Synthetic 3D Research Articles

Synthesis of 3D on-air signatures with the Sigma–Lognormal model

Signature synthesis is a computation technique that generates artificial specimens which can support decision making in automatic signature verification. A lot of work has been dedicated to this subject, which centres on synthesizing dynamic and static two-dimensional handwriting on canvas. This paper proposes a framework to generate synthetic 3D on-air signatures exploiting the lognormality principle, which mimics the complex neuromotor control processes at play as the fingertip moves. Addressing the usual cases involving the development of artificial individuals and duplicated samples, this paper contributes to the synthesis of: (1) the trajectory and velocity of entirely 3D new signatures; (2) kinematic information when only the 3D trajectory of the signature is known, and (3) duplicate samples of 3D real signatures. Validation was conducted by generating synthetic 3D signature databases mimicking real ones and showing that automatic signature verifications of genuine and skilled forgeries report performances similar to those of real and synthetic databases. We also observed that training 3D automatic signature verifiers with duplicates can reduce errors. We further demonstrated that our proposal is also valid for synthesizing 3D air writing and gestures. Finally, a perception test confirmed the human likeness of the generated specimens. The databases generated are publicly available, only for research purposes, at .

A Simple Correction to the Apparent Conductivity from Surface Magnetic Dipole Data

A method to improve the determination of apparent conductivities from near-surface electromagnetic dipole data should be simple and easy to implement and use in the field. With this objective, we present a simple correction to the apparent resistivity from surface magnetic dipole data. In contrast with the traditional approach that uses a truncated series to approximate the fields, the proposed method calculates the apparent conductivity by solving a minimization problem using the complete homogeneous half-space analytical solutions for the magnetic field of the dipole sources. In comparison with previous approaches to improve apparent conductivity estimation, this is an extremely simple solution, easily implemented and demanding almost negligible computer time and memory. To illustrate the method, applications to synthetic 2D data and real data are presented. The results show that the method can improve the responses of conductive zones which are difficult to perceive in the raw field data.

Time-based Chern number in periodically driven systems in the adiabatic limit

To define the topology of driven systems, recent works have proposed synthetic dimensions as a way to uncover the underlying parameter space of topological invariants. Using time as a synthetic dimension, together with a momentum dimension, gives access to a synthetic two-dimensional ($2\mathrm{D}$) Chern number. It is, however, still unclear how the synthetic 2D Chern number is related to the Chern number that is defined from a parametric variable that evolves with time. Here we show that in periodically driven systems in the adiabatic limit, the synthetic $2\mathrm{D}$ Chern number is a multiple of the Chern number defined from the parametric variable. The synthetic 2D Chern number can thus be engineered via how the parametric variable evolves in its own space. We justify our claims by investigating Thouless pumping in two one-dimensional ($1\mathrm{D}$) tight-binding models, a three-site chain model, and a two-$1\mathrm{D}$-sliding-chains model. The present findings could be extended to higher dimensions and other periodically driven configurations.

A Review of 3D Polymeric Scaffolds for Bone Tissue Engineering: Principles, Fabrication Techniques, Immunomodulatory Roles, and Challenges.

Over the last few years, biopolymers have attracted great interest in tissue engineering and regenerative medicine due to the great diversity of their chemical, mechanical, and physical properties for the fabrication of 3D scaffolds. This review is devoted to recent advances in synthetic and natural polymeric 3D scaffolds for bone tissue engineering (BTE) and regenerative therapies. The review comprehensively discusses the implications of biological macromolecules, structure, and composition of polymeric scaffolds used in BTE. Various approaches to fabricating 3D BTE scaffolds are discussed, including solvent casting and particle leaching, freeze-drying, thermally induced phase separation, gas foaming, electrospinning, and sol-gel techniques. Rapid prototyping technologies such as stereolithography, fused deposition modeling, selective laser sintering, and 3D bioprinting are also covered. The immunomodulatory roles of polymeric scaffolds utilized for BTE applications are discussed. In addition, the features and challenges of 3D polymer scaffolds fabricated using advanced additive manufacturing technologies (rapid prototyping) are addressed and compared to conventional subtractive manufacturing techniques. Finally, the challenges of applying scaffold-based BTE treatments in practice are discussed in-depth.

Design and biofabrication of bacterial living materials with robust and multiplexed biosensing capabilities.

The intertwined adoption of synthetic biology and 3D bioprinting has the potential to improve different application fields by fabricating engineered living materials (ELMs) with unnatural genetically-encoded sense & response capabilities. However, efforts are still needed to streamline the fabrication of sensing ELMs compatible with field use and improving their functional complexity. To investigate these two unmet needs, we adopted a workflow to reproducibly construct bacterial ELMs with synthetic biosensing circuits that provide red pigmentation as visible readout in response to different proof-of-concept chemical inducers. We first fabricated single-input/single-output ELMs and we demonstrated their robust performance in terms of longevity (cell viability and evolutionary stability >15 days, and long-term storage >1 month), sensing in harsh, non-sterile or nutrient-free conditions compatible with field use (soil, water, and clinical samples, including real samples from Pseudomonas aeruginosa infected patients). Then, we fabricated ELMs including multiple spatially-separated biosensor strains to engineer: level-bar materials detecting molecule concentration ranges, multi-input/multi-output devices with multiplexed sensing and information processing capabilities, and materials with cell-cell communication enabling on-demand pattern formation. Overall, we showed successful field use and multiplexed functioning of reproducibly fabricated ELMs, paving the way to a future automation of the prototyping process and boosting applications of such devices as in-situ monitoring tools or easy-to-use sensing kits.

Sulfurized Polyacrylonitrile Impregnated Delignified Wood-Based 3D Carbon Framework for High-Performance Lithium–Sulfur Batteries

Sulfurized polyacrylonitrile (SPAN) is a promising cathode active material capable of suppressing lithium polysulfide dissolution in lithium–sulfur (Li–S) batteries. However, due to the low S content in SPAN, achieving a high SPAN areal loading without compromising specific capacity and cycling stability would be required. To address this challenge, we took advantage of the inherent porous structure of natural wood and engineered carbonized delignified wood (CDW) frameworks using a delignification/low-temperature pyrolysis approach. The unique design of the SPAN-impregnated CDW (SPAN@CDW) electrode, wherein the interconnected pathways run in three dimensions, ensures effective electron and ion transport within the electrode. Biomass-derived SPAN@CDW cathodes exhibited an excellent rate capability compared to the popular synthetic 3D scaffolds, such as graphene foam cathodes, with discharge capacities >1000 mA h gs–1 at 1 C (1672 mA g–1). Electrodes with CDW pyrolyzed at 600 °C, and a S areal loading of ∼2 mg cm–2 exhibited a high specific capacity of ∼1350 mA h gs–1 after 500 cycles at 0.1 C, indicating good cycling stability. We also demonstrated the sustainable fabrication of SPAN@CDW electrodes with high SPAN loadings up to ∼35 mg cm–2, which could deliver a high areal capacity of ∼15.1 mA h cm–2 at 0.1 C.

Difference-of-Gaussian generative adversarial network for segmenting breast arterial calcifications in mammograms

Breast arterial calcifications (BACs) are amongst the different types of benign calcifications observed on mammograms. BACs have been found to correlate with cardiovascular risk factors, cardiovascular mortality, and coronary artery disease (CAD). Considering that women are recommended to undergo routine screening mammography for the early detection of breast cancer, identifying BACs on mammograms could help identify women at risk of cardiovascular diseases (CVD) without the additional cost or radiation of other tests, such as coronary artery computed tomography (CT). In this paper, we present a difference of Gaussian generative adversarial network (DoG-GAN) model for segmenting BACs in mammograms. It combines the multi-scale difference of Gaussian (DoG) pyramid with the U-net as a generator of the GAN. This approach allows the model to explore image details at each scale and exploit the edge information to effectively segment BACs. We evaluated the performance of our model on a set of synthetic 2D images from digital breast tomosynthesis exams (DBT) collected and prepared for this task. The experimental results show that our model outperforms the state-of-the-art methods.

Large-scale perfused tissues via synthetic 3D soft microfluidics

The vascularization of engineered tissues and organoids has remained a major unresolved challenge in regenerative medicine. While multiple approaches have been developed to vascularize in vitro tissues, it has thus far not been possible to generate sufficiently dense networks of small-scale vessels to perfuse large de novo tissues. Here, we achieve the perfusion of multi-mm3 tissue constructs by generating networks of synthetic capillary-scale 3D vessels. Our 3D soft microfluidic strategy is uniquely enabled by a 3D-printable 2-photon-polymerizable hydrogel formulation, which allows for precise microvessel printing at scales below the diffusion limit of living tissues. We demonstrate that these large-scale engineered tissues are viable, proliferative and exhibit complex morphogenesis during long-term in-vitro culture, while avoiding hypoxia and necrosis. We show by scRNAseq and immunohistochemistry that neural differentiation is significantly accelerated in perfused neural constructs. Additionally, we illustrate the versatility of this platform by demonstrating long-term perfusion of developing neural and liver tissue. This fully synthetic vascularization platform opens the door to the generation of human tissue models at unprecedented scale and complexity.

Bionic artificial penile Tunica albuginea

Bionic artificial penile Tunica albuginea

Emergent non-Hermitian localization phenomena in the synthetic space of zero-dimensional bosonic systems

Phase transitions in non-Hermitian systems are at the focus of cutting edge theoretical and experimental research. On the one hand, parity-time- $(\mathcal{PT}\ensuremath{-})$ and $\mathrm{anti}\ensuremath{-}\mathcal{PT}\ensuremath{-}\mathrm{symmetric}$ physics have gained ever-growing interest, due to the existence of non-Hermitian spectral singularities called exceptional points (EPs). On the other hand, topological and localization transitions in non-Hermitian systems reveal new phenomena, e.g., the non-Hermitian skin effect and the absence of conventional bulk-boundary correspondence. The great majority of previous studies exclusively focus on non-Hermitian Hamiltonians, whose realization requires an a priori fine-tuned extended lattice to exhibit topological and localization transition phenomena. In this work, we show how the non-Hermitian localization phenomena can naturally emerge in the synthetic field moment space of zero-dimensional bosonic systems, e.g., in $\mathrm{anti}\ensuremath{-}\mathcal{PT}$- and $\mathcal{PT}\ensuremath{-}\mathrm{symmetric}$ quantum dimers. This offers an opportunity to simulate localization transitions in low-dimensional systems, without the need to construct complex arrays of, e.g., coupled cavities or waveguides. Indeed, the field moment equations of motion can describe an equivalent (quasi)particle moving in a one-dimensional (1D) synthetic lattice. This synthetic field moment space can exhibit nontrivial localization phenomena, such as non-Hermitian skin effect, induced by the presence of highly degenerate EPs. We demonstrate our findings on the example of an $\mathrm{anti}\ensuremath{-}\mathcal{PT}\ensuremath{-}\mathrm{symmetric}$ two-mode system, whose higher-order field moment eigenspace is emulated by a synthetic 1D non-Hermitian Hamiltonian having a Sylvester matrix shape. Our results can be directly verified in state-of-the-art optical setups, such as superconducting circuits and toroidal resonators, by measuring photon moments or correlation functions.

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

An Intelligent Vision-Based Nutritional Assessment Method for Handheld Food Items

Dietary assessment has proven to be an important tool to evaluate the dietary intake of patients with diabetes and obesity. The traditional approach of accessing the dietary intake is to conduct a 24-hour dietary recall (24HR), a structured interview designed to obtain information on food categories and volume consumed by the participants. Due to the unconscious biases in this kind of self-reporting approach, many research studies have explored the use of vision-based approaches to provide accurate and objective assessments. Despite the promising results of food recognition by deep neural networks, there still exist several hurdles in deep learning-based food volume estimation ranging from domain shift between synthetic and raw 3D models, shape completion ambiguity and, most importantly, lack of large-scale paired training dataset. Therefore, this paper proposed an intelligent nutritional assessment approach via weakly-supervised point cloud completion. It aims to close the reality gap in 3D point cloud completion tasks and address the targeted challenges by proposing the Point Cloud Auxiliary Classifier GAN (PC-ACGAN), a novel and robust point cloud completion architecture that can be trained in a weakly-supervised manner. Then the volume can be easily estimated from the completed representation of the food. In addition to advantages with regard to efficiency, another major merit of our system is that it can be used to estimate the volume of handheld food items without requiring the constraints including placing the food items on a table or next to fiducial markers, which facilitate the implementation on both wearable and handheld cameras. Comprehensive experiments have been carried out on major benchmark datasets and self-constructed volume-annotated dataset respectively, in which the proposed PC-ACGAN demonstrates comparable results with several strong fully-supervised baseline methods and shows superior completion ability in handling food volume estimation.

EB-LG Module for 3D Point Cloud Classification and Segmentation

Thanks to the development of deep learning technology and computer science, 3D point cloud analysis is becoming a research hotspot. Based on convolution operator, local feature learning is fundamental but far from perfect regarding point cloud analysis task. Actually, when people see a wing-like part (local features), our brain immediately associates it with a plane model (global features), and then we further trust that it's a wing based on the feedback from our brain. Such hidden features between local features and global features are commonly-existed, but ignored by existing methods. To this end, we propose Error feature Back-projection based Local-Global (EB-LG) feature learning module for better representations of point clouds. Specifically, EB-LG module adequately captures the hidden features firstly; and then borrowing from the successful idea of error-feedback mechanism, the learned hidden features will be back-projected to original local features, so that the enhanced local features are obtained. Serving as a plug-and-play, EB-LG module is lightweight and can be easily integrated into existing state-of-the-art networks to boost their performance. Extensive evaluations on both synthetic and real-world 3D point cloud benchmarks demonstrate the effectiveness and the generalization ability of our method.

Autonomous Human and Animal Classification Using Synthetic 2D Tensor Data Based on Dual-Receiver mmWave Radar System

Autonomous Human and Animal Classification Using Synthetic 2D Tensor Data Based on Dual-Receiver mmWave Radar System

Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation.

We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.6 ±4.91, 91.9 ±4.22, 85.2 ±4.83 % for left ventricular endocardium, epicardium and left atrial segmentation respectively. This represents a relative increase of 9.2, 3.3 and 13.9 % in Dice scores compared to the previous state-of-the-art. The proposed pipeline has potential for application to a wide range of other tasks across various medical imaging modalities.

Utilization of Machine Learning for the Objective Assessment of Rhinoplasty Outcomes

Machine Learning started to provide solutions to various challenges in many fields, including medicine. The objective assessment of rhinoplasty results has been a challenge since the assessment of beauty is subjective in nature. This study explores if Machine Learning can be used to accomplish the complex task of objective evaluating the outcome evaluation and automated scoring for rhinoplasty. We introduce a methodology to map the aesthetics of visual appearance to the quantified measurements of pre-surgery, planned outcome, and post-surgery using machine learning. To develop the methodology, we generated synthetic 3D models utilizing artificial intelligence tools and applied various nasal deformities to simulate the pre-surgery, planned outcome, and post-surgery scans of rhinoplasty patients. The simulated outcomes were scored by reviewing the 3D visuals and corresponding measurements to prepare the training data for machine learning models. AutoGluon AutoML framework is used to generate the best-performing machine learning model. We successfully developed machine learning models with accuracy between 82% to 88% depending on the scoring method. We also identified the measurements that are highly influential in determining the scores. This is the first study that correlates the visual appearance and quantitative facial measurements of simulated rhinoplasty outcomes. The results suggest that an AI-based objective rhinoplasty outcome scoring tool is possible when machine learning algorithms are trained using consensus scores along with patients’ pre-surgery, planned, and post-surgery measurements. This study introduces a methodology regarding how to map the aesthetics of visual appearance to the quantified measurements of pre-surgery, planned outcome, and post-surgery using machine learning.

A 3D printable synthetic hydrogel as an immobilization matrix for continuous synthesis with fungal peroxygenases

Development of a novel immobilization strategy using synthetic 3D printable hydrogels for flow biocatalysis.

Synthetic 3D flower-like 1T/2H MoS2@CoFe2O4 composites with enhanced microwave absorption performances

Synthetic 3D flower-like 1T/2H MoS2@CoFe2O4 composites with enhanced microwave absorption performances

A synthetic human 3D in vitro lymphoid model enhancing B-cell survival and functional differentiation

To investigate B-cell differentiation and maturation occurring in the germinal center (GC) using invitro culture systems, key factors and interactions of the GC reaction need to be accurately simulated. This study aims at improving invitro GC simulation using 3D culture techniques. Human B-cells were incorporated into PEG-4MAL hydrogels, to create a synthetic extracellular matrix, supported by CD40L cells, human tonsil-derived lymphoid stromal cells, and cytokines. The differentiation and antibody production of CD19+B-cells was best supported in a 5.0%-PEG-4MAL, 2.0mM-RGD-peptide composition. The 3D culture significantly increased plasmablast and plasma cell numbers as well as antibody production, with less B-cell death compared to 2D cultures. Class switching of naive CD19+IgD+B-cells toward IgG+ and IgA+B-cells was observed. The formation of large B-cell clusters indicates the formation of GC-like structures. In conclusion, a well-characterized and controllable hydrogel-based human 3D lymphoid model is presented that supports enhanced B-cell survival, proliferation, differentiation, and antibody production.

Fast virtual view synthesis for an 8K 3D light-field display based on cutoff-NeRF and 3D voxel rendering.

Three-dimensional (3D) light-field displays can provide an immersive visual experience, which has attracted significant attention. However, the generating of high-quality 3D light-field content in the real world is still a challenge because it is difficult to capture dense high-resolution viewpoints of the real world with the camera array. Novel view synthesis based on CNN can generate dense high-resolution viewpoints from sparse inputs but suffer from high-computational resource consumption, low rendering speed, and limited camera baseline. Here, a two-stage virtual view synthesis method based on cutoff-NeRF and 3D voxel rendering is presented, which can fast synthesize dense novel views with smooth parallax and 3D images with a resolution of 7680 × 4320 for the 3D light-field display. In the first stage, an image-based cutoff-NeRF is proposed to implicitly represent the distribution of scene content and improve the quality of the virtual view. In the second stage, a 3D voxel-based image rendering and coding algorithm is presented, which quantify the scene content distribution learned by cutoff-NeRF to render high-resolution virtual views fast and output high-resolution 3D images. Among them, a coarse-to-fine 3D voxel rendering method is proposed to improve the accuracy of voxel representation effectively. Furthermore, a 3D voxel-based off-axis pixel encoding method is proposed to speed up 3D image generation. Finally, a sparse views dataset is built by ourselves to analyze the effectiveness of the proposed method. Experimental results demonstrate the method's effectiveness, which can fast synthesize novel views and 3D images with high resolution in real 3D scenes and physical simulation environments. PSNR of the virtual view is about 29.75 dB, SSIM is about 0.88, and the synthetic 8K 3D image time is about 14.41s. We believe that our fast high-resolution virtual viewpoint synthesis method can effectively improve the application of 3D light field display.