable and other patients may change the MIA up to 25% after registration. NMI between SPECT and CT was significantly increased after registrations in both studies. Registration between static [99mTc]Tc-MAA SPECT and corresponding CTs is feasible to reduce their spatial mismatch and improve dosimetric estimation. The improvement of LSF is larger than TNR. Our method can potentially improve patient selection and personalized treatment planning for liver radioembolization.

A Multivariate Local Descriptor Registration Method for Surface Topography Evaluation

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.

Rotational Distortion and Compensation in Optical Coherence Tomography with Anisotropic Pixel Resolution.

Accurate image registration is essential for eye movement compensation in optical coherence tomography (OCT) and OCT angiography (OCTA). The spatial resolution of an OCT instrument is typically anisotropic, i.e., has different resolutions in the lateral and axial dimensions. When OCT images have anisotropic pixel resolution, residual distortion (RD) and false translation (FT) are always observed after image registration for rotational movement. In this study, RD and FT were quantitively analyzed over different degrees of rotational movement and various lateral and axial pixel resolution ratio (RL/RA) values. The RD and FT provide the evaluation criteria for image registration. The theoretical analysis confirmed that the RD and FT increase significantly with the rotation degree and RL/RA. An image resizing assisting registration (RAR) strategy was proposed for accurate image registration. The performance of direct registration (DR) and RAR for retinal OCT and OCTA images were quantitatively compared. Experimental results confirmed that unnormalized RL/RA causes RD and FT; RAR can effectively improve the performance of OCT and OCTA image registration and distortion compensation.

Applying Multi-Metric Deformable Image Registration for Dose Accumulation in Combined Cervical Cancer Radiotherapy

(1) Purpose: Challenges remain in dose accumulation for cervical cancer radiotherapy combined with external beam radiotherapy (EBRT) and brachytherapy (BT) as there are many large and complex organ deformations between different treatments. This study aims to improve deformable image registration (DIR) accuracy with the introduction of multi-metric objectives for dose accumulation of EBRT and BT. (2) Materials and methods: Twenty cervical cancer patients treated with EBRT (45-50 Gy/25 fractions) and high-dose-rate BT (≥20 Gy in 4 fractions) were included for DIR. The multi-metric DIR algorithm included an intensity-based metric, three contour-based metrics, and a penalty term. Nonrigid B-spine transformation was used to transform the planning CT images from EBRT to the first BT, with a six-level resolution registration strategy. To evaluate its performance, the multi-metric DIR was compared with a hybrid DIR provided by commercial software. The DIR accuracy was measured by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) between deformed and reference organ contours. The accumulated maximum dose of 2 cc (D2cc) of the bladder and rectum was calculated and compared to simply addition of D2cc from EBRT and BT (ΔD2cc). (3) Results: The mean DSC of all organ contours for the multi-metric DIR were significantly higher than those for the hybrid DIR (p ≤ 0.011). In total, 70% of patients had DSC > 0.8 using the multi-metric DIR, while 15% of patients had DSC > 0.8 using the commercial hybrid DIR. The mean ΔD2cc of the bladder and rectum for the multi-metric DIR were 3.25 ± 2.29 and 3.54 ± 2.02 GyEQD2, respectively, whereas those for the hybrid DIR were 2.68 ± 2.56 and 2.32 ± 3.25 GyEQD2, respectively. The multi-metric DIR resulted in a much lower proportion of unrealistic D2cc than the hybrid DIR (2.5% vs. 17.5%). (4) Conclusions: Compared with the commercial hybrid DIR, the introduced multi-metric DIR significantly improved the registration accuracy and resulted in a more reasonable accumulated dose distribution.

Inheritance and innovation: On the strategy of traditional Chinese medicine registration and supervision

<p indent="0mm">The overall development of the pharmaceutical industry has promoted the reconstruction of the traditional medical system, and the rapid popularization of modern medicines has brought new challenges and opportunities to natural medicine, such as traditional Chinese medicine (TCM). In this paper, the current problems in TCM promotion were analyzed, and the research focus and transformation trends of the future TCM were discussed from the perspective of the history of drug research. (1) The predicament of TCM promotion. First, in terms of drug research and development, TCM is a reverse verification from experience to theory, while modern medicine is a linear process of translating basic research into clinical application. Second, there is no direct connection among TCM registration, TCM supervision and mainstream international standards constructed by modern medicine. Third, TCM needs to face the scrutiny and judgment of Western thinking in global promotion due to the huge difference between Eastern and Western cultures. (2) Implications for modern pharmaceutical research and development. Traditional medicines have strong humanity, region and inheritance. Modern medicines have developed rapidly since the 19th century, and modern medicines (including chemicals and biological products) have gradually replaced traditional medicines and become the mainstream of the market. For example, aspirin, metformin and artemisinin are classic cases of traditional medicine being transformed into modern medicine. Traditional medicines are not confined to the characteristics of “crude, complex, vague”, and the discovery of modern medicines also shows the enlightenment or participation of traditional medicines. (3) Value regression for drug research and development. Humans form different races due to sociocultural symbols such as blood, regions, cultures, beliefs and customs. However, drugs have irreplaceable and important roles in health. Therefore, a set of extremely strict technical standards around effectiveness, safety and quality has been gradually formed in the modern new drug research and development, and the “cultural value” of drugs is required to transform into the “efficacy effect”. (4) The modernization of TCM. There are two main methods to develop new TCM medicines: Following the research and development mode of modern medicines, or following the research and development mode of traditional medicines. For the first method, within the framework of modern medicines, it is difficult for traditional medicines such as TCM to meet the research and development standards of modern medicines comprehensively. However, the existing difficulties do not mean denying the correctness of scientific standards. When the research and development mode of the traditional medicines is used, the efficacy of the new drug should withstand comprehensive reproducibility and examination, and understanding the material basis and exploring the mechanism of drug action are still the keys to clarifying and explaining TCM. In addition, the uniformity and stability of TCM quality are important components of drug management.

GlobalMatch: Registration of forest terrestrial point clouds by global matching of relative stem positions

Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registration require the extraction of individual tree attributes, and they have an efficiency bottleneck when dealing with point clouds of real-world forests with dense trees. We propose an automatic, robust, and efficient method for the registration of forest point clouds. Our approach first locates tree stems from raw point clouds and then matches the stems based on their relative spatial relationship to determine the registration transformation. The algorithm requires no extra individual tree attributes and has quadratic complexity to the number of trees in the environment, allowing it to align point clouds of large forest environments. Extensive experiments on forest terrestrial point clouds have revealed that our method inherits the effectiveness and robustness of the stem-based registration strategy while exceedingly increasing its efficiency. Besides, we introduce a new benchmark dataset that complements the very few existing open datasets for the development and evaluation of registration methods for forest point clouds. The source code of our method and the dataset are available at https://github.com/zexinyang/GlobalMatch.

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data

Purpose7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required.MethodsTo combine TOF and PC-MRI data, we developed a hybrid registration approach. Vessels and their centerlines are segmented from the TOF data. The centerline is fit to the intensity ridges of the lower resolved PC-MRI data, which provides temporal information. We used a metric that utilizes a scaled sum of weighted intensities and gradients on the normal plane. The registration is then guided by decoupled local affine transformations. It is applied hierarchically following the branching order of the vessel tree.ResultsA landmark validation over Monte Carlo simulations yielded an average mean squared error of 184.73 mm and an average Hausdorff distance of 15.20 mm. The hierarchical traversal that transforms child vessels with their parents registers even small vessels not detectable in the PC-MRI.ConclusionThe presented work combines high-resolution tomographic information from 7T TOF-MRI and measured flow data from 4D 7T PC-MRI scan for the arteries of the brain. This enables usage of patient-specific flow parameters for realistic simulations, thus supporting research in areas such as cerebral small vessel disease. Automatization and free deformations can help address the limiting error measures in the future.

Reconciling Registration Policies for Unmanned Aircraft with Unmanned Aircraft Ownership Characteristics

Registration of unmanned aircraft is a common policy around the world and forms part of the International Civil Aviation Organisation’s model regulations for unmanned aircraft. This study conducts a review of the various registration policies that have been implemented amongst advanced economies to find commonalities and differences. New Zealand is then used as a case study. The country does not currently have registration of unmanned aircraft; however, their Ministry of Transport has put forward the idea of implementing a registration scheme. As part of this case study, the ownership characteristics of 919 New Zealand unmanned aircraft users were collected using an online survey. The results highlight that personally owned aircraft tend to only be used by their owner, with the number of users being lower than the number of aircraft. For organisationally owned aircraft, there are multiple users per aircraft; however, these users tend to only be employees of the organisation. These findings suggest that for New Zealand, the best way to implement a registration scheme would be to register users and organisations rather than individual aircraft. While specific to New Zealand, these findings also prompt the need for future research worldwide to see whether registration schemes reconcile with ownership data.

Fast Protein and Metabolites (Nucleotides and Nucleosides) Liquid Chromatography Technique and Chemical Sensor for the Assessment of Fish and Meat Freshness

Fast protein and metabolite liquid chromatography (FPLMC) was introduced years ago to enable the easy separation of high-molecular compounds such as proteins from small molecules and the identification of the low-molecular substances. In this paper, the method is applied for the rapid evaluation of freshness and monitoring the aging of animal meat and fish. A novel chromatographic sensor was developed with a deep UV LED-based photometric detection unit (255–265 nm), an original flow cuvette and registration scheme; the processing of a chromatogram with the sensor takes approximately 15 min. Strict isochronism between the elution of ATP metabolites, mainly hypoxanthine (Hx) and inosine monophosphate (IMP), and the time of maturation of meat or fish, was discovered. A new freshness index H* = [Hx]/[IMP] was introduced, which is proportional to the instrumental delay time in the FPMLC chromatograms: the H* index &lt; 0.5 indicates the presence of inosine monophosphate (IMP) and the high quality of the meat or fish. Reasonably strong correlations were revealed between data obtained by FPMLC and total volatile basic nitrogen TVB-N (for fish) or volatile fatty acids VFA (for meat) content. Moreover, putative nucleotide salvage and an increase in the concentration of IMP were observed in fish after heat treatment using the FPMLC sensor and NMR technique.

An Image-Aided Sparse Point Cloud Registration Strategy for Managing Stockpiles in Dome Storage Facilities

Stockpile volume estimation plays a critical role in several industrial/commercial bulk material management applications. LiDAR systems are commonly used for this task. Thanks to Global Navigation Satellite System (GNSS) signal availability in outdoor environments, Uncrewed Aerial Vehicles (UAV) equipped with LiDAR are frequently adopted for the derivation of dense point clouds, which can be used for stockpile volume estimation. For indoor facilities, static LiDAR scanners are usually used for the acquisition of point clouds from multiple locations. Acquired point clouds are then registered to a common reference frame. Registration of such point clouds can be established through the deployment of registration targets, which is not practical for scalable implementation. For scans in facilities bounded by planar walls/roofs, features can be automatically extracted/matched and used for the registration process. However, monitoring stockpiles stored in dome facilities remains to be a challenging task. This study introduces an image-aided fine registration strategy of acquired sparse point clouds in dome facilities, where roof and roof stringers are extracted, matched, and modeled as quadratic surfaces and curves. These features are then used in a Least Squares Adjustment (LSA) procedure to derive well-aligned LiDAR point clouds. Planar features, if available, can also be used in the registration process. Registered point clouds can then be used for accurate volume estimation of stockpiles. The proposed approach is evaluated using datasets acquired by a recently developed camera-assisted LiDAR mapping platform—Stockpile Monitoring and Reporting Technology (SMART). Experimental results from three datasets indicate the capability of the proposed approach in producing well-aligned point clouds acquired inside dome facilities, with a feature fitting error in the 0.03–0.08 m range.

Flexible High-Resolution Continuous 3-D Scanning for Large-Scale Industrial Components

Generating a large-scale high-density point cloud is crucial for three-dimensional shape measurements. Both area-scan and line-scan camera systems have advantages and limitations for large-scale structured light measurement. Consequently, we propose a method that achieves complementary advantages of both the camera systems through flexible continuous scanning with high resolution and frequency. For point cloud generation, efficient scanning in continuous motion with high resolution, large field of view, and high frequency is realized using line-scan cameras. A flexible registration strategy without mechanical positioning is proposed for point cloud registration after referring to the registration methods in area-scan camera systems. To realize the above functions, system structure and calibration are designed. Techniques for point cloud generation with high dynamic performance and interference resistance are introduced. The point cloud registration strategy includes methods for high-frequency pose acquisition and processing. Moreover, an optimization method is designed for the point cloud after registration, and the texture is attached. The proposed system has excellent prospects for large-scale industrial component measurements.

A survey of hyperspectral image super-resolution method

A survey of hyperspectral image super-resolution method

Strategy to obtain an accurate occlusal registration using intraoral scanners

Obtaining precise occlusal registration is one of the main issues in prosthodontics. The adequate trimming strategy of bite material and removing the bubbles in the cast are important and time-consuming procedures to acquire the correct occlusal registration in the conventional pathway. In the era of digital dentistry, the use of intraoral scanners has been widely spread out, and the occlusal registration step is automatically performed through the software algorithm. However, the accuracy of occlusal registration using intraoral scanners is differ-ent depending on the digital trimming strategy. Clinicians are required to understand the theoretical knowledge for occlusal registration and data trimming strategy.

Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy.

Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images. We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the non-registered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance. 19 left submandibular glands, 18 right submandibular glands, 20left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30s to execute per case, with the exception of Elastix 23 which took ∼458s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p<0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods. The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy.

Joint sensor registration and multi-object tracking with PHD filter in distributed multi-sensor networks

This paper addresses the joint sensor registration and multi-object tracking in distributed sensor networks wherein the accurate positions of sensors are unknown. Each local sensor runs the probability hypothesis density filter (PHD) in its local coordinate system, having no knowledge about the relative positions of other sensors in the network. The generalized covariance intersection (GCI) fusion rule is employed to fuse the local densities. To tackle the sensor registration problem, a cost function expressing the information discrepancy between local densities is constructed based on the GCI divergence, then the relative positions of sensors are estimated by minimizing such a cost function. As the constructed cost function leads to a high-dimension optimization problem, for computational efficiency, a pairwise sensor registration strategy is proposed which is proved equally sound with the high-dimension registration method but more computationally efficient. Lastly, we demonstrate that the formulated registration method involves a non-convex problem. To solve such a non-convex problem, a two-step optimization method is proposed which can transform the non-convex problem into a convex one. The efficacy of the proposed method is demonstrated in a challenging tracking scenario via numerical experiments.

Multi-View High Precise 3D Human Body Reconstruction Method for Virtual Fitting

Online shopping has experienced rapid development recently. However, compared to offline shopping, the return rate and complaint rate of online shopping are much higher, especially for online clothes shopping. In order to solve this problem, virtual fitting technology arises at the right moment, and 3D human modeling is a crucial part of virtual fitting technology. The reconstruction of the parametric 3D human models often faces challenges as long fitting time, low accuracy and fuzzy depth information. Although the reconstruction of nonparametric 3D human model has improved accuracy and detail to some extent, such models typically lack flexibility and controllability. Therefore, this paper reconstructs a high-precision parametric 3D human model by proposing a multi-view iterative registration strategy based on pose prior estimation, which is integrated with a nonparametric 3D human model based on implicit functions. The resulting model retains not only high-precision and detail but also high flexibility and controllability, which has achieved a good effect on the application of 3D virtual fitting. The proposed method is tested on the open-source 3D human body dataset multi-garment network (MGN). The Chamfer distance of the SMPL mannequin reconstructed from multiple views can reach 2.18[Formula: see text]cm and the per-vertical-error can reach 1.63[Formula: see text]cm.

An efficient producer mobility management technique for real-time communication in NDN-based Remote Health Monitoring systems

The remote Health Monitoring (RHM) system is a remote healthcare platform that provides real-time monitoring services and brings convenience and safety for doctors as well as patients. Named Data Network (NDN) is a future network architecture, which is a kind of Information-Centric Network. NDN’s advantages in data security and performance can make the health system more reliable and efficient. However, in the existing NDN network architecture, there exist some producer mobility issues that can impede the proceeding towards real-time and seamless communication. To solve the problem, a Neighborhood Registration Scheme (NRS) is proposed here. When the mobile producer moves from one AP to another, it has not registered into the new AP and thus cannot receive the Interest Packet (IntP) from the consumer in time, which can cause data losses. Meanwhile, the updating process in the NDN map also takes a considerable time, which can also cause delay, referred to as updating delay. This is due to some consumers having got the new location of the mobile producer while others have not. The scheme proposes in this paper is to register the mobile producer not only into the nearest Access Point (AP) but also multiple neighbor APs. In this way, the delay caused by AP switching and package loss rate can be effectively reduced, which allows mobile producers to move across the cells with low latency and Packet Loss (PL) rate. NRS is a comprehensive routing algorithm that designs the modified packet transmission strategy. Compare to the Anchor-less strategy, the proposed NRS decreases about 80% PL rate and saves 16% delay time, whereas, NRS reduces more than 90% PL rate and saves 40% delay time than Hop-count strategy in high-speed producer mobility scenarios. These improvements are necessary for RHM because low delay and PL are the most important requirements of the RHM that can strengthen the real-time monitoring for patients, information interaction between doctors and patients, and safety assurance. By setting the proper parameters, NRS can promise to have no PL and acceptable network cost.

Registration of photoacoustic tomography vascular images: Comparison and analysis of automatic registration approaches

Image registration is crucial in the clinical application of photoacoustic tomography (PAT) for vascular growth monitoring. Aiming to find an optimized registration scheme for PAT vascular images acquired at different times and with varying imaging conditions, we compared and analyzed different commonly used intensity-based and feature-based automatic registration schemes. To further improve the registration performance, we proposed a new scheme that combines phase correlation with these commonly used intensity-based registration methods and compared their performances. The objective evaluation measures: peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), root mean square error (RMSE), and quantitative visual perception (jump percentage P), as well as subjective evaluation using mean opinion score (MOS), were combined to evaluate the registration performance. Results show that the feature-based approaches in this study were not suitable for PAT image registration. And by adding phase correlation as rough registration, the overall registration performance was improved significantly. Among these methods, the proposed scheme of phase correlation combined with mean square error (MSE) similarity measure and regular-step-gradient-descent optimizer provides the best visual effect, accuracy, and efficiency in PAT vascular image registration.

Accurate depth image generation via overfit training of point cloud registration using local frame sets

Accurate three-dimensional perception is a fundamental task in various computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains insufficient owing to the inherent noise from a single-view environment. Several recent studies have focused on the single-view depth enhancement based on modern deep-learning technologies. The approaches typically train networks using high-quality benchmark depth datasets, which indicates that the quality of the supervised depth dataset is a fundamental factor for the depth enhancement system; however, such high-quality depth datasets are difficult to obtain. In this study, we developed a novel method for high-quality depth image generation based on an RGB-D stream dataset. First, we defined consecutive depth frames in a local spatial region as a local frame set. Then, the depth frames were aligned to a certain frame in the local frame set using an unsupervised point cloud registration scheme. The registration parameters were trained based on an overfit-training scheme, which was primarily used to generate an enhanced depth image for each frame set. The final depth dataset was constructed using multiple local frame sets, and each local frame set was trained independently. The primary advantage of this study is that a high-quality depth dataset can be constructed under various scanning environments using only the RGB-D stream dataset. Moreover, our proposed method can be used as a new benchmark depth dataset for accurate performance evaluations. We evaluated our dataset on previously benchmarked depth datasets and demonstrated that our method is superior to state-of-the-art depth enhancement frameworks.

Reflections on supervision strategies of new Tibetan drug registration

Tibetan medicine is an essential part of Chinese medicine and has unique theoretical experience and therapeutic advantages. According to the development principle of inheriting the essence, sticking to the truth, and keeping innovative, the supervision department should give clear and reasonable guidance considering the characteristics of Tibetan medicine, establish a standard system for quality control, clinical verification and evaluation, and accelerate the research and commercialization of new drugs. In view of the needs of drug supply-side reform and the current situation of Tibetan medicine and new pharmaceutical research, we ponder and provide suggestions on the confusion faced by the current supervision of Tibetan drug registration, hoping to contribute to the supervision strategy of Tibetan drug registration and the high-quality development of Tibetan medicine industry.