3D Editing Research Articles

Advances in text-guided 3D editing: a survey

In 3D Artificial Intelligence Generated Content (AIGC), compared with generating 3D assets from scratch, editing extant 3D assets satisfies user prompts, allowing the creation of diverse and high-quality 3D assets in a time and labor-saving manner. More recently, text-guided 3D editing that modifies 3D assets guided by text prompts is user-friendly and practical, which evokes a surge in research within this field. In this survey, we comprehensively investigate recent literature on text-guided 3D editing in an attempt to answer two questions: What are the methodologies of existing text-guided 3D editing? How has current progress in text-guided 3D editing gone so far? Specifically, we focus on text-guided 3D editing methods published in the past 4 years, delving deeply into their frameworks and principles. We then present a fundamental taxonomy in terms of the editing strategy, optimization scheme, and 3D representation. Based on the taxonomy, we review recent advances in this field, considering factors such as editing scale, type, granularity, and perspective. In addition, we highlight four applications of text-guided 3D editing, including texturing, style transfer, local editing of scenes, and insertion editing, to exploit further the 3D editing capacities with in-depth comparisons and discussions. Depending on the insights achieved by this survey, we discuss open challenges and future research directions. We hope this survey will help readers gain a deeper understanding of this exciting field and foster further advancements in text-guided 3D editing.

Navigating the Future of 3D Laparoscopic Liver Surgeries: Visualization of Internal Anatomy on Laparoscopic Images With Augmented Reality.

Liver tumor resection requires precise localization of tumors and blood vessels. Despite advancements in 3-dimensional (3D) visualization for laparoscopic surgeries, challenges persist. We developed and evaluated an augmented reality (AR) system that overlays preoperative 3D models onto laparoscopic images, offering crucial support for 3D visualization during laparoscopic liver surgeries. Anatomic liver structures from preoperative computed tomography scans were segmented using open-source software including 3D Slicer and Maya 2022 for 3D model editing. A registration system was created with 3D visualization software utilizing a stereo registration input system to overlay the virtual liver onto laparoscopic images during surgical procedures. A controller was customized using a modified keyboard to facilitate manual alignment of the virtual liver with the laparoscopic image. The AR system was evaluated by 3 experienced surgeons who performed manual registration for a total of 27 images from 7 clinical cases. The evaluation criteria included registration time; measured in minutes, and accuracy; measured using the Dice similarity coefficient. The overall mean registration time was 2.4±1.7 minutes (range: 0.3 to 9.5min), and the overall mean registration accuracy was 93.8%±4.9% (range: 80.9% to 99.7%). Our validated AR system has the potential to effectively enable the prediction of internal hepatic anatomic structures during 3D laparoscopic liver resection, and may enhance 3D visualization for select laparoscopic liver surgeries.

LiDAR and CAD Reconstruction of Queen's University Ontario Hall

The emergence of highly accurate LiDAR (Light Detection and Ranging) technology has opened new avenues for engaging with and understanding cultural heritage sites. How do we convert buildings like Ontario Hall into editable 3D models to engage various stakeholders in a campus setting? This initiative developed from the goal of MobArch (Queen's Mobile Laboratory for the study of Architecture and the Built Environment) to use non-invasive visualization technologies to understand the social, developmental, and environmental impacts upon our built environment and architectural past. Current initiatives involving LiDAR for heritage conservation investigate hidden aspects of sites or address structural disrepair. The Ontario Hall project uniquely documents the state of a heritage building before structural concerns arise. This project creates an accurate digital record of Ontario Hall for future infrastructure initiatives and cultural preservation. In addition, this project tests the limits of LiDAR with 3D reconstruction in CAD (Computer Aided Design) software. This project used the Leica RTC360 3D Laser Scanner to create over 300 scans with a point cloud density of 6mm. The resulting cloud was optimized using Leica Cyclone Register 360 Plus software and was imported to Sketch Up where a 3D model was manually reconstructed based on the cloud data. The result of this project is a SKP file containing the 3D model, and the point cloud. The cloud is a highly accurate resource for future infrastructure projects. The 3D model contains room volumes, windows, staircases, and external architectural elements in a more edit-friendly software environment allowing diverse stakeholders to creatively reimagine Ontario Hall. The model can be integrated into archives, websites, games, and VR. Heritage architecture can contribute to the marketing appeal and visual identity of Queen’s. Moreover, awareness of community heritage improves social wellbeing and addresses the need to connect with our built history.

MeshWGAN: Mesh-to-Mesh Wasserstein GAN With Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation.

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications.

EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points.

Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and support topological changes. Input with an image sequence from a single camera, our network is trained automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art.

SketchDream: Sketch-based Text-To-3D Generation and Editing

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing texture-less mesh models within predefined categories. Integrating sketch and text simultaneously for 3D generation promises enhanced control over geometry and appearance but faces challenges from 2D-to-3D translation ambiguity and multi-modal condition integration. Moreover, further editing of 3D models in arbitrary views will give users more freedom to customize their models. However, it is difficult to achieve high generation quality, preserve unedited regions, and manage proper interactions between shape components. To solve the above issues, we propose a text-driven 3D content generation and editing method, SketchDream, which supports NeRF generation from given hand-drawn sketches and achieves free-view sketch-based local editing. To tackle the 2D-to-3D ambiguity challenge, we introduce a sketch-based multi-view image generation diffusion model, which leverages depth guidance to establish spatial correspondence. A 3D ControlNet with a 3D attention module is utilized to control multi-view images and ensure their 3D consistency. To support local editing, we further propose a coarse-to-fine editing approach: the coarse phase analyzes component interactions and provides 3D masks to label edited regions, while the fine stage generates realistic results with refined details by local enhancement. Extensive experiments validate that our method generates higher-quality results compared with a combination of 2D ControlNet and image-to-3D generation techniques and achieves detailed control compared with existing diffusion-based 3D editing approaches.

TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts

Text-driven 3D scene editing has gained significant attention owing to its convenience and user-friendliness. However, existing methods still lack accurate control of the specified appearance and location of the editing result due to the inherent limitations of the text description. To this end, we propose a 3D scene editing framework, TIP-Editor, that accepts both text and image prompts and a 3D bounding box to specify the editing region. With the image prompt, users can conveniently specify the detailed appearance/style of the target content in complement to the text description, enabling accurate control of the appearance. Specifically, TIP-Editor employs a stepwise 2D personalization strategy to better learn the representation of the existing scene and the reference image, in which a localization loss is proposed to encourage correct object placement as specified by the bounding box. Additionally, TIP-Editor utilizes explicit and flexible 3D Gaussian splatting (GS) as the 3D representation to facilitate local editing while keeping the background unchanged. Extensive experiments have demonstrated that TIP-Editor conducts accurate editing following the text and image prompts in the specified bounding box region, consistently outperforming the baselines in editing quality, and the alignment to the prompts, qualitatively and quantitatively.

Instruct Pix-to-3D: Instructional 3D object generation from a single image

We study a challenging task, creating the instructional 3D object based on a single image and human instruction. Although existing methods utilize pre-trained text-to-image diffusion models to optimize Neural Radiance Fields (NeRF) and achieve remarkable results, they are inherently challenged in addressing this task due to two inherent obstacles. First, the written instruction is mismatched or uncorrelated with target images in the distribution of pre-trained text-to-image diffusion models, which leads to insufficient supervision. Another is editing 3D content starting from a single image, which is an ill-posed problem as the image only provides partial information. To tackle the difficulties, we propose an Instruct Pixel-to-3D framework, which exploits the supervisory signals from the given instructions and integrates the domain-specific knowledge drawn from 2D and 3D diffusion models. Specifically, to address the first challenge, we propose instruction guidance to facilitate meticulous image editing using fine-grained instructions and regulate the novel views of 3D objects using positional prompts. To tackle the second difficulty, we propose diffusion guidance for 3D generation, which optimizes the NeRF employing dual guidances: a 2D diffusion guidance derived from the score distillation sampling (SDS) loss and a 3D diffusion guidance embodied by the Zero-1-to-3 loss. Moreover, Instruct Pix-to-3D adopts a coarse-to-fine training strategy aimed at mitigating the complexities inherent in reconstructing scenes from sparse viewpoints. Qualitative and quantitative experiments on the DTU dataset and in-the-wild images demonstrate that our approach could synthesize high-quality and high-fidelity 3D content based on a single image and human instruction.

Hydrogel Encapsulation Techniques and Its Clinical Applications in Drug Delivery and Regenerative Medicine: A Systematic Review.

Hydrogel encapsulation is a promising carrier for cell and drug delivery due to its ability to protect the encapsulated entities from harsh physiological conditions and enhance their therapeutic efficacy and bioavailability. However, there is not yet consensus on the optimal hydrogel type, encapsulation method, and clinical application. Therefore, a systematic review of hydrogel encapsulation techniques and their potential for clinical application is needed to provide a comprehensive and up-to-date overview. In this systematic review, we searched electronic databases for articles published between 2008 and 2023 that described the encapsulation of cells or drug molecules within hydrogels. Herein, we identified 9 relevant studies that met the inclusion and exclusion criteria of our study. Our analysis revealed that the physicochemical properties of the hydrogel, such as its porosity, swelling behavior, and degradation rate, play a critical role in the encapsulation of cells or drug molecules. Furthermore, the encapsulation method, including physical, chemical, or biological methods, can affect the encapsulated entities' stability, bioavailability, and therapeutic efficacy. Challenges of hydrogel encapsulation include poor control over the release of encapsulated entities, limited shelf life, and potential immune responses. Future directions of hydrogel encapsulation include the development of novel hydrogel and encapsulation methods and the integration of hydrogel encapsulation with other technologies, such as 3D printing and gene editing. In conclusion, this review is useful for researchers, clinicians, and policymakers who are interested in this field of drug delivery and regenerative medicine that can serve as a guide for the future development of novel technologies that can be applied into clinical practice.

SIMULATING MOLECULAR OPTICAL PROPERTIES OF POLYIMIDE FOR NONVOLATILE RESISTIVE PHOTOMEMORY DEVICES

In this study, the focus was on utilizing the polyimide (PI) thin film as a resistive conversion layer for organic polyimide-based resistive random-access memory (ReRAM) applications. We utilized the 3D molecular editor WebMO to manipulate and analyze the structures of Aromatic-PI and Quinoid-PI molecules. The molecular chain length, molecular weight, and HOMO/LUMO molecular orbitals were investigated to gain insights into the fundamental properties of PI molecular structures. The analysis revealed that the Quinoid-PI molecule exhibited a shorter molecular chain compared to Aromatic-PI, attributed to the inability of the cyclic structure in Quinoid-PI to maintain a planar conjugated structure, resulting in an uneven overall structure and reduced molecular chain length (Norcorss, 1988). The research results demonstrate that the optical energy gap of Aromatic-PI remains constant regardless of the number of molecular bonds, with a value of approximately 3.2661 eV. In contrast, for Quinoid-PI, the optical energy gap undergoes a significant decrease as the number of molecular bonds ranges from 1 to 5. As the bond count increases to 20~23, the change in the optical energy gap gradually diminishes, ultimately resulting in an optical energy gap of 0.6932 eV. This observation indicates that longer molecular chains composed of Aromatic-PI act as insulators, while Quinoid-PI transforms into a low-energy gap conductor.

User-centric design of a 3D search interface for protein-ligand complexes

In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine’s new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine’s 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server (https://proteins.plus).

ВИКОРИСТАННЯ МЕТОДІВ ВІРТУАЛЬНОЇ РЕАЛЬНОСТІ ДЛЯ ОТРИМАННЯ ПРАКТИЧНИХ НАВИКІВ З ВИПРОБУВАНЬ БУДІВЕЛЬНИХ МАТЕРІАЛІВ

Introduction. Current developments that are being implemented by modern organizations must include not only initial methodological complexes of disciplines (modules), but also security programs aimed at mastering professional competencies. The optimal way to develop competencies is through virtual laboratories, which are modeled in an electronic lighting environment on real-world objects. Problem Statement. The problem of this article is the need to promote current technologies of virtual modeling, which is considered as a tool for increasing the brightness of engineering lighting. Purpose. development and implementation of virtual 3D laboratories to obtain practical skills from testing real-life materials. Materials and methods. 3D graphics editor, software Unity 2022.3.4. Results. We also have an analysis of programs for 3D modeling, which can be used not only for the purpose of displaying on printed products or on the computer screen, but also for the development of entire elements. In addition to software products, the authors propose an approach to creating a virtual technological laboratory, which allows industry professionals to learn practical skills from testing real-life materials.

Development of Digital Show Methodology

The article proposes a method for creating a digital display of a clothing collection using modern information technologies. The authors analyzed technologies that can be used to display the collection in a digital environment. Particular attention is paid to 3D modeling and animation, as well as the integration of virtual models and scenes into videos. An algorithm is proposed that includes the stages of creating the image of the collection and selecting avatars, animation of the process of passing models in designed clothes across the stage in three-dimensional space. The purpose of this work is to consider the features of using neural networks for generating images, online resources and three graphic editors: a general-purpose 3D editor Blender, a 3D editor for designing and visualizing clothing Clo3D and a program for color correction, editing and video processing DaVinci Resolve. The main task was to study the possibilities of these technological solutions for creating motion design for a virtual fashion show of a clothing collection. Development algorithms are described at different stages: from searching for an artistic image using neural networks and digital design to developing animation design and video post-processing. The research materials contribute to understanding the possibilities of digital design in the field of fashion. The described tools allow you to create realistic clothing models, demonstrate animation and fabric simulation, and present collections in a dynamic format. In conclusion, recommendations are offered for the use of computer graphics for the effective development of virtual screenings. The article is intended for specialists in the field of costume and fashion design, computer graphics, virtual reality, as well as for anyone interested in innovative approaches to the organization and presentation of digital products.

SENS: Part‐Aware Sketch‐based Implicit Neural Shape Modeling

AbstractWe present SENS, a novel method for generating and editing 3D models from hand‐drawn sketches, including those of abstract nature. Our method allows users to quickly and easily sketch a shape, and then maps the sketch into the latent space of a part‐aware neural implicit shape architecture. SENS analyzes the sketch and encodes its parts into ViT patch encoding, subsequently feeding them into a transformer decoder that converts them to shape embeddings suitable for editing 3D neural implicit shapes. SENS provides intuitive sketch‐based generation and editing, and also succeeds in capturing the intent of the user's sketch to generate a variety of novel and expressive 3D shapes, even from abstract and imprecise sketches. Additionally, SENS supports refinement via part reconstruction, allowing for nuanced adjustments and artifact removal. It also offers part‐based modeling capabilities, enabling the combination of features from multiple sketches to create more complex and customized 3D shapes. We demonstrate the effectiveness of our model compared to the state‐of‐the‐art using objective metric evaluation criteria and a user study, both indicating strong performance on sketches with a medium level of abstraction. Furthermore, we showcase our method's intuitive sketch‐based shape editing capabilities, and validate it through a usability study.

Classification, registration and segmentation of ear canal impressions using convolutional neural networks

Today, fitting bespoke hearing aids involves injecting silicone into patients’ ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to create automation tools to assist the hearing aid design, manufacturing and fitting processes as well as normalizing anatomical data to assist the study of the outer ear canal’s morphology. The methods include classifying the handedness of the impression into left and right ear types, orienting the geometries onto the same coordinate system sense, and removing extraneous artifacts introduced by the silicone mold. We investigate the use of convolutional neural networks for performing these semantic tasks and evaluate their accuracy using a dataset of 3000 ear canal impressions. The neural networks proved highly effective at performing these tasks with 95.8% adjusted accuracy in classification, 92.3% within 20° angular error in registration and 93.4% intersection over union in segmentation.

Learning an Interpretable Stylized Subspace for 3D-aware Animatable Artforms.

Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the interpretability and 3D controllability of the latent space in a 3D-aware GAN to a stylized sub-space of a customized GAN, revitalizing the original artforms. To this end, the proposed two-stage optimization framework of 3DArtmator begins with discovering an anchor in the original latent space that accurately mimics the pose and content of a given art painting. This anchor serves as a reliable indicator of the original latent space local structure, therefore sharing the same editable predefined expression vectors. In the second stage, we train a customized 3D-aware GAN specific to the input artform, while enforcing the preservation of the original latent local structure through a meticulous style-directional difference loss. This approach ensures the creation of a stylized sub-space that remains interpretable and retains 3D control. The effectiveness and versatility of 3DArtmator are validated through extensive experiments across a diverse range of art styles. With the ability to generate 3D reconstruction and editing for artforms while maintaining interpretability, 3DArtmator opens up new possibilities for artistic exploration and engagement.

SPECIAL EFFECTS USED IN CREATING 3D ANIMATED SCENES PART II

Today, the computers have made special effects so real, very hard to distinguish from the real elements on the screen, that we can barely notice them. The 3D animation technology development mirrors the progress of technology in the 20th century from mechanization and industrial to computerization and virtual. While the special 3D animation from today could not exist without computers, people are still the ones who conceive these effects. There are many opinions according to which 3D animation will have a much greater impact than it has had up to now. Filming animated 3D scenes offers various opportunities for achieving direct camera shooting. 3D Graphics is a lot more complex, it works with textures, volumes, light schemes and everything is being realized with the help of the computer and of the intelligent mind of the 3D graphics editor. Also, the computer-aided design, the integrated machine-tool systems with automatic control, industrial robots, etc., are some of the current mutations of industrial production that strongly influence product design. The 3D programs are spectacular; thanks to the developments from the hard and software technology, lighting and dynamic renderings specific to 3D scenes can be integrated more and more easily even in complex scenes. The viewer will not even notice that some of the nature landscapes will be generated by the 3D graphic editor. The aim of this article is to briefly present the geometric modelling of some component elements of the proposed 3D scene. Like any other developed product, it has to replace one that already exists, and for it to be accomplished an essential criterion is to introduce a new user facility or to improve the technical performance of the predecessor. Competition also led to diversity, the producers had to capture unexplored market sectors and bring improvements specific to a certain job. The aesthetic valences are ensured by the use of high-quality plastic, metal and ceramic materials and finishing, but also by an arrangement of different primary or combined geometric shapes that confer the status of a “decor” object, when we look at the design of new products as an art.

Interactive 3D Annotation of Objects in Moving Videos from Sparse Multi-view Frames

Segmenting and determining the 3D bounding boxes of objects of interest in RGB videos is an important task for a variety of applications such as augmented reality, navigation, and robotics. Supervised machine learning techniques are commonly used for this, but they need training datasets: sets of images with associated 3D bounding boxes manually defined by human annotators using a labelling tool. However, precisely placing 3D bounding boxes can be difficult using conventional 3D manipulation tools on a 2D interface. To alleviate that burden, we propose a novel technique with which 3D bounding boxes can be created by simply drawing 2D bounding rectangles on multiple frames of a video sequence showing the object from different angles. The method uses reconstructed dense 3D point clouds from the video and computes tightly fitting 3D bounding boxes of desired objects selected by back-projecting the 2D rectangles. We show concrete application scenarios of our interface, including training dataset creation and editing 3D spaces and videos. An evaluation comparing our technique with a conventional 3D annotation tool shows that our method results in higher accuracy. We also confirm that the bounding boxes created with our interface have a lower variance, likely yielding more consistent labels and datasets.

Anatomically Realistic 3D Printed Mouse Phantom for Multi-Institutional Benchmarking of FLASH and CONV Irradiation

It is reported that about US$28B/year is spent on pre-clinical studies that are not reproducible. FLASH studies may suffer from the same reproducibility crisis due to the non-standard nature of the FLASH beamlines and the lack of dosimeters that can function at ultra-high dose-rates. There have been reports of different outcomes with regard to the FLASH effect across different institutions, even though similar beamlines, temporal structure, and nominal dose levels were used. This brings up the question of the accuracy of dosimetry under FLASH conditions for a fair comparison between FLASH and CONV. To answer this question, we develop and characterize an anatomically realistic 3D-printed mouse phantom to be used in a multi-institutional dosimetric benchmarking effort. Mesh files for bony anatomy, lungs, and soft tissue derived from a CT scan of a mouse were converted to an editable 3D model. The 3D model was cut along the coronal plane and modified to allow the inclusion of radiographic film. A multi-material approach was employed to print the phantom. A dual-nozzle 3D printer was used, where one of the nozzles used Acrylonitrile butadiene styrene (ABS) to mimic soft tissue and the other nozzle used Polyactic acid (PLA) to mimic bone density. The two materials were used together in a single print. Lungs were approximated by lightweight PLA and were printed separately and inserted into corresponding cavities in the phantom. Hounsfield Units (HU) and print-to-print stability were verified. Radiographic films were laser cut for different anatomical sites. Two institutes took part in this study with data pending from 3 more institutions. The institutes were instructed to deliver 10 Gy to the plane of the film for the whole abdomen, whole lung, and brain irradiations. 2D dose maps were compared between FLASH and CONV, and the deviation from the prescribed dose was also measured. The 3D-printed soft tissue, bone, and lung densities were measured to be ∼ 1.01 g/cc, 1.22 g/cc, and 0.44 g/cc, respectively. For soft tissue and bone, the Hounsfield unit (HU) difference from one print to another was < 10 HU. The greatest variation was within the lungs (54 HU), but this had a minimal effect on the dose distribution (<1%). For the two institutions that completed the survey, the maximum average difference between FLASH and CONV for all irradiations was 0.75 Gy (7.48%). The maximum average difference from the prescribed dose for all irradiations was 0.7 Gy (7.20%) across both institutions. The largest discrepancy was generally observed to be for lung irradiation, indicating that lack of treatment planning systems limits our ability to prescribe accurately in areas of inhomogeneities. A 3D printed anatomically realistic mouse phantom was developed, characterized, and used in a multi-institutional dosimetric benchmarking effort. Such a study is paramount for the clinical translation of FLASH as it facilitates reduced variability from one institution to another.

Application of optical scanning systems and 3D modeling algorithms as a method to control the depth of teeth preparation for fixed orthopedic structures

Background. In orthopedic dentistry at the present stage of its development, a chemical technique for preparing teeth for fixed structures of dentures has been developed; however, the state of the pulp in response to preparation is not taken into account. As a result, medical tactics for preserving or extirpating the pulp of abutment teeth, depending on the clinical case, are unreasonable. Given the generally accepted rules for preparing teeth for fixed orthopedic structures, there is a significant percentage of complications. Controlling the depth of tooth preparation makes it possible to carry out this manipulation as rationally as possible, and as a result to ensure the effectiveness of procedure, based on the state of vitality of the prepared teeth. The aim of the study is to determine and control the depth of the studied teeth preparation using digital volumetric scanning technology and algorithms of a digital graphic 3D editor in the CAD/CAM system. Materials and methods. The article presents the results of using digital volumetric scanning of the CAD/CAM system and an integrated algorithm for digital 3D modeling to determine and control the depth of teeth preparation for fixed orthopedic structures. Results. Based on the results of digital volumetric scanning, the working thickness of the tooth preparation in the area of the tooth neck was determined with the formation of various types of chamfer in different study groups. In order to determine the depth of the preparation of hard dental tissues, an artificial crown was modeled in the CAD system — digital volumetric scanner NeWay (Open Technologies) and data on its thickness in the area of the ledge were obtained. Conclusions. Method of digital volumetric scanning to determine and control the depth of preparation of hard dental tissues makes it possible to prevent the development of irreversible morpho-functional changes in the tissues of teeth caused by odontoreparation.