Initial Alignment Research Articles

Collaborative Tooth Motion Diffusion Model in Digital Orthodontics

Tooth motion generation is an essential task in digital orthodontic treatment for precise and quick dental healthcare, which aims to generate the whole intermediate tooth motion process given the initial pathological and target ideal tooth alignments. Most prior works for multi-agent motion planning problems usually result in complex solutions. Moreover, the occlusal relationship between upper and lower teeth is often overlooked. In this paper, we propose a collaborative tooth motion diffusion model. The critical insight is to remodel the problem as a diffusion process. In this sense, we model the whole tooth motion distribution with a diffusion model and transform the planning problem into a sampling process from this distribution. We design a tooth latent representation to provide accurate conditional guides consisting of two key components: the tooth frame represents the position and posture, and the tooth latent shape code represents the geometric morphology. Subsequently, we present a collaborative diffusion model to learn the multi-tooth motion distribution based on inter-tooth and occlusal constraints, which are implemented by graph structure and new loss functions, respectively. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in the application of orthodontics compared with state-of-the-art methods.

Better in sync: Temporal dynamics explain multisensory word-action-object learning in early development.

We investigated the temporal impact of multisensory settings on children's learning of word-object and action-object associations at 1- and 2-years of age. Specifically, we examined whether the temporal alignment of words and actions influenced the acquisition of novel word-action-object associations. We used a preferential looking and violation of expectation task in which infants and young children were first presented with two distinct word-object and action-object pairings either in a synchronous (overlapping in time) or sequential manner (one after the other). Findings revealed that 2-year-olds recognized both, action-object and word-object associations when they first saw the word-action-object combinations synchronously, but not sequentially, as evidenced by looking behavior. 1-year-olds did not show evidence for recognition for either of the word-object and action-object pairs, regardless of the initial temporal alignment of these cues. To control for individual differences, we explored factors that might influence associative learning based on parental reports of 1- and 2-year-olds development, however, developmental measures did not explain word-action-object associative learning in either group. We discuss that while young children may benefit from the temporal alignment of multisensory cues as it enables them to actively engage with the multisensory content in real-time, infants may have been overwhelmed by the complexity of this input.

Dynamic Path Planning Based on 3D Cloud Recognition for an Assistive Bathing Robot

Assistive bathing robots have become a popular point due to their metrics, such as a humanoid working approach in the solution of elder care. However, the abilities of dynamic recognition and path planning are the key to obtain the advantages. This paper proposes a novel approach to recognize and track the dynamical human back, and path planning on it via a 3D point cloud. Firstly, the human back geometric features are recognized through coarse-to-fine alignment. The Intrinsic Shape Signature (ISS) algorithm combined with the Fast Point Feature Histogram (FPFH) and the Sample Consensus Initial Alignment (SAC-IA) algorithm are adopted to complete the coarse alignment, and the Iterative Closest Point (ICP) algorithm is applied to the fine alignment to improve the accuracy of recognition. Then, the dynamic transformation matrix between the contiguous recognized back is deduced based on spatial motion between two adjacent recognized back point clouds. The path can be planned on the tracked human back. Finally, a set of testing experiments are conducted to verify the proposed algorithm. The results show that the running time is reduced by 66.18% and 96.29% compared with the other two common algorithms, respectively.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

Robust Adaptive SINS/DVL Initial Alignment Method Based on Variational Bayesian Information Filter

Robust Adaptive SINS/DVL Initial Alignment Method Based on Variational Bayesian Information Filter

Ultralow‐Voltage Frequency Tunning of Liquid Crystal‐Based GHz Antenna via Nanoscratching Method

AbstractLiquid crystal (LC)‐based radio‐frequency (RF) tunable antennas garner attention because of their compact size and minimal heat generation. Exploiting the LC's property to alter the RF permittivity depending on its alignment, voltage‐induced orientational switching enables frequency modulation of the radiated wave. To ensure dependable antenna performance, precise control of the initial LC alignment is crucial, which is conventionally achieved by polyimide (PI) coating followed by the rubbing process (PI‐rubbing). However, this approach demands high temperature (200 °C) and prolonged processing time (2 h), while also necessitating high voltage for LC reorientation due to induced excessive surface polar anchoring strength. Here, a PI‐free‐nanoscratching method creating 1D channels is introduced that induces initial LC alignment in a desired direction. The initial orienting capability of the scratched metal surface is superior to that of the PI‐rubbed surface. Furthermore, the LC‐based antenna employing the nanoscratching method shows a remarkable resonance frequency shift of up to 3 GHz with a low voltage of 2 V. This driving voltage is five times lower than that of antennas using the conventional PI‐rubbing method, which can open a new era for the next generation LC‐antenna.

Initial arch wires used in orthodontic treatment with fixed appliances.

Initial arch wires used in orthodontic treatment with fixed appliances.

Comparison of the efficiency of initial dental alignment with Invisalign® aligners changed every 7 or 14 days in mature adults: Randomized clinical trial.

To compare the anterior crowding correction after sequential use of the first 5 aligners between two aligner exchange protocols (7 and 14 days) in mature adult patients. Thirty-six patients over 35 years who received orthodontic treatment with Invisalign® were randomly allocated to two different aligner replacement sequence protocols: Group 1: exchange every 7 days (n = 18); and Group 2: exchange every 14 days (n = 18). All patients were scanned with iTero Element 5D® (Align Technology) in two treatment times: at pre-treatment (T1) and after using the first five aligners (T2). Little's Irregularity Index (LII) and arch widths were evaluated with OrthoCAD software. Intra and intergroup comparisons were performed with the dependent and independent t-tests. The results were considered significant for P < .05. Thirty-five patients completed the study. Both groups' maxillary and mandibular Irregularity Indexesi were significantly lower at T2. There was no significant difference in inter-canine, inter-premolar, and intermolar distances.G2 (14 days) presented a greater decrease in mandibular irregularity than G1 (7 days). Both exchange protocols (7 and 14 days) effectively correct anterior crowding in the initial phase of orthodontic treatment with aligners in mature adult patients. However, the 14-day exchange protocol provided a greater correction in mandibular anterior crowding in the evaluated period than the 7-day exchange protocol.

SLAM-RAMU: 3D LiDAR-IMU lifelong SLAM with relocalization and autonomous map updating for accurate and reliable navigation

PurposeAutonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.Design/methodology/approachThis research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.FindingsThe performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.Originality/valueCompared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

RNA threading with secondary structure and sequence profile.

RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory. Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection. https://yanglab.qd.sdu.edu.cn/RNAthreader.

Features of the influence of emotional state on the implementation of arbitrary purposeful bimanual movements

The emotional state has a significant impact on the autonomic and somatic nervous systems, which give various manifestations. All external manifestations of emotions are strictly determined and begin to be realized when the cortical areas are activated. The leading role in the realization of the emotional state belongs to the frontal lobes of the cortex and the limbic system. The precentral sulcus is the cortical center of complex purposeful movements. The purpose of this work is to study the influence of the emotional state on the implementation of the motor program of bimanual purposeful voluntary movement. The emotional state was determined using the K scale. Izard. The bimanual motor activity of the hands was measured using the method of supportmetry. As a result of the study, it was found that a negative emotional state leads to a decrease in the reaction rate of the reaction to an error, which may be caused by a change in the initial alignment between the motor cortex and the limbic system.

Ridge split assisted molar protraction

Protraction of a second molar into the gap left by a missing first molar is an option, although it is challenging to maintain root parallelism during mesial migration due to the thick and dense cortical bone. This study was carried out to make an attempt to evaluate whether molar protraction in subjects with missing mandibular first molar and knife edge ridges is possible or not using ridge split procedure (expansion).The sample consisted of 12 subjects with missing mandibular first molar and knife edge ridges. All pre-treatment records were taken, after initial leveling and alignment ridge split procedure was performed and molar protraction was carried out using mini implants. Data was collected at a fixed interval of every six weeks. Statistical analysis using Repeated-measures ANOVA and Paired t test were done for evaluation of the rate of molar protraction and mesial molar tipping 0.12mm per week and 0.9 mm per six weeks was the rate of molar protraction that was accomplished. The mean edentulous space decreased from pre protraction to third visit significantly with a p value of ≤ 0.001.There is mesial tipping of molar post protraction with a P value of 0.148 which is statistically non-significant. On an average duration for molar protraction was 8.4 months.Ridge split made molar protraction possible in subjects with missing mandibular molar and knife edge ridges and enhanced the rate of tooth movement. The absolute anterior dental anchorage for the desired orthodontic movement was achieved by utilisation of mini implants.

Multi-Frame Content-Aware Mapping Network for Standard-Dynamic-Range to High-Dynamic-Range Television Artifact Removal.

Recently, advancements in image sensor technology have paved the way for the proliferation of high-dynamic-range television (HDRTV). Consequently, there has been a surge in demand for the conversion of standard-dynamic-range television (SDRTV) to HDRTV, especially due to the dearth of native HDRTV content. However, since SDRTV often comes with video encoding artifacts, SDRTV to HDRTV conversion often amplifies these encoding artifacts, thereby reducing the visual quality of the output video. To solve this problem, this paper proposes a multi-frame content-aware mapping network (MCMN), aiming to improve the performance of conversion from low-quality SDRTV to high-quality HDRTV. Specifically, we utilize the temporal spatial characteristics of videos to design a content-aware temporal spatial alignment module for the initial alignment of video features. In the feature prior extraction stage, we innovatively propose a hybrid prior extraction module, including cross-temporal priors, local spatial priors, and global spatial prior extraction. Finally, we design a temporal spatial transformation module to generate an improved tone mapping result. From time to space, from local to global, our method makes full use of multi-frame information to perform inverse tone mapping of single-frame images, while it is also able to better repair coding artifacts.

Learning accurate template matching with differentiable coarse-to-fine correspondence refinement

Template matching is a fundamental task in computer vision and has been studied for decades. It plays an essential role in manufacturing industry for estimating the poses of different parts, facilitating downstream tasks such as robotic grasping. Existing methods fail when the template and source images have different modalities, cluttered backgrounds, or weak textures. They also rarely consider geometric transformations via homographies, which commonly exist even for planar industrial parts. To tackle the challenges, we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement. We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image, allowing robust matching. An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers. This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation. Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines, providing good generalization ability and visually plausible results even on unseen real data.

Constructing an Osseointegrated Prosthetic Leg.

Constructing an osseointegrated prosthetic leg is the necessary subsequent phase of care for patients following the surgical implantation of an osseointegrated prosthetic limb anchor. The surgeon implants the bone-anchored transcutaneous implant1,2 and the prosthetist constructs the prosthetic leg, which then attaches to the surgically implanted anchor. An osseointegration surgical procedure is usually considered in patients who are unable to use or are dissatisfied with the use of a socket prosthesis. This present video article describes the techniques and principles involved in constructing a prosthetic leg for transfemoral and transtibial amputees, as well as postoperative patient care. Preoperatively, as part of a multidisciplinary team approach, the prosthetist should assist in patient evaluation to determine suitability for osseointegration surgery. Postoperatively, when approved by the surgeon, the first step is to perform an implant inspection and to take patient measurements. A temporary loading implant is provided to allow the patient to start loading the limb. When the patient is approved for full-length leg to begin full weight-bearing, the implant and prosthetic quality are evaluated, including torque, implant position, bench alignment, static alignment in the standing position, and initial dynamic alignment. This surgical procedure also requires long-term, continued patient care and prosthetic maintenance. For patients who are dissatisfied with the use of a socket prosthesis, adjustments can often be made to improve the comfort, fit, and performance of the prosthesis. Non-osseointegration surgical options include bone lengthening and/or soft-tissue contouring. Osseointegration can be provided for amputees who are expressing dissatisfaction with their socket prosthesis, and typically provides superior mobility and quality of life compared with nonoperative and other operative options3,4. Specific differences between the appropriate design and construction of osseointegrated prostheses versus socket prostheses include component selection, component fit, patient-prosthesis static and dynamic alignment, tolerances and accommodations, and also the expected long-term changes in patient joint mobility and behavior. Providing an osseointegrated prosthesis according to the principles appropriate for socket prostheses may often leave an osseointegrated patient improperly aligned and provoke maladaptive accommodations, hindering performance and potentially putting patients at unnecessary risk for injury. Review articles describing the clinical outcomes of osseointegration consistently suggest that patients with osseointegrated prostheses have improved prosthesis wear time, mobility, and quality of life compared with patients with socket prostheses. Importantly, studies have shown that osseointegrated prostheses can be utilized in patients with short residual limbs that preclude the use of a socket prosthesis, allowing them to regain or retain function of the joint proximal to the short residuum5,6. Osseoperception improves patient confidence during mobility7. Because there is an open skin portal, low-grade soft-tissue infection can occur, which is usually treated with a short course of oral antibiotics. Much less often, soft-tissue debridement or implant removal may be needed to treat infection8. Periprosthetic fractures can nearly always be treated with familiar fracture fixation techniques and implant retention9,10. Falls can lead to periprosthetic fractures.Malalignment can lead to unnecessary pathologic joint forces, soft-tissue contractures, and an accommodative gait.Inadequately sophisticated components can leave patients at a performance deficit.Wearing the prosthetic leg while sleeping may lead to rotational forces exerted on the limb, which may cause prolonged tension on the soft tissue. QTFA = Questionnaire for Persons with a Transfemoral AmputationLD-SRS = Limb Deformity Modified Scoliosis Research SocietyPROMIS = Patient-Reported Outcomes Measurement Information SystemEQ-5D = EuroQol 5 Dimensions.

Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Invisible Orthodontics: Ceramic appliance vs Lingual appliance -A patient perception survey

The aim of the study was to analyse variations in patients’ perception on oral health, aesthetic acceptance, pain perceptions and comfort levels in patients who has undergone/undergoing orthodontic treatment by means of two types of invisible orthodontic appliances: fixed lingual metal brackets and fixed buccal aesthetic/ceramic brackets. A comparative survey to assess the patient perception after their initial levelling alignment phase was created via Google form and sent to 25 ceramic labially treated and 25 lingually treated patient. Comfort level, pain perception, speech impediment ,quality of life in patients was significantly better in Ceramic brackets as compared to lingual (p=0.001),Aesthetic perception was significantly higher impact on ceramic (p=0.001) as 72% reported no impact on aesthetics with lingual brackets as compared to 4% in the ceramic brackets. Oral hygiene maintenance: The difference on impact of Oral hygiene maintenance was statistically non- significant between ceramic brackets and lingual brackets (p=0.238).Difficulty while having food was statistically non- significantly between ceramic brackets and lingual brackets.(p=0.448). Patients prefer lingual treatment cause of their esthetic nature and their expectations are higher with lingual treatment which itself is not favorable from a clinicians perspective and it is worth noting that speech disturbances may lead to greater social embarrassment than more or less invisible ceramic brackets.

Vehicle integrated navigation IMU mounting angles estimation method based on nonlinear optimization

A vehicle integrated navigation system based on Global Navigation Satellite System (GNSS) aided Inertial Navigation System (INS) enhances its navigation accuracy by utilizing non-holonomic constraints and odometer velocity. However, the navigation accuracy of the system is compromised due to the challenges in the estimation of the inertial measurement unit (IMU) mounting angles relative to the host vehicle. This paper presents a method for estimating the IMU mounting angles of a vehicle that integrated navigation system through a nonlinear optimization. The proposed method firstly achieves the initial alignment of the vehicle integrated navigation system through a GNSS/INS loosely coupled algorithm. Then, the vehicle navigation algorithm is switched to the INS algorithm after collecting 100 satellite-fixed solutions. Finally, a nonlinear optimization objective function based on the difference between the satellite-fixed solutions and the INS integration results is constructed to solve for the IMU mounting angles. Experiments were conducted by using a Murata SCHA634 IMU on a land vehicle to validate the proposed method. This method has a reduction of approximately 30% in the three-dimensional positioning accuracy within error compared to that without mounting angles compensation, and a reduction of approximately 15% when compared with the traditional approach of compensating for IMU mounting angles using the error state Kalman filter method.

End-to-end validation of fiducial tracking accuracy in robotic radiosurgery using MRI-only simulation imaging.

Image-guided radiation-therapy (IGRT)-based robotic radiosurgery using magnetic resonance imaging (MRI)-only simulation could allow for improved target definition with highly conformal radiotherapy treatments. Fiducial marker (FM)-based alignment is used with robotic radiosurgery treatments of sites such as the prostate because it aids in accurate target localization. Synthetic CT (sCT) images are generated in the MRI-only workflow but FMs used for IGRT appear as signal voids in MRIs and do not appear in MR-generated sCTs, hindering the ability to use sCTs for fiducial-based IGRT. In this study we evaluate the fiducial tracking accuracy for a novel artificial fiducial insertion method in sCT images that allows for fiducial marker tracking in robotic radiosurgery, using MRI-only simulation imaging (MRI-only workflow). Artificial fiducial markers were inserted into sCT images at the site of the real marker implantation as visible in MRI. Two phantoms were used in this study. A custom anthropomorphic pelvis phantom was designed to validate the tracking accuracy for a variety of artificial fiducials in an MRI-only workflow. A head phantom containing a hidden target and orthogonal film pair inserts was used to perform end-to-end tests of artificial fiducial configurations inserted in sCT images. The setup and end-to-end targeting accuracy of the MRI-only workflow were compared to the computed tomography (CT)-based standard. Each phantom had six FMs implanted with a minimum spacing of 2cm. For each phantom a bulk-density sCT was generated, and artificial FMs were inserted at the implantation location. Several methods of FM insertion were tested including: (1) replacing HU with a fixed value (10000HU) (voxel-burned); (2) using a representative fiducial image derived from a linear combination of fiducial templates (composite-fiducial); (3) computationally simulating FM signal voids using a digital phantom containing FMs and inserting the corresponding signal void into sCT images (simulated-fiducial). All tests were performed on a CyberKnife system (Accuray, Sunnyvale, CA). Treatment plans and digital-reconstructed-radiographs were generated from the original CT and sCTs with embedded fiducials and used to align the phantom on the treatment couch. Differences in the initial phantom alignment (3D translations/rotations) and tracking parameters between CT-based plans and sCT-based plans were analyzed. End-to-end plans for both scenarios were generated and analyzed following our clinical protocol. For all plans, the fiducial tracking algorithm was able to identify the fiducial locations. The mean FM-extraction uncertainty for the composite and simulated FMs was below 48% for fiducials in both the anthropomorphic pelvis and end-to-end phantoms, which is below the 70% treatment uncertainty threshold. The total targeting error was within tolerance (<0.95mm) for end-to-end tests of sCT images with the composite and head-on simulated FMs (0.26, 0.44, and 0.35mm for the composite fiducial in sCT, head-on simulated fiducial in sCT, and fiducials in original CT, respectively. MRI-only simulation for robotic radiosurgery could potentially improve treatment accuracy and reduce planning margins. Our study has shown that using a composite-derived or simulated FM in conjunction with sCT images, MRI-only workflow can provide clinically acceptable setup accuracy in line with CT-based standards for FM-based robotic radiosurgery.

CONTINUOUS BIM ALIGNMENT FOR MIXED REALITY VISUALISATION

Abstract. Several methods exist that can be used to perform initial alignment of Building information models (BIMs) to the real building for Mixed Reality (MR) applications, such as marker-based or markerless visual methods, but this alignment is susceptible to drift over time. The existing model-based methods that can be used to maintain this alignment have multiple limitations, such as the use of iterative processes and poor performance in environments with either too many or not enough lines. To address these issues, we propose an end-to-end trainable Convolutional Neural Network (CNN) that takes a real and synthetic BIM image pair as input to regress the 6 DoF relative camera pose difference between them directly. By correcting the relative pose error we are able to considerably improve the alignment of the BIM to the real building. Furthermore, the results of our experiments demonstrate good performance in a challenging environment and high resilience to domain shift between synthetic and real images. A high localisation accuracy of approximately 7.0 cm and 0.9° is achieved which indicates the method can be used to reduce the camera tracking drift for MR applications.