3D View Research Articles

TVNeRF: Improving few-view neural volume rendering with total variation maximization

Neural Radiance Fields (NeRF) has profoundly impacted few-shot novel view synthesis and 3D reconstruction with its innovative rendering techniques and high-quality output. However, synthesizing novel views from sparse inputs remains challenging for NeRF. With limited view inputs, NeRF tends to overfit the available views, leading to reconstruction discrepancies and artifacts. To address this issue, we propose TVNeRF, which introduces two complementary regularization methods. By maximizing the total variation of unseen rays, we control the density distribution of ray sampled points to prevent excessive sampling in non-critical areas, thereby reducing artifacts and floaters. Concurrently, minimizing the accumulated opacity of unseen rays allows for the effective selection of important rays and sampled points without additional computational overhead, while also mitigating potential over-regularization effects. These combined strategies not only prevent overfitting but also achieve effects similar to the Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Moreover, TVNeRF achieves these improvements without the need for pre-trained models or prior knowledge. By utilizing these straightforward yet effective regularization techniques, TVNeRF can deliver results comparable to state-of-the-art models while maintaining computational efficiency. This approach highlights that substantial improvements in NeRF performance can be realized through targeted regularization methods, effectively addressing the challenges posed by sparse view synthesis.

New 3-D Technologies in Salivary Gland Surgery: The Exoscope-Assisted Surgery for Treatment of Benign Tumors of Parotid Gland.

New technologies are increasingly widespread in medical practice. Particularly, the 3D view is considered among the most useful innovations for surgery. It allows the operator to reconstruct the patient's anatomy in his own mind, going beyond his personal imagination. In the last few years, a new facility has been experienced, it's the Exoscopy. Exoscopy is a magnified vision system, similar to Microscopy, but which also allows a tridimensional vision of the surgical anatomy. Despite Exoscopy having been used for years in Neurosurgery, it has been just rarely described in parotid surgery. We intend to report our experience with Exoscope Aesculap AEOS used to remove benign tumors of the parotid gland. We treated 14 patients with benign tumors of the parotid gland, since September 2023 to November 2023. Each surgery was conducted by the same expert surgeon which also reported his experience about intra-operative complications (as bleeding) in comparison to the traditional procedure without Exoscope. We evaluated the learning curve of Exoscope-Assisted Parotid Surgery comparing, among them, the operative times of the same procedures performed in chronological order. Each patient underwent the same follow-up which included three checks at one month, three months and six months. The follow-up was especially about the evaluation of palsy of the VII C.N. which was assessed through House-Brackmann score (H-B score). The results of our experience reports that the Exoscope is a useful tool for parotid gland surgery. It allows an excellent visualization of the facial nerve main trunk and its branches. Although the first procedures presented longer times in comparison to traditional surgery, the progressive reduction of the operative times demonstrates that the learning curve of Exoscopy is very fast. Certainly, more experience is required for the full introduction of Exoscopy in surgery practice of parotid gland but, now, its potentialities are highly exciting.

Validity and accuracy of automatic cobb angle measurement on 3D spinal ultrasonographs for children with adolescent idiopathic scoliosis: SOSORT 2024 award winner.

Ultrasonography for scoliosis is a novel imaging method that does not expose children with adolescent idiopathic scoliosis (AIS) to radiation. A single ultrasound scan provides 3D spinal views directly. However, measuring ultrasonograph parameters is challenging, time-consuming, and requires considerable training. This study aimed to validate a machine learning method to measure the coronal curve angle on ultrasonographs automatically. A total of 144 3D spinal ultrasonographs were extracted to train and validate a machine learning model. Among the 144 images, 70 were used for training, and 74 consisted of 144 curves for testing. Automatic coronal curve angle measurements were validated by comparing them with manual measurements performed by an experienced rater. The inter-method intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM), and percentage of measurements within clinical acceptance (≤ 5°) were analyzed. The automatic method detected 125/144 manually measured curves. The averages of the 125 manual and automatic coronal curve angle measurements were 22.4 ± 8.0° and 22.9 ± 8.7°, respectively. Good reliability was achieved with ICC2,1 = 0.81 and SEM = 1.4°. A total of 75% (94/125) of the measurements were within clinical acceptance. The average measurement time per ultrasonograph was 36 ± 7s. Additionally, the algorithm displayed the predicted centers of laminae to illustrate the measurement. The automatic algorithm measured the coronal curve angle with moderate accuracy but good reliability. The algorithm's quick measurement time and interpretability can make ultrasound a more accessible imaging method for children with AIS. However, further improvements are needed to bring the method to clinical use.

Real-time representation and rendering of high-resolution 3D light field based on texture-enhanced optical flow prediction.

Three-dimensional (3D) light field displays can provide an immersive visual perception and have attracted widespread attention, especially in 3D light field communications, where 3D light field displays can provide face-to-face communication experiences. However, due to limitations in 3D reconstruction and dense views rendering efficiency, generating high-quality 3D light field content in real-time remains a challenge. Traditional 3D light field capturing and reconstruction methods suffer from high reconstruction complexity and low rendering efficiency. Here, a Real-time optical flow representation for the high-resolution light field is proposed. Based on the principle of 3D light field display, we use optical flow to ray trace and multiplex sparse view pixels. We simultaneously synthesize 3D light field images during the real-time interpolation process of views. In addition, we built a complete capturing-display system to verify the effectiveness of our method. The experiments' results show that the proposed method can synthesize 8 K 3D light field videos containing 100 views in real-time. The PSNR of the virtual views is around 32 dB and SSIM is over 0.99, and the rendered frame rate is 32 fps. Qualitative experimental results show that this method can be used for high-resolution 3D light field communication.

The Value of SINO Robot and Angio Render Technology for Stereoelectroencephalography Electrode Implantation in Drug-Resistant Epilepsy.

Stereoelectroencephalography (SEEG) electrodes are implanted using a variety of stereotactic technologies to treat refractory epilepsy. The value of the SINO robot for SEEG electrode implantation is not yet defined. The aim of the current study was to assess the value of the SINO robot in conjunction with Angio Render technology for SEEG electrode implantation and to assess its efficacy. Between June 2018 and October 2020, 58 patients underwent SEEG electrode implantation to resect or ablate their epileptogenic zone (EZ). The SINO robot and the Angio Render technology was used to guide the electrodes and visualize the individual vasculature in a three-dimensional (3D) fashion. The 3D view functionality was used to increase the safety and accuracy of the electrode implantation, and for reducing the risk of hemorrhage by avoiding blood vessels. In this study, 634 SEEG electrodes were implanted in 58 patients, with a mean of 10.92 (range: 5-18) leads per patient. The mean entry point localization error (EPLE) was 0.94 ± 0.23 mm (range: 0.39-1.63 mm), and the mean target point localization error (TPLE) was 1.49 ± 0.37 mm (range: 0.80-2.78 mm). The mean operating time per lead (MOTPL) was 6. 18 ± 1.80 minutes (range: 3.02-14.61 minutes). The mean depth of electrodes was 56.96 ± 3.62 mm (range: 27.23-124.85 mm). At a follow-up of at least 1 year, in total, 81.57% (47/58) patients achieved an Engel class I seizure freedom. There were two patients with asymptomatic intracerebral hematomas following SEEG electrode placement, with no late complications or mortality in this cohort. The SINO robot in conjunction with Angio Render technology-in SEEG electrode implantation is safe and accurate in mitigating the risk of intracranial hemorrhage in patients suffering from drug-resistant epilepsy.

Poster 246: Radiographic Landmarks for Femoral Tunnel Positioning During MPFC Reconstruction Vary With Trochlear Dysplasia

Objectives: Accurate femoral tunnel positioning during medial patellofemoral complex (MPFC) reconstruction has been shown to be critical in optimizing outcomes and avoiding complications. Multiple anatomic and radiographic studies have described methods to accurately identify the appropriate femoral attachment of the MPFC, commonly referencing the adductor tubercle and medial epicondyle as landmarks. However, these studies have been performed in normal knees, and the relevance to knees with patellar instability and anatomic risk factors has not been described. Therefore, the purpose of this study was to compare the radiographic positions of commonly utilized landmarks between symptomatic and normal knees. Methods: Three-dimensional (3D) models were created from computed tomography scans of knees with patellar instability and compared to age- and sex-matched control knees. On the 3D models, the locations of the adductor tubercle (AT), medial epicondyle (ME) and gastrocnemius tubercle (GT) were marked. A 2-dimensional perfect lateral view was then created from these models to simulate radiographs, and the radiographic location of each landmark was described with respect to the following: 1) anteroposterior relation to the posterior cortical line and 2) proximal-distal relationship to the posterior condylar line. The position of each landmark was compared between symptomatic and control groups, and linear regression analysis was performed to assess variations in position with severity of anatomic risk factors including trochlear dysplasia, tuberosity lateralization and patella alta. Results: Forty knees were included in this study, including 20 symptomatic and 20 control knees (8 men and 12 women in each group, age 21.5+/-6.9 years old vs 21.3+/-7.0 years old). On the 2D (radiographic) views, the ME landmarks in the symptomatic group were found to be significantly more posterior (6.9+/-3.7mm vs 9.5+/-4.2 mm, p = 0.031) and significantly more distal (9.2+/-4.4 vs 12.1+/-5.6, p = 0.047) than in the control group. For the AT, no significant differences were noted in the anteroposterior (0.7+/-2.4 vs 1.3+/-2.6 mm anterior to the posterior cortex, p = 0.238) or proximal-distal (4.9+/-2.8 vs 5.4+/-2.3mm proximal to the posterior condylar line, p = 0.259) positions. Similarly, no differences for the GT were found (4.4+/-3.4 vs 2.8+/-3.7 mm posterior to the posterior cortex, p = 0.100; and 5.1+/-4.6 vs 4.0+/-3.2 mm distal to the posterior condylar line, p = 0.175). An association between severity of trochlear dysplasia and posterior position of the AT was identified (R = 0.43, R2 = 0.18, p = 0.058). Conclusions: Although intraoperative fluoroscopy is commonly utilized to identify femoral tunnel positioning during MPFC reconstruction, this study demonstrates that commonly utilized anatomic reference points such as the medial epicondyle in symptomatic knees are more posterior and more distal on perfect lateral radiographs when compared to normal knees. Furthermore, the severity of trochlear dysplasia appears to influence radiographic AT position. These findings should be taken into consideration when utilizing fluoroscopy during MPFC reconstruction. Further studies are needed to identify optimal femoral tunnel positioning for knees with anatomic risk factors when performing surgical treatment for patellar instability.

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit.

With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file.

Comparing the three-dimensional morphological asymmetries in the ejecta of Kepler and Tycho in X-rays

Aims. Recent simulations have shown that asymmetries in the ejecta distribution of supernova remnants (SNRs) may be a reflection of asymmetries left over from the initial supernova explosion. Thus, SNR studies provide a vital means for testing and constraining model predictions in relation to the distribution of heavy elements, which are key to improving our understanding of the explosion mechanisms in Type Ia supernovae. Methods. The use of a novel blind source separation method applied to the megasecond X-ray observations of the historic Kepler and Tycho supernova remnants has revealed maps of the ejecta distribution. These maps are endowed with an unprecedented level of detail and clear separations from the continuum emission. Our method also provides a three-dimensional (3D) view of the ejecta by individually disentangling red- and blueshifted spectral components associated with images of the Si, S, Ar, Ca, and Fe emission. This approach provides insights into the morphology of the ejecta distribution in those two remnants. Results. Those mappings have allowed us to thoroughly investigate the asymmetries in the intermediate-mass elements and Fe distribution in two Type Ia supernova remnants. We also compared the results with the core-collapse Cassiopeia A remnant, which we had studied previously. The images obtained confirm, as expected for Type Ia SNRs, that the Fe distribution is mostly closer to the core than that of intermediate-mass elements. They also highlight peculiar features in the ejecta distribution, such as the Fe-rich southeastern knot in Tycho.

Enhanced multi view 3D reconstruction with improved MVSNet

Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.

From Nature to UAV - A Study on Collision Avoidance in Bee Congregation

Insects engage in a variety of survival-related activities, including feeding, mating, and communication, which are frequently motivated by innate impulses and environmental signals. Social insects, such as ants and bees, exhibit complex collective behaviors. They carry out well-organized duties, including defense, nursing, and foraging, inside their colonies. For analyzing the behavior of any living entity, we selected honeybees (Apis Mellifera) and worked on a small portion of it. We have captured the video of honeybees flying close to a hive (human-made artificial hive) while the entrance was temporarily sealed which resulted in the” bee cloud”. An exploration of the flight trajectories executed and a 3D view of the” bee cloud” constructed. We analyzed the behaviors of honeybees, especially on their speed and distance. The results showed that the loitering honeybees performed turns that are fully coordinated, and free of sideslips so thus they made no collision between themselves which inspired us to propose a method for avoiding collision in unmanned aerial vehicle. This paper gives the collective behavioral information and analysis report of the small portion of data set (honeybees), that bee maintains a safe distance (35mm) to avoid collision.

RCSB protein Data Bank: exploring protein 3D similarities via comprehensive structural alignments.

Tools for pairwise alignments between 3D structures of proteins are of fundamental importance for structural biology and bioinformatics, enabling visual exploration of evolutionary and functional relationships. However, the absence of a user-friendly, browser-based tool for creating alignments and visualizing them at both 1D sequence and 3D structural levels makes this process unnecessarily cumbersome. We introduce a novel pairwise structure alignment tool (rcsb.org/alignment) that seamlessly integrates into the RCSB Protein Data Bank (RCSB PDB) research-focused RCSB.org web portal. Our tool and its underlying application programming interface (alignment.rcsb.org) empowers users to align several protein chains with a reference structure by providing access to established alignment algorithms (FATCAT, CE, TM-align, or Smith-Waterman 3D). The user-friendly interface simplifies parameter setup and input selection. Within seconds, our tool enables visualization of results in both sequence (1D) and structural (3D) perspectives through the RCSB PDB RCSB.org Sequence Annotations viewer and Mol* 3D viewer, respectively. Users can effortlessly compare structures deposited in the PDB archive alongside more than a million incorporated Computed Structure Models coming from the ModelArchive and AlphaFold DB. Moreover, this tool can be used to align custom structure data by providing a link/URL or uploading atomic coordinate files directly. Importantly, alignment results can be bookmarked and shared with collaborators. By bridging the gap between 1D sequence and 3D structures of proteins, our tool facilitates deeper understanding of complex evolutionary relationships among proteins through comprehensive sequence and structural analyses. The alignment tool is part of the RCSB PDB research-focused RCSB.org web portal and available at rcsb.org/alignment. Programmatic access is available via alignment.rcsb.org. Frontend code has been published at github.com/rcsb/rcsb-pecos-app. Visualization is powered by the open-source Mol* viewer (github.com/molstar/molstar and github.com/molstar/rcsb-molstar) plus the Sequence Annotations in 3D Viewer (github.com/rcsb/rcsb-saguaro-3d).

Neural Radiance Selector: Find the best 2D representations of 3D data for CLIP based 3D tasks

Representing the world in 3D space provides vivid texture and depth information. However, 3D datasets currently do not match the scale of 2D datasets. There is a growing trend in representing 3D data as multi-view 2D images and using large-scale 2D models, to solve 3D tasks. In this work, we present the Neural Radiance Selector, a method that automatically selects the optimal 2D representations of 3D data. Instead of indiscriminately sampling multi-view 2D images, we define the optimal 2D views as those capable of reconstructing the entire 3D scene with a conditional neural radiance field. We propose two distinct methods for 3D point cloud data and 3D implicit models to achieve faster inference. We demonstrate the efficacy of our methods in various 3D tasks, including zero-shot 3D point cloud classification, 3D implicit model classification, and language-guided NeRF editing.

ChARpack: The Chemistry Augmented Reality Package.

Off-loading visualization and interaction into virtual reality (VR) using head-mounted displays (HMDs) has gained considerable popularity in simulation sciences, particularly in chemical modeling. Because of its unique way of soft immersion, augmented reality (AR) HMD technology has even more potential to be integrated into the everyday workflow of computational chemists. In this work, we present our environment to explore the prospects of AR in chemistry and general molecular sciences: The chemistry in Augmented Reality package (chARpack). Besides providing an extensible framework, our software focuses on a seamless transition between a 3D stereoscopic view with true 3D interactions and the traditional desktop PC setup to provide users with the best setup for all tasks in their workflow. Using feedback from domain experts, we discuss our design requirements for this kind of hybrid working environment (AR + PC), regarding input, features, degree of immersion, and collaboration.

An element perception and state recognition method oriented to complex assembled product

Quality inspection during assembly process has an important impact on the performance and service life of the final product. Automated inspection solutions based on machine vision and artificial intelligence greatly improve the efficiency of inspection. However, the current mainstream large-scene inspection solutions, such as mobile vision platforms, are difficult to apply widely due to cost issues. Therefore, portable vision inspection solutions have gradually gained attention. However, the bottleneck technologies that limit this solution are full-element perception and occlusion judgment under free viewing angle, robust object matching method. To address the above problems, This paper proposes a free-viewpoint-based element perception and state recognition scheme. An improved structure estimation model is adopted to establish a bridge between virtual and actual mapping under the support of small-sample data, and all elements are located and occlusion analyzed by a 3D model-constrained view evolution algorithm. In addition, a hierarchy matching and optimization method is established to complete visual analysis. Experimental results show that this scheme performs well in structure estimation, element localization, occlusion judgment, state recognition, and work efficiency, demonstrating high application value.

Automatic 3D left atrial strain extraction framework on cardiac computed tomography

Background and objectiveStrain analysis provides insights into myocardial function and cardiac condition evaluation. However, the anatomical characteristics of left atrium (LA) inherently limit LA strain analysis when using echocardiography. Cardiac computed tomography (CT) with its superior spatial resolution, has become critical for in-depth evaluation of LA function. Recent studies have explored the feasibility of CT-derived strain; however, they relied on manually selected regions of interest (ROIs) and mainly focused on left ventricle (LV). This study aimed to propose a first-of-its-kind fully automatic deep learning (DL)-based framework for three-dimensional (3D) LA strain extraction on cardiac CT. MethodsA total of 111 patients undergoing ECG-gated contrast-enhanced CT for evaluating subclinical atrial fibrillation (AF) were enrolled in this study. We developed a 3D strain extraction framework on cardiac CT images, containing a 2.5D GN-U-Net network for LA segmentation, axis-oriented 3D view extraction, and LA strain measure. The segmentation accuracy was evaluated using Dice similarity coefficient (DSC). The model-extracted LA volumes and emptying fraction (EF) were compared with ground-truth measurements using intraclass correlation coefficient (ICC), correlation coefficient (r), and Bland-Altman plot (B-A). The automatically extracted LA strains were evaluated against the LA strains measured from 2D echocardiograms. We utilized this framework to gauge the effect of AF burden on LA strain, employing the atrial high rate episode (AHRE) burden as the measurement parameter. ResultsThe GN-U-Net LA segmentation network achieved a DSC score of 0.9603 on the test set. The framework-extracted LA estimates demonstrated excellent ICCs of 0.949 (95 % CI: 0.93–0.97) for minimal LA volume, 0.904 (95 % CI: 0.86–0.93) for maximal LA volume, and 0.902 (95 % CI: 0.86–0.93) for EF, compared with expert measurements. The framework-extracted LA strains demonstrated moderate agreement with the LA strains based on 2D echocardiography (ICCs >0.703). Patients with AHRE > 6 min had significantly lower global strain and LAEF, as extracted by the framework than those with AHRE ≤ 6 min. ConclusionThe promising results highlighted the feasibility and clinical usefulness of automatically extracting 3D LA strain from CT images using a DL-based framework. This tool could provide a 3D-based alternative to echocardiography for assessing LA function.

3D colored object reconstruction from a single view image through diffusion

In this paper we propose a novel 3D colored object reconstruction method from a single view image. Given a reference image, a conditional diffusion probabilistic model is built to reconstruct both a 3D point cloud shape and the corresponding color features at each point, and then images from arbitrary views can be synthesized using a volume rendering technique. The approach involves several sequential steps. First, the reference RGB image is encoded into separate shape and color latent variables. Then, a shape prediction module predicts reverse geometric noise based on the shape latent variable within the diffusion model. Next, a color prediction module predicts color features for each 3D point using information from the color latent variable. Finally, a volume rendering module transforms the generated colored point cloud into 2D image space, facilitating training based solely on a reference image. Experimental results demonstrate that the proposed method achieves competitive performance on colored 3D shape reconstruction and novel view image synthesis.

Multi-scale hydrostructural approach for karst environment. Application to the Arcier hydrosystem (eastern France)

Based on a multi-scale and hydrostructural approach, this study presents the most relevant methodology to be applied to a karst hydrosystem in order to get a full understanding of underground water flow. It implies a complete structural analysis, from the hydrosystem scale to the outcrop scale, including the intermediate scale of the major geological structures. We illustrate the method in the Arcier hydrosystem, in the northwestern border of the Jura fold-and-thrust belt (Eastern France).Field mapping and structural analysis allow to update the geological vision of the hydrosystem with two kink-type fault propagation folds, including a trishear kinematic model, on either side of a plateau presenting a hollow-and-dome configuration. Fracturing analysis reveals a fault-fracture network that we infer governs the entire hydrosystem. A Riedel pattern is highlighted, characterized by a N–S-striking (N355° ± 5), sinistral strike-slip, regional shear zone. Then, two 3D geological models, at different scales, constructed with MOVE and Visual Karsys softwares are combined with water levels and artificial tracer tests. It reveals a multilayer aquifer and a redefinition of groundwater circulations for the Arcier hydrosystem.The results demonstrate a strong geological control of karstic hydrosystems on groundwater circulations, proving that classical hydrogeological methods, such as natural and/or artificial tracers, must be combined with rigorous geological analysis. Moreover, the multi-scale approach provides an explanation of groundwater circulation based on the intersection between 3D geometry of impervious layers delimiting the aquifers and their base water level, instead of the 2D view (section or map) requiring systematic recourse to inferred vertical faults to cross permeability barriers vertically or laterally. This study also brings a new vision to the local protection of the water resource.

Primacy of vision shapes behavioral strategies and neural substrates of spatial navigation in marmoset hippocampus

The role of the hippocampus in spatial navigation has been primarily studied in nocturnal mammals, such as rats, that lack many adaptations for daylight vision. Here we demonstrate that during 3D navigation, the common marmoset, a new world primate adapted to daylight, predominantly uses rapid head-gaze shifts for visual exploration while remaining stationary. During active locomotion marmosets stabilize the head, in contrast to rats that use low-velocity head movements to scan the environment as they locomote. Pyramidal neurons in the marmoset hippocampus CA3/CA1 regions predominantly show mixed selectivity for 3D spatial view, head direction, and place. Exclusive place selectivity is scarce. Inhibitory interneurons are predominantly mixed selective for angular head velocity and translation speed. Finally, we found theta phase resetting of local field potential oscillations triggered by head-gaze shifts. Our findings indicate that marmosets adapted to their daylight ecological niche by modifying exploration/navigation strategies and their corresponding hippocampal specializations.

Architectural distortion on digital breast tomosynthesis mammograms in symptomatic breast clinics: what are the result outcomes?

In April 2020, standard two-dimensional (2D) full-field digital mammograms were replaced with digital breast tomosynthesis (DBT) and synthesised 2D views for symptomatic breast clinics. This study aimed to evaluate the positive predictive value (PPV) for malignancy in DBT-detected Architectural distortion (AD). All mammogram reports with the word "distortion" were assessed between April 2020 and October 2022. There were 458 mammograms with the word "distortion." After excluding mammograms with no distortion (n = 128), post-surgical distortion (n = 173), distortion with mass (n = 33), and unchanged distortion (n = 14), there were 111 patients with pure distortion. Correlation with histopathology was obtained where possible. All patients were followed for a minimum of 2 years. Forty-two out of 111 patients (37.84%) with AD had a normal ultrasound (US) and were discharged. Fifty-five (49.5%) patients had sonographic correlation corresponding to the distortion, leading to US-guided biopsy. Thirteen (23.6%) had tomosynthesis-guided biopsy, and one had a skin biopsy. The PPV for malignancy was 42.34%. Malignancy diagnoses were higher with US-guided biopsies than tomosynthesis-guided biopsies, 78.1% and 30%, respectively. With a total malignancy rate of 42.34%, DBT-detected AD has a high enough PPV for malignancy to justify selective tissue sampling if a sonographic correlate is present or with suspicious mammograms. The chances of malignancy are higher when a sonographic correlate corresponding to AD is present. AD on DBT/synthesized mammograms views in symptomatic breast clinic patients justifies selective sampling.

Real-time volume rendering with octree-based implicit surface representation

Recent breakthroughs in neural radiance fields have significantly advanced the field of novel view synthesis and 3D reconstruction from multi-view images. However, the prevalent neural volume rendering techniques often suffer from long rendering time and require extensive network training. To address these limitations, recent initiatives have explored explicit voxel representations of scenes to expedite training. Yet, they often fall short in delivering accurate geometric reconstructions due to a lack of effective 3D representation. In this paper, we propose an octree-based approach for the reconstruction of implicit surfaces from multi-view images. Leveraging an explicit, network-free data structure, our method substantially increases rendering speed, achieving real-time performance. Moreover, our reconstruction technique yields surfaces with quality comparable to state-of-the-art network-based learning methods. The source code and data can be downloaded from https://github.com/LaoChui999/Octree-VolSDF.