Adaptable cascaded registration for personalized maxilla completion and cleft defect volume estimation.

BACKGROUND: Cone-beam computed tomography (CBCT) images provide high-resolution insights into the underlying craniofacial anomaly in patients with cleft lip and palate (CLP), requiring non-negligible annotation costs to measure the cleft defect for the guidance of the clinical secondary alveolar bone graft procedures. Considering the cumbersome volumetric image acquisition, there is a lack of paired CLP CBCTs and normal CBCTs for learning-based anatomical structure restoration models. Nowadays, the registration-based method relieves the annotation burden, though one-shot registration and the regular mask are limited to handling fine-grained shape variations and harmony between restored bony tissues and the defected maxilla. PURPOSE: This study aimed to design and evaluate a novel method for deformable partial registration of the CLP CBCTs and normal CBCTs, enabling personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. METHODS: We proposed an adaptable deep registration framework for personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. The key ingredient was a cascaded partial registration to exploit the maxillary morphology prior and attribute transfer. Cascaded registration with coarse-to-fine registration fields handled morphological variations of cleft defects and fine-grained maxillary restoration. We designed an adaptable cleft defect mask and volumetric Boolean operators for reliable voxel filling of the defected maxilla. A total of 36 clinically obtained CLP CBCTs were used to train and validate the proposed model, among which 22 CLP CBCTs were used to generate a training dataset with 440 synthetic CBCTs by B-spline deformation-based data augmentation and the remaining for testing. The proposed model was evaluated on maxilla completion and cleft defect volume prediction from clinically obtained unilateral and bilateral CLP CBCTs. RESULTS: Extensive experiments demonstrated the effectiveness of the adaptable cleft defect mask and the cascaded partial registration on maxilla completion and cleft defect volume prediction. The proposed method achieved state-of-the-art performances with the Dice similarity coefficient of 0.90 ± $\pm$ 0.02 on the restored maxilla and 0.84 ± $\pm$ 0.04 on the estimated cleft defect, respectively. The average Hausdorff distance between the estimated cleft defect and the manually annotated ground truth was 0.30 ± $\pm$ 0.08 mm. The relative volume error of the cleft defect was 0.09 ± $0.09\pm$ 0.08. The proposed model allowed for the prediction of cleft defect maps that were in line with the ground truth in the challenging unilateral and bilateral CLP CBCTs. CONCLUSIONS: The results suggest that the proposed adaptable deep registration model enables patient-specific maxilla completion and automatic annotation of cleft defects, relieving tedious voxel-wise annotation and image acquisition burdens.

Toward intrinsic ultra-high-temperature ferromagnetism in a CrAuTe2/graphene heterosystem.

Exploring intrinsic two-dimensional (2D) ferromagnetic (FM) materials with high Curie temperatures (TC) and large magnetic anisotropy energies (MAE) is one of the effective solutions to develop materials for high-performance spintronic applications. Using density functional theory calculations and high-throughput computations, we predict an intrinsic bimetallic FM monolayer, CrAuTe2, which has a large MAE and high TC. The results show that the value of the MAE can reach about 1.5 meV per Cr, and Monte Carlo simulations show that the TC of monolayer CrAuTe2 is about 840 K. Further analysis indicates that the joint effects of spin-orbit coupling (SOC) interaction and magnetic dipole-dipole interaction result in high in-plane magnetic anisotropy. In addition, this monolayer has good dynamic, thermal, and mechanical stabilities, which were confirmed by ab initio molecular dynamics simulations, phonon spectra, and elastic constants, respectively. In order to propose a practical synthesis approach, we built a CrAuTe2/graphene van der Waals heterostructure, and found that the heterostructure does not affect the magnetic properties of monolayer CrAuTe2. These findings appear promising for the future applications in nano-spintronics.

Natural reference structures for three-dimensional maxillary regional superimposition in growing patients.

BACKGROUND: Assessment of growth-related or treatment-related changes in the maxilla requires a reliable method of superimposition. Such methods are well established for two-dimensional (2D) cephalometric images but not yet for three-dimensions (3D). The aims of this study were to identify natural reference structures (NRS) for the maxilla in growing patients in 3D, opportunistically using orthodontic mini-screws as reference; and to test the applicability of the proposed NRS for maxillary superimposition by assessing the concordance of this approach with Björk's 'stable reference structures' in lateral projection. METHODS: The stability of the mini-screws was tested on longitudinal pairs of pre- and post-orthodontic cone-beam computed tomography (CBCT) images by measuring the distance changes between screws. After verifying the stability of the mini-screws, rigid registration was performed for aligning the stable mini-screws. Then, non-rigid registration was used to establish the dense voxel-correspondence among CBCT images and calculate the displacement of each voxel belonging to the maxilla relative to the mini-screws. The displacement vectors were transformed to a standardized maxillary template to categorize the stability of the internal structures statistically. Those voxels that displaced less relative to the mini-screws were considered as the natural reference structures (NRS) for the maxilla. Test samples included another dataset of longitudinal CBCT scans. They were used to evaluate the applicability of the proposed NRS for maxillary superimposition. We assessed whether aligning the maxilla with proposed NRS is in concordance with the maxillary internal reference structures superimposition in the traditional 2D lateral view as suggested by Björk. This was quantitively assessed by comparing the mean sagittal and vertical tooth movements for both superimposition methods. RESULTS: The stability of the mini-screws was tested on 10 pairs of pre- and post-orthodontic cone-beam computed tomography (CBCT) images (T1: 12.9 ± 0.8 yrs, T2: 14.8 ± 0.7 yrs). Both the loaded and the unloaded mini-screws were shown to be stable during orthodontic treatment, which indicates that they can be used as reference points. By analyzing the deformation map of the maxilla, we confirmed that the infraorbital rims, maxilla around the piriform foramen, the infrazygomatic crest and the hard palate (palatal vault more than  1 cm distal to incisor foramen except the palatal suture) were stable during growth. Another dataset of longitudinal CBCT scans (T1: 12.2 ± 0.63 yrs, T2: 15.2 ± 0.96 yrs) was used to assess the concordance of this approach with Björk's 'stable reference structures'. The movement of the maxillary first molar and central incisor showed no statistically significant difference when superimposing the test images with the proposed NRS or with the classic Björk maxillary superimposition in the lateral view. CONCLUSIONS: The infraorbital rims, maxilla around the piriform foramen, the infrazygomatic crest and the hard palate (palatal vault more than 1 cm posterior to incisal foramen except the palatal suture) were identified as stable regions in the maxilla. These stable structures can be used for maxillary superimposition in 3D and generate comparable results to Björk superimposition in the lateral view.

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography.

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

Origin and regulation of triaxial magnetic anisotropy in the ferromagnetic semiconductor CrSBr monolayer.

Magnetic anisotropy plays a vital role in stabilizing the long-range magnetic order of two-dimensional ferromagnetic systems. In this work, using the first-principles method, we systematically explored the triaxial magnetic anisotropic properties of a ferromagnetic semiconductor CrSBr monolayer, which is recently exfoliated from its bulk. Further analysis shows that the triaxial magnetic anisotropic properties originate from the coexistence of the magnetic dipole-dipole interaction (shape anisotropy) and the spin-orbit coupling interaction (magnetocrystalline anisotropy). Interestingly, the shape anisotropy, which has been neglected in most previous works, dominates over the magnetocrystalline anisotropy. Besides, the experimental Curie temperature of the CrSBr monolayer is well reproduced using Monte Carlo simulations. What is more, the easy magnetic axes and ferromagnetism in the CrSBr monolayer can be manipulated by strains and are relatively more susceptible to the uniaxial strain in the x direction. Our study not only explains the mechanism of triaxial magnetic anisotropy of the CrSBr monolayer, but also sheds light on how to tune the magnetic anisotropy and Curie temperature in ferromagnetic monolayers.

High temperature ferromagnetic metal: a Janus CrSSe monolayer.

Two-dimensional (2D) ferromagnets are popular in fields such as spintronic devices, but their low Curie temperature (TC) limits their practical application. In this work, by using a global optimization evolutionary algorithm and density functional theory method, a Janus CrSSe ferromagnetic monolayer was predicted systematically. Monte Carlo simulations show that the Curie temperature of the Janus CrSSe monolayer is about 272 K and can be adjusted to 496 K under a small tensile biaxial strain. Besides, this monolayer possesses large magnetic anisotropy energy (1.4 meV Cr-1). The magnetic order can be changed from ferromagnetic to antiferromagnetic order under compressive strain. What's more, this monolayer possesses the lowest energy in the 2D search space and excellent thermal, dynamic, and mechanical stabilities. Considering its excellent properties and current experimental techniques, it is possible to synthesize CrSSe monolayer experimentally.

1D group V-VI-VII ternary nanowires: moderate band gaps, easy to exfoliate from bulk, and unexpected ferroelectricity.

One-dimensional nanowires have emerged as compelling ideal materials due to their characteristic structure, properties, and applications in nanodevices. Herein, based on experimental vdW-chain bulk crystals, a series of one-dimensional (1D) XVYVIZVII (X = As, Sb, Bi; Y = S, Se, Te; Z = Cl, Br, I) ternary nanowires are theoretically investigated. Such exfoliated 1D nanowires possess excellent stability and moderate band gaps (1.76-3.16 eV). The calculated electron mobilities were found to reach a magnitude of 102 cm2 V-1 s-1 and even up to 322.95 cm2 V-1 s-1 for 1D BiSeI nanowires, which are much larger than those of the previously reported 1D materials. Furthermore, the appropriate band edge alignments and considerable optical absorption endow 1D XVYVIZVII nanowires with prospective photocatalytic properties for water splitting. Notably, AsSI and AsSeI nanowires possess a unique non-centrosymmetric structure and exhibit promising 1D ferroelectricity. Large spontaneous polarization values, Ps, of 11.31 × 10-10 and 6.92 × 10-10 C m-1 are obtained for 1D AsSI and AsSeI nanowires, respectively, and such 1D ferroelectricity can be regulated by intra-chain strains. Our calculations not only broaden the family of 1D materials but also reveal their great potential applications in electronic, optoelectronic, and ferroelectric devices.

Dense correspondence of deformable volumetric images via deep spectral embedding and descriptor learning.

Deformable image correspondence plays an essential role in a variety of medical image analysis tasks. Most existing deep learning-based registration and correspondence techniques exploit metric space alignments in the spatial domain and learn a nonlinear voxel-wise mapping function between volumetric images and displacement fields, agnostic to intrinsic structure correspondence. When confronted with high-frequency perturbations of patients' poses and anatomical structural variations, they relied on prior rigid and affine transformations, as well as additional segmentation masks and landmark annotations for reliable registration. This paper presents a data-driven spectral mapping-based correspondence framework to handle the intrinsic correspondence of anatomical structures. At the core of our approach lies a deep convolutional framework that approximates spectral bases and optimizes volumetric descriptors. The multi-path graph convolutional network-based spectral embedding approximation module relieves the computationally expensive eigendecomposition-based embedding of volumetric images. The deep descriptor learning module surpasses the prior hand-crafted descriptors and the descriptor selection. We showcase the efficacy of the core modules, i.e., the spectral embedding approximation and descriptor learning, for volumetric image correspondence and the atlas-based registration on two volumetric image datasets. The proposed method achieves comparable correspondence accuracy with the state-of-the-art deep registration models, resilient to pose and shape perturbations.

Automated assessment of mandibular shape asymmetry in 3-dimensions.

INTRODUCTION: This study aimed to develop an automatic pipeline for analyzing mandibular shape asymmetry in 3-dimensions. METHODS: Forty patients with skeletal Class I pattern and 80 patients with skeletal Class III pattern were used. The mandible was automatically segmented from the cone-beam computed tomography images using a U-net deep learning network. A total of 17,415 uniformly sampled quasi-landmarks were automatically identified on the mandibular surface via a template mapping technique. After alignment with the robust Procrustes superimposition, the pointwise surface-to-surface distance between original and reflected mandibles was visualized in a color-coded map, indicating the location of asymmetry. The degree of overall mandibular asymmetry and the asymmetry of subskeletal units were scored using the root-mean-squared-error between the left and right sides. These asymmetry parameters were compared between the skeletal Class I and skeletal Class III groups. RESULTS: The mandible shape was significantly more asymmetrical in patients with skeletal Class III pattern with positional asymmetry. The condyles were identified as the most asymmetric region in all groups, followed by the coronoid process and the ramus. CONCLUSIONS: This automated approach to quantify mandibular shape asymmetry will facilitate high-throughput image processing for big data analysis. The spatially-dense landmarks allow for evaluating mandibular asymmetry over the entire surface, which overcomes the information loss inherent in conventional linear distance or angular measurements. Precise quantification of the asymmetry can provide important information for individualized diagnosis and treatment planning in orthodontics and orthognathic surgery.

Deep Volumetric Descriptor Learning for Dense Correspondence of Cone-Beam Computed Tomography via Spectral Maps.

The deep neural network has achieved great success in 3D volumetric correspondence. These methods infer the dense displacement or velocity fields directly from the extracted volumetric features without addressing the intrinsic structure correspondence, being prone to shape and pose variations. On the other hand, the spectral maps address the intrinsic structure matching in the low dimensional embedding space, remain less involved in volumetric image correspondence. This paper presents an unsupervised deep volumetric descriptor learning neural network via the low dimensional spectral maps to address the dense volumetric correspondence. The neural network is optimized by a novel criterion on descriptor alignments in the spectral domain regarding the supervoxel graph. Aside from the deep convolved multi-scale features, we explicitly address the supervoxel-wise spatial and cross-channel dependencies to enrich deep descriptors. The dense volumetric correspondence is formulated as the low-dimensional spectral mapping. The proposed approach has been applied to both synthetic and clinically obtained cone-beam computed tomography images to establish dense supervoxel-wise and up-scaled voxel-wise correspondences. Extensive series of experimental results demonstrate the contribution of the proposed approach in volumetric descriptor extraction and consistent correspondence, facilitating attribute transfer for segmentation and landmark location. The proposed approach performs favorably against the state-of-the-art volumetric descriptors and the deep registration models, being resilient to pose or shape variations and independent of the prior transformations.

Deformable registration of lateral cephalogram and cone-beam computed tomography image.

PURPOSE: This study aimed to design and evaluate a novel method for the registration of 2D lateral cephalograms and 3D craniofacial cone-beam computed tomography (CBCT) images, providing patient-specific 3D structures from a 2D lateral cephalogram without additional radiation exposure. METHODS: We developed a cross-modal deformable registration model based on a deep convolutional neural network. Our approach took advantage of a low-dimensional deformation field encoding and an iterative feedback scheme to infer coarse-to-fine volumetric deformations. In particular, we constructed a statistical subspace of deformation fields and parameterized the nonlinear mapping function from an image pair, consisting of the target 2D lateral cephalogram and the reference volumetric CBCT, to a latent encoding of the deformation field. Instead of the one-shot registration by the learned mapping function, a feedback scheme was introduced to progressively update the reference volumetric image and to infer coarse-to-fine deformations fields, accounting for the shape variations of anatomical structures. A total of 220 clinically obtained CBCTs were used to train and validate the proposed model, among which 120 CBCTs were used to generate a training dataset with 24k paired synthetic lateral cephalograms and CBCTs. The proposed approach was evaluated on the deformable 2D-3D registration of clinically obtained lateral cephalograms and CBCTs from growing and adult orthodontic patients. RESULTS: Strong structural consistencies were observed between the deformed CBCT and the target lateral cephalogram in all criteria. The proposed method achieved state-of-the-art performances with the mean contour deviation of 0.41 ± 0.12 mm on the anterior cranial base, 0.48 ± 0.17 mm on the mandible, and 0.35 ± 0.08 mm on the maxilla, respectively. The mean surface mesh ranged from 0.78 to 0.97 mm on various craniofacial structures, and the LREs ranged from 0.83 to 1.24 mm on the growing datasets regarding 14 landmarks. The proposed iterative feedback scheme handled the structural details and improved the registration. The resultant deformed volumetric image was consistent with the target lateral cephalogram in both 2D projective planes and 3D volumetric space regarding the multicategory craniofacial structures. CONCLUSIONS: The results suggest that the deep learning-based 2D-3D registration model enables the deformable alignment of 2D lateral cephalograms and CBCTs and estimates patient-specific 3D craniofacial structures.

B2S3monolayer: a two-dimensional direct-gap semiconductor with tunable band-gap and high carrier mobility.

Atomically two-dimensional materials with direct band-gap and high carrier mobility are highly desirable due to their promising applications in electronic devices. Here, on the basis ofab initiocalculations and global particle-swarm optimization method, we predict the B2S3monolayer as a new semiconductor with favorable functional properties. The B2S3monolayer possesses a high electron mobility of 553 cm2V-1s-1and a direct band-gap of 1.85 eV. The direct band-gap can be manipulated under biaxial strain. Furthermore, B2S3monolayer can absorb sunlight efficiently in the entire range of the visible light spectrum. Besides, this monolayer holds good dynamical, thermal, and mechanical stabilities. All the desired properties render B2S3monolayer a promising candidate for future applications in high-speed (opto)electronic devices.

Thickness change of masseter muscles and the surrounding soft tissues in female patients during orthodontic treatment: a retrospective study.

BACKGROUND: Facial esthetics is a major concern of orthodontic patients. This study aims to evaluate orthodontic treatment-related thickness changes of the masseter muscles and surrounding soft tissues and the potential factors that would influence these changes during orthodontic treatment in female adults. METHODS: Forty-two female adult patients were included in this retrospective study and were divided into extraction (n = 22) and nonextraction (n = 20) groups. Pretreatment and posttreatment cone-beam computed tomography (CBCT) images were superimposed and reconstructed. The thickness changes of the masseter area of facial soft tissue (MAS), masseter muscles (MM) and surrounding fat tissue (FT) were measured. Pretreatment age, treatment duration, sagittal relationship (ANB), and vertical relationship (Frankfort-mandibular plane angle, FMA)-related MAS, MM and FT changes were compared between extraction and nonextraction groups. Spearman's correlation coefficient was calculated between the above variables. Regression analysis was conducted to confirm the causal relations of the variables. RESULTS: The thickness of MAS and MM significantly decreased in both groups, with larger decreases (> 1 mm) in the extraction group. There were strong correlations (r > 0.7) between the thickness decrease in MAS and MM in both groups and moderate correlations (r > 0.4) between MAS and FT in the nonextraction group. A significantly greater decrease of MAS and MM were found to be moderately correlated with a smaller FMA (r > 0.4) in the extraction group. Scatter plots and regression analysis confirmed these correlations. CONCLUSIONS: Masseter muscles and the surrounding soft tissue exhibited a significant decrease in thickness during orthodontic treatment in female adults. Low-angle patients experienced a greater decrease in soft tissue thickness in the masseter area in the extraction case. But the thickness changes were clinically very small in most patients.