Rigid Point Research Articles

Phantom-Enhanced High Mechanical Accuracy for Frame-Based Deep Brain Stimulation.

Deep brain stimulation (DBS) is a neurosurgical procedure that depends on high-accuracy targeting of structures to implant electrodes within the brain. The positioning of these electrodes in the brain determines the long-term efficacy of treating diseases such as Parkinson's disease, essential tremor, or dystonia.Misplaced electrodes in DBS can lead to poor efficacy and stimulation-induced side effects. Widespread targeting errors and variability have been published throughout the literature. As such,improvement in targeting accuracy is needed to enhance the quality of the procedures. A stereotactic phantom was utilized to test and adjust targeting before the surgical placement in the brain for 91 sequential electrodes.The tip of the microelectrode, the first rigid point in time, was measured and compared to the planned target.The technique utilized a to-target cannulawith an XY stage that allowed x-axis and y-axis adjustments and correction for inaccuracies relative to the phantom.A calculation was developed to convert anatomical angles (sagittal and coronal) provided by commercial planning stations to spherical angles to calculate points along a trajectory.Errorcalculations included each dimensional axis, Euclidean error, and radial error. Bends in the cannula and microelectrode were observed and corrected by referencing the phantom. All 91 first-pass tracks traversed the intended target, and three electrodes required a second mapping track beyond the first penetration due to neurophysiological and intraoperative testing. The results showed overall ultra-high (0-0.5 mm) to high (>0.5-1 mm) accuracy, an average Euclidean error of 0.66±0.30 mm, and a radial error of 0.45±0.28 mm with dimensional errors of less than 0.5 mm per axis. The utilization of a stereotactic phantom is an important tool to enhance the stereotactic targeting before insertion into the brain. This phantom technique yielded ultra-high to high accuracy in error calculations.Future methods and studies shouldfocus on error minimization to enhance these DBS mechanical accuracy and correlations with clinical outcomes.

Coherent point drift with Skewed Distribution for accurate point cloud registration

Point cloud registration methods based on Gaussian Mixture Models (GMMs) exhibit high robustness. However, GMM cannot precisely depict point clouds, because the Gaussian distribution is spatially symmetric and local surfaces of point clouds are typically non-symmetric. In this paper, we propose a novel method for rigid point cloud registration, termed coherent point drift with Skewed Distribution (Skewed CPD). Our method employs an asymmetric distribution constructed from the local surface normals and curvature radii. Compared to the Gaussian distribution, this skewed distribution provides a more accurate spatial description of points on local surfaces. Additionally, we integrate an adaptive multiplier to the covariance, which reallocates the weight of the covariance for different components in the probabilistic mixture model. We employ the EM algorithm to address this maximum likelihood estimation (MLE) issue and leverage GPU acceleration. In the M-step, we adopt an unconstrained optimization technique rooted in a Lie group and Lie algebra to attain the optimal transformation. Experimental results indicate that our method outperforms state-of-the-art methods in both accuracy and robustness. Remarkably, even without loop closure detection, the cumulative error of our approach remains minimal.

Rigid point cloud registration based on correspondence cloud for image-to-patient registration in image-guided surgery.

Image-to-patient registration aligns preoperative images to intra-operative anatomical structures and it is a critical step in image-guided surgery (IGS). The accuracy and speed of this step significantly influence the performance of IGS systems. Rigid registration based on paired points has been widely used in IGS, but studies have shown its limitations in terms of cost, accuracy, and registration time. Therefore, rigid registration of point clouds representing the human anatomical surfaces has become an alternative way for image-to-patient registration in the IGSsystems. We propose a novel correspondence-based rigid point cloud registration method that can achieve global registration without the need for pose initialization. The proposed method is less sensitive to outliers compared to the widely used RANSAC-based registration methods and it achieves high accuracy at a high speed, which is particularly suitable for the image-to-patient registration inIGS. We use the rotation axis and angle to represent the rigid spatial transformation between two coordinate systems. Given a set of correspondences between two point clouds in two coordinate systems, we first construct a 3D correspondence cloud (CC) from the inlier correspondences and prove that the CC distributes on a plane, whose normal is the rotation axis between the two point clouds. Thus, the rotation axis can be estimated by fitting the CP. Then, we further show that when projecting the normals of a pair of corresponding points onto the CP, the angle between the projected normal pairs is equal to the rotation angle. Therefore, the rotation angle can be estimated from the angle histogram. Besides, this two-stage estimation also produces a high-quality correspondence subset with high inlier rate. With the estimated rotation axis, rotation angle, and the correspondence subset, the spatial transformation can be computed directly, or be estimated using RANSAC in a fast and robust way within only 100iterations. To validate the performance of the proposed registration method, we conducted experiments on the CT-Skull dataset. We first conducted a simulation experiment by controlling the initial inlier rate of the correspondence set, and the results showed that the proposed method can effectively obtain a correspondence subset with much higher inlier rate. We then compared our method with traditional approaches such as ICP, Go-ICP, and RANSAC, as well as recently proposed methods like TEASER, SC2-PCR, and MAC. Our method outperformed all traditional methods in terms of registration accuracy and speed. While achieving a registration accuracy comparable to the recently proposed methods, our method demonstrated superior speed, being almost three times faster thanTEASER. Experiments on the CT-Skull dataset demonstrate that the proposed method can effectively obtain a high-quality correspondence subset with high inlier rate, and a tiny RANSAC with 100 iterations is sufficient to estimate the optimal transformation for point cloud registration. Our method achieves higher registration accuracy and faster speed than existing widely used methods, demonstrating great potential for the image-to-patient registration, where a rigid spatial transformation is needed to align preoperative images to intra-operative patientanatomy.

Recurrent multi-view collaborative registration network for 3D reconstruction and optical measurement of blade profiles

Aligning multiple viewpoints to reconstruct a complete profile is crucial for the three-dimensional optical measurement of blade profiles. As foundational solutions, rigid pairwise point cloud registration methods often struggle to produce satisfactory results due to significant cumulative errors stemming from pairwise registration errors, while current multi-view registration methods also face challenges when their prerequisites conflict with the practical measurement scenarios of blade profiles. To address these issues, this study proposes a learning framework to recover a transformation vector between the coordinate frames of both the optical sensor and the rotational axis in our developed optical measurement system based on extracted adaptive feature embeddings from the initial coordinate representation of viewpoints, which simultaneously aligns all viewpoints into a complete profile without relying on any prerequisites. Furthermore, an elaborate recurrent updating strategy is incorporated into the framework that enables incremental refinement of the transformation vector, thus aiding the framework in meeting the high-precision measurement requirements at the 0.01 mm level. Experimental results from both theoretical and real-world data, conducted on three representative blades and compared against nine state-of-the-art algorithms, consistently demonstrate the superiority of the proposed method in terms of accuracy, time efficiency, and robustness. The source code is available at https://github.com/Clarkxielf/Recurrent-Multi-View-Collaborative-Registration-Network.

Toward automatic robotic massage based on interactive trajectory planning and control

Autonomous robotic massage holds the potential to alleviate the workload of nurses and improve the quality of healthcare. However, the complexity of the task and the dynamic of the environment present significant challenges for robotic massage. This paper presents a vision-based robotic massage (VBRM) framework that facilitates autonomous robot massaging of the human body while ensuring safe operation in a dynamic environment. The VBRM framework allows the operator to define the massage trajectory by drawing a 2D curve on an RGB image. An interactive trajectory planning method is developed to calculate a 3D massage trajectory from the 2D trajectory. This method accounts for potential movements of the human body and updates the planned trajectory using rigid point cloud registration. Additionally, a hybrid motion/force controller is employed to regulate the motion of the robot’s end-effector, considering the possibility of excessive contact force. The proposed framework enables the operator to adjust the massage trajectory and speed according to their requirements. Real-world experiments are conducted to evaluate the efficacy of the proposed approach. The results demonstrate that the framework enables successful planning and execution of the massage task in a dynamic environment. Furthermore, the operator has the flexibility to set the massage trajectory, speed, and contact force arbitrarily, thereby enhancing human–machine interaction.

A review of rigid point cloud registration based on deep learning.

With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.

Generalized 3-D Rigid Point Set Registration With Bidirectional Hybrid Mixture Models

Generalized 3-D Rigid Point Set Registration With Bidirectional Hybrid Mixture Models

Evaluation of Red Cell Deformability in Sickle Cell Disease Patients, Sickle Cell Carriers, and Healthy Controls Using Lorrca-Derived Parameters

BACKGROUND Red blood cell (RBC) deformability plays a critical role in RBC elongation and viscosity, and when reduced, perfusion of peripheral tissues and oxygen delivery are impaired. Intracellular ATP maintains water and ion homeostasis in RBCs; reduced ATP leads to water and ion loss, dehydration and loss of RBC elasticity. RBC dehydration increases mean corpuscular hemoglobin concentration (MCHC) and also affects distribution of RBC width (RDW). Patients with sickle cell disease (SCD) have reduced RBC ATP, and markedly reduced RBC deformability when compared to healthy individuals. Here, we employed Laser-Optical Rotational Red Cell Analyzer (LORRCA, RR Mechatronics) to evaluate deformability in RBCs from patients with SCD (HbSS) compared to RBCs from sickle cell carriers (HbAS) and ethnic-matched healthy controls (HbAA). We further analyzed how these LORRCA-derived parameters correlate with ATP, MCHC, and RDW. METHODS We enrolled adult subjects (age ≥ 18 years) of African-descent, and not recently transfused (within 8 weeks) under protocol NCT03685721 approved by the NHLBI Institutional Review Board. Fresh whole blood in EDTA was processed on the same day of collection. Cells were counted and diluted in polyvinylpyrrolidone (PVP) solution before undergoing LORRCA assays including deformability, osmoscan, and oxygenscan (HbSS subjects). The deformability assay uses the elongation Index (EI) to measure the cells' ability to undergo deformation with increasing shear stress at 0.95, 3, and 30 Pascals (Pa). The osmoscan assay measures the deformability under continuous osmotic changes (0-600mOsm/kg) and has several parameters: O min (RBC fragility); El max (maximum deformability and membrane flexibility); O max (osmolality where EI max is achieved) and O hyper (intracellular viscosity and maximum deformability in the hypertonic region). The oxygenscan assay measures the oxygen pressure at the time when RBCs start to become rigid (point of sickling). Whole blood levels of ATP were measured using LC-MS/MS with LLOQ at 50.0 μg/mL and converted to intracellular concentrations by dividing by the hematocrit (as a fraction). Descriptive statistics and correlation (Spearman) of ATP, RDW, and MCHC with each LORRCA parameter were performed using R (v4.2.3) and Prism(v9). RESULTS We studied 156 subjects which comprised of 63 HbSS (33 males), 61 HbAS (23 males) and 32 HbAA (8 males) genotypes. The average age in years (range) in HbSS was 32 (18-58); in HbAS, 43 (18-70), and in HbAA, 38 (19-72). As shear stress and osmotic pressure increased, the mean EI and osmolality were significantly (p<0.001, unpaired two-sample t-test) lower in the HbSS cohort compared to HbAS and HbAA (Figure 1). There was no significant difference in the mean EI and osmolality between HbAA and HbAS with increasing shear stresses and osmotic pressures. As shear stress increased, the correlation of deformability with reduced ATP got significantly stronger in the HbSS cohort (r= -0.351 at 0.95 Pa to r=-0.401 at 30 Pa) but the trend was reversed for the HbAS and HbAA cohort (Table 1). Under increasing osmotic pressure, a significant correlation was found between reduced ATP and maximal deformability (EI Max) in HbSS (p=0.008) compared to HbAS (p=0.896) and HbAA (p=0.114). MCHC was significantly correlated with RBC hydration state (O Hyper) in all 3 genotypic groups (HbSS, HbAS and HbAA). Similarly, RDW correlated with maximal deformability (EI Max) under increasing osmotic pressure in all 3 cohorts. In the HbSS subjects, there was a significant positive correlation of ATP and RDW with point of sickling (oxygenscan); MCHC was negatively correlated but was not significant. CONCLUSION RBC deformability was reduced in HbSS patients compared to HbAS and HbAA under increasing shear stress and osmotic pressure. ATP levels showed significant correlation with RBC deformability as measured by LORRCA, particularly in patients with SCD, suggesting increasing ATP levels as an important therapeutic strategy in patients with SCD. In keeping with published data, our analyses showed that RBC volume and size as well as intracellular hemoglobin concentration can affect deformability.

RPP-Net: Rigid Constrained Point Cloud Prediction Network

Forecasting the future environment is an essential and fundamental capability of autonomous driving systems. In contrast to the widely studied video prediction, only a few literatures have explored LiDAR point cloud prediction. To achieve future point cloud generation, most existing methods are based on free-form 3D scene flow prediction. However, these simple scene flow prediction-based methods may cause distortions since the motions of 3D scenes can be seen as a combination of rigid (static background) and flow (dynamic foreground) motions. To address this issue, we propose a simple but effective rigid constrained point cloud prediction network named RPP-Net. The key component of the proposed method is the hybrid motion decoder, which generates motion masks and combines flow motions and rigid motions to produce hybrid motions without additional annotations and sensors. Besides, to reduce running time, we provide a hierarchical cell, which uses a feature encoder to extract deep features and an RPP-RNN module to capture temporal correlations across frames. To evaluate the effectiveness of our RPP-Net, we have conducted extensive experiments on both KITTI dataset and Argoverse dataset and the results show that our RPP-Net significantly outperforms existing methods and achieves a new state-of-the-art.

Dual-Branch Deep Point Cloud Registration Framework for Unconstrained Rotation

Learning-based rigid point cloud registration (RPCR) studies have made great progress recently but most existing methods have a small convergence region and can only be used to solve the registration problem with a small rotation angle, which is usually constrained within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[0, 45^\circ ]$</tex-math></inline-formula> . However, the relative rotation between point clouds is usually unconstrained in practice. To address this challenging problem, we propose a new RPCR network and integrate it into a new dual-branch registration framework for unconstrained rotation point cloud registration. The dual-branch framework consists of a large-rotation branch and a small-rotation branch, which are used to accurately register point clouds with large and small relative rotations, respectively. In addition, we propose a multi-view Intersection over Union module (MVIOU) to select a better registration result from the output of the two branches. Extensive experiments on both ModelNet40 and MVP-RG datasets demonstrate that our proposed method outperforms existing state-of-the-art techniques by a large margin. The code and pre-trained models will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fukexue/Point-DR</uri> .

Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms

This study addresses multi-view Terrestrial Laser Scanning Point Cloud data registration methods. Multiple rigid point cloud data registration is mandatory for aligning all scans into a common reference frame and it is still considered a challenge looking from a large-scale surveys point of view. The goal of this work is to support the development of cutting-edge registration methods in geoscience and mobile robotics domains. This work evaluates 3 data sets of total 20 scenes available in the literature. This paper provides a novel open-source framework for multi-view Terrestrial Laser Scanning Point Cloud data registration benchmarks. The goal was to verify experimentally which registration variant can improve the open-source data looking from the quantitative and qualitative points of view. In particular, the following scanners provided measurement data: Z+F TLS Imager 5006i, Z+F TLS Imager 5010C, Leica ScanStation C5, Leica ScanStation C10, Leica P40 and Riegl VZ-400. The benchmark shows an impact of the metric e.g. point to point, point to projection onto a plane, plane to plane etc..., rotation matrix parameterization (Tait–Bryan, quaternion, Rodrigues) and other implementation variations (e.g. multi-view Normal Distributions Transform, Pose Graph SLAM approach) onto the multi-view data registration accuracy and performance. An open-source project is created and it can be used for improving existing data sets reported in the literature, it is the added value of the presented research. The combination of metrics, rotation matrix parameterization and optimization algorithms creates hundreds of possible approaches. It is shown that chosen metric is a dominant factor in data registration. The rotation parameterization and other degrees of freedom of proposed variants are rather negligible compared with chosen metric. Most of the proposed approaches improve registered reference data provided by other researchers. Only for 2 from 20 scenes it was not possible to provide significant improvement. The largest improvements are evident for large-scale scenes. The project is available and maintained at https://github.com/MapsHD/HDMapping.

3-D Rigid Point Set Registration for Computer-Assisted Orthopedic Surgery (CAOS): A Review From the Algorithmic Perspective

Registration is an important problem in computerassisted orthopedic surgery (CAOS). As one typical example, the pre-operative space where the patient-specific surgical plan is usually made needs to be accurately aligned with the patient space where the surgical procedures are conducted. The registration task still attracts a lot of research efforts, partially because establishing the point correspondences between two point sets (PSs) under noise, outliers and partial overlapping is a non-trivial problem. More specifically, in CAOS, (a) the intraoperative points only cover a small partial region of the preoperative whole model; (b) the acquired intra-operative points are usually noisy and contains outliers. To facilitate the related researchers to quickly know both the classical and state-of-theart rigid point set registration (RPSR) methods, we present a concise review about related rigid registration methods in CAOS in this paper. The contributions of this paper include: (1) we review the RPSR methods that are suitable for or very related to the CAOS application; (2) the surveyed registration algorithms’ advantages and disadvantages are discussed and compared; (3) potential future research directions are also discussed.

The modified occipital condyle screw: A quantitative anatomic study investigating the feasibility of a novel instrumented fixation technique for craniocervical fusion.

Prospective human anatomical study. Occipitocervical fusion with occipital plate or condyle screws has shown higher failure rates in those with skeletal dysplasia. The modified occipital condyle screw connects the occipital condyle to the pars basilaris of the occipital bone that may achieve fortified bony purchase and serve as a more rigid fixation point. We evaluate anatomical feasibility of a novel cranial fixation technique designed to decrease risk of pseudarthrosis. Occipital condyles were analyzed morphologically using multiplanar three-dimensional reconstructed, ultra-thin section computed tomography. The following parameters were obtained: occipital condyle length, maximal cross section, location of hypoglossal canal, axial and sagittal orientation of the long axis, occipital condyle pedicle (OCP) diameter, maximal length of OCP screw, and entry point. Forty patients with total of 80 occipital condyles were analyzed and the following measurements were obtained: occipital condyle length 24.1 mm (20.5-27.7, standard deviation [SD]: 2.2); condyle maximum axial cross-section 12.6 mm (9-15.8, SD: 1.9); length of OCP screw 38.9 mm (29.3-44, SD: 5.7); diameter of OCP 3.4 mm (3.2-3.6, SD: 0.2); clearance below hypoglossal canal 4.5 mm (3.4-7, SD: 1.1); and distance of screw entry point from condylar foramen 2 mm (range 0-4, SD 1.6). The modified occipital condyle screw connects the condyle with the clivus through the pars basilaris and represents a safe and technically feasible approach to achieve craniocervical fusion in skeletally mature individuals. This cephalad anchor point serves as an alternate fixation point of the occipitocervical junction with increased strength of construct and decreased risk of hardware failure or pseudarthrosis given cortical bone purchase and longer screw instrumentation.

Transient Deformation of an Anisotropic Cylindrical Shell with Structural Features

In this work, a new transient function of normal displacements is obtained in a thin elastic anisotropic cylindrical shell of constant thickness with structural features. Structural features form of two sequences in angular direction of weld spots or rivets. Structural features are mathematically described by point boundary conditions of rigid pinching or point boundary conditions of hinge support, accordingly. A load with time variable amplitude and impact boundaries influences on the outer surface of the shell along the normal. Such movement of the shell is considered in the cylindrical coordinate system as connected with the shell axis. Kirchhoff–Love hypothesis are confirmed as a shell model. The solution of this problem can be formed with the help of Green function method and lift compensation method. The required function of transient normal displacements is represented as the sum of integral operators of Green convolution for an infinite shell with functions of current transient load and compensating loads out of point boundary conditions. Compensating loads satisfying to boundary conditions can be found based on the solution of Volterra integral equations of the first kind with the difference kernel. Green function is such kernel. The system solution can be executed including preliminary discretization of compensating time loads. The convolution integrals can be taken numerically with the help of quadrature formula by the method of rectangles. The verification of the method is represented by comparing the solution of specific case with the known solution for a hinged isotropic cylindrical shell.

Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration

Registration is highly demanded in many real-world scenarios such as robotics and automation. Registration is challenging partly due to the fact that the acquired data is usually noisy and has many outliers. In addition, in many practical applications, one point set (PS) usually only covers a partial region of the other PS. Thus, most existing registration algorithms cannot guarantee theoretical convergence. This article presents a novel, robust, and accurate three-dimensional (3D) rigid point set registration (PSR) method, which is achieved by generalizing the state-of-the-art (SOTA) Bayesian coherent point drift (BCPD) theory to the scenario that high-dimensional point sets (PSs) are aligned and the anisotropic positional noise is considered. The high-dimensional point sets typically consist of the positional vectors and normal vectors. On one hand, with the normal vectors, the proposed method is more robust to noise and outliers, and the point correspondences can be found more accurately. On the other hand, incorporating the registration into the BCPD framework will guarantee the algorithm’s theoretical convergence. Our contributions in this article are three folds. First, the problem of rigidly aligning two general PSs with normal vectors is incorporated into a variational Bayesian inference framework, which is solved by generalizing the BCPD approach while the anisotropic positional noise is considered. Second, the updated parameters during the algorithm’s iterations are given in closed-form or with iterative solutions. Third, extensive experiments have been done to validate the proposed approach and its significant improvements over the BCPD. Note to Practitioners—This paper was motivated by the problem of 3D rigid PSR for computer-assisted surgery (CAS), especially in orthopedic applications. The proposed algorithm is also suitable for other scenarios where the initial coarse registration is conducted. The traditional registration methods are susceptible to noise (especially anisotropic noise), outliers, and incomplete partial data. This paper generalizes the recently proposed BCPD method to the six-dimensional scenario where anisotropic positional noise is considered and normal vectors are incorporated. The proposed noise model is decomposed into three parts to be solved alternately: the membership probability of mixture distributions, the soft correspondence estimation, and the model parameters (i.e., the rotation matrix, translation vector, the covariance matrix with the anisotropic positional error, and the concentration parameter with the estimation of the normal vectors). Especially, the convergence is guaranteed at the theoretical level using the variational inference theory. The experimental results demonstrate the superiority of our algorithm on registration accuracy, convergence speed, and robustness to noise, outliers, and partial data.

Anisotropic 6-D Rigid Point Set Registration

Anisotropic 6-D Rigid Point Set Registration

Developing a three-dimensional statistical shape model of normal dentition using an automated algorithm and normal samples

The statistical shape model (SSM) is a model of geometric properties of a set of shapes based on statistical shape analysis. The SSM develops an average model of several objects using an automated algorithm that excludes the operator's subjectivity. The aim of this study was to develop a three-dimensional (3D) SSM of normal dentition to provide virtual templates for efficient treatment. Dental casts were obtained from participants with normal dentition. After acquiring the 3D models, the SSMs of the individual teeth and whole dental arch were generated by an iterative closest point (ICP)-based rigid registration and point correspondences, respectively. Then, the individual tooth SSM was aligned to the whole dental arch SSM using ICP-based registration to generate an average model of normal dentition. The generated 3D SSM showed specific morphological features of normal dentition similar to those previously reported. Moreover, on measuring the arch dimensions, all values in this study were similar to those previously reported using normal dentition. The 3D SSM of normal dentition may increase the diagnostic efficiency of orthodontic treatments by providing a visual objective. It can be alsoused as a 3D template in various fields of dentistry. Our SSM of normal dentition provides both quantitative and qualitative information on the 3D morphology of teeth and dental arches, which may provide valuable information on 3D virtual-setup, bracket fabrication, and aligner treatment.

Predicting rupture locations of ascending aortic aneurysms using CT-based finite element models

Accurate rupture risk assessment of ascending aortic aneurysms is important for reducing aneurysm-related mortality. More recently, computational models have been shown to better predict rupture risk than diameter-based measurements. However, it remains unclear whether finite element (FE) models of the ascending aorta can predict rupture location, and over what timeframe those predictions are reliable. The aim of this study was to evaluate FE models of the ascending aorta generated from computed tomography (CT) scans in predicting rupture location. Pre- and post-rupture CT scans were obtained of 12 patients who underwent emergency surgical repair for ascending aorta rupture with varying time intervals between scans (20 days – 6 years). A rigid iterative closest point (ICP) registration was used to overlay post-rupture aortic geometries with pre-rupture FE models and directly compare predicted regions of high equivalent strain with actual rupture. The FE model predicted the rupture location in the 5 patients with the shortest time intervals between the pre- and post-rupture scans (20 days – 2 years, 3 months). However, rupture location was not predicted in the 4/5 patients with greater than 3 years between scans. Achieving a physiological equivalent strain distribution in the FE model was highly dependent on the resolution of the pre-rupture scan and whether contrast agent was present. The results suggest there may be a time interval beyond which FE predictions of rupture location may not be reliable. The results in this study may help clinical validation of FE models of ascending aortic aneurysms predicting rupture risk.

Point matching based on affine invariant centroid trees

Object detection can be formulated as a point matching problem when objects are modeled by point sets. Moments, which have been widely used for point matching, are limited to affine transformations as their support point sets cannot keep invariant. To address this problem, we developed an affine invariant centroid tree (AICT) to obtain a rigorous affine invariant support point set (SPS). The algorithm is constructed by a recursive process: the point set is first divided by the vector from the certain point to the centroid of the point set, and the centroids of subsets are used to generate vectors for renewed partitions. In addition, the centroids of the subsets are stored to form an AICT. The AICT represents the inherent structure of the point set. It is highly tolerant to noise and outliers due to the partitions on the whole point set. More importantly, it is affine invariant owing to the affine invariance of partition. Therefore, we can get rigorous affine invariant descriptors while moments are combined with AICT. The experimental results on synthesized and real data verify that our proposed algorithm outperforms the state-of-the-art point matching methods including shape context, iterative closet point, and the method adopting thin plate spline for rigid robust point matching (TPS-RPM).

Rigid Registration of Point Clouds Based on Partial Optimal Transport

AbstractFor rigid point cloud data registration, algorithms based on soft correspondences are more robust than the traditional ICP method and its variants. However, point clouds with severe outliers and missing data may lead to imprecise many‐to‐many correspondences and consequently inaccurate registration. In this study, we propose a point cloud registration algorithm based on partial optimal transport via a hard marginal constraint. The hard marginal constraint provides an explicit parameter to adjust the ratio of points that should be accurately matched, and helps avoid incorrect many‐to‐many correspondences. Experiments show that the proposed method achieves state‐of‐the‐art registration results when dealing with point clouds with significant amount of outliers and missing points (see https://www.acm.org/publications/class‐2012).