Volumetric Imaging Research Articles

Rectangular sparse array with equal transmit-receive element counts for 3D ultrasound imaging.

Generating three-dimensional (3D) ultrasound (US) data from conventional two-dimensional (2D) freehand acquisitions typically necessitates external tracking systems to ascertain probe positioning. However, these hardware-based solutions often present practical challenges, including substantial cost, increased setup complexity, and susceptibility to interference or line-of-sight issues, thereby limiting their widespread clinical integration. A tracker-free approach to 3D US reconstruction could dramatically improve the accessibility and applicability of volumetric ultrasound in standard medicalprocedures. This study presents a method for 3D US reconstruction from 2D B-mode image sequences that eliminates the need for external tracking. The goal is to improve reconstruction accuracy and usability for diagnostic, preoperative, and intraoperativeapplications. A hybrid Transformer-convolutional neural network (CNN) is trained end-to-end to regress inter-frame six degrees of freedom (6-DoF) poses. Global self-attention captures long-range probe motion, whereas convolutional layers refine local speckle patterns. Experiments used 53 forearm scans (Dataset 1) and 36 prostate scans (Dataset 2). Reconstruction performance was assessed with Dice Similarity Coefficient (DSC) and trajectory drift (Dataset 1) plus structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) (Dataset 2). In Dataset 1, the proposed model achieved a DSC of 0.71 0.19 and a drift of 13.25 9.17 , outperforming the 2D CNN (0.62 0.26, 18.92 11.20 ) and ConvLSTM (0.67 0.32, 18.36 5.44 ). In Dataset 2, it obtained an SSIM of 0.845 and a PSNR of 29.10 dB, exceeding the 2-D CNN (0.742, 26.40 dB) and ConvLSTM (0.812, 27.85 dB). All improvements were statistically significant as determined by paired t-tests (p 0.01). The tracker-free Transformer-CNN consistently improves volumetric overlap, trajectory stability, and image quality relative to established CNN- or recurrent neural network (RNN)-based schemes, demonstrating a practical, cost-effective route to high-fidelity 3D ultrasound across diverse clinical protocols.

Evaluation of brain parenchyma in paediatric patients diagnosed with poorly controlled type 1 diabetes using volumetric magnetic resonance imaging (MRI).

Molecularly targeted photoacoustic endoscopy with fiber-scanning side-view probe for in vivo staging of early mucosal tumors.

Charting the landscape of cytoskeletal diversity in microbial eukaryotes.

Dental pulp responses to dental decay, the most prevalent chronic disease worldwide, involve remodeling processes comparable to those observed in other human diseases. By combining volumetric imaging and single-cell analysis at various stages of the disease in human samples, the natural history of how dental pulp responds to decay is uncovered. During the early phases, an arterialization of capillary networks and a progressive outward remodeling of larger vessels are observed. Additionally, neurogenesis of nerve endings and the reprogramming of perivascular progenitor cells into fibroblasts, initiating the physiological reparative response of the stromal tissue, is identified. Vascular and nerve regression, along with a shift in immune response and dental pulp fibrosis, contribute to irreversible pulpitis. These findings establish a foundation for a more comprehensive understanding of how dental tissues respond to injury, thereby prompting a paradigm shift in patient management strategies. Furthermore, this study underscores the potential of the human tooth as a valuable model for investigating other systemic diseases and evaluating treatment responses.

Diatom ultrastructural diversity across controlled and natural environments.

Inverse Synthetic Aperture Radar (ISAR) has recently advanced to volumetric 3D-ISAR imaging, creating new opportunities and challenges for automatic target recognition (ATR). This work proposes a spatiotemporal deep learning framework that jointly learns target structure and motion dynamics from high-resolution 3D-ISAR sequences. A CNN backbone (ResNet) extracts per-frame spatial features, which are fed to temporal models Bidirectional LSTM and/or ConvLSTM to capture micro-Doppler cues and aspect-dependent scattering over time; the pipeline is supported by physics-aware formation and backprojection-style 3D reconstruction. We evaluate on a four-class dataset (aircraft, helicopter, drone, tank) comprising 400 labeled samples drawn from MSTAR and simulated 3D-ISAR sequences, with standard train/validation/test partitions and targeted denoising, normalization, and augmentation to enhance robustness. The proposed model achieves strong performance across metrics: an overall accuracy of 95% on the final evaluation set with near-ideal class separability (AUC ≈ 0.98–1.00), and a best accuracy of 96.7% when all preprocessing and geometric/data-level augmentations are enabled. Ablation and robustness studies show consistent gains from motion-aware temporal modeling and the preprocessing stack under low-SNR and distortion conditions, while confusion is largely confined to visually and dynamically similar aerial classes. These results demonstrate that coupling modern spatiotemporal architectures with principled ISAR signal processing yields reliable, accurate, and deployment-oriented ATR for 3D-ISAR systems.

We demonstrate a depth-resolved Swept-Source Optical Coherence Tomography (SS-OCT) system for automated poroscopy network identification in fingerprint biometrics. The system employs a 100 kHz swept-source laser (λ0 = 1060 nm, Δλ = 110 nm) to achieve ~4.5 μm axial and 13 μm lateral resolution at 1.8 mW incident power. High-density scanning (1500 A-scans/B-scan × 500 B-scans over a 3 × 3 mm region) captures volumetric enface images that precisely localize Level 3 sweat-pore microfeatures. In a study of 40 healthy volunteers (20 M, 20 F), an automated ImageJ pipeline extracted pore morphology with sub-pixel accuracy. Two-way ANOVA attributed 60.10% of total variance to pore shape (p < 0.0001) and 14.62% to gender (p < 0.0001). The average pore count was significantly higher in males (20.53 ± 2.14) compared to females (14.00 ± 1.76, p < 0.0001). These results validate SS-OCT-based poroscopy as a robust, depth-resolved alternative for forensic and biometric identification.

Abstract The 3D scale invariant feature transform (SIFT) algorithm is an effective approach for estimating the three-dimensional displacement field. However, although the current 3D SIFT algorithm can extract many feature points, the quantity of successfully matched feature points remains relatively limited because of the insufficient accuracy of the local orientations of the feature points. We propose employing a matrix singular value decomposition (SVD) method to compute the local orientations by processing the image structure tensor of the feature points instead of the conventional matrix eigenvalue decomposition approach. The proposed method is validated using computed tomography (CT) triaxial tests on granite residual soil. The results from the rotation and deformation tests of the volumetric images demonstrate that, compared with the current algorithm, the improved 3D SIFT method increases the feature point matching rate by approximately 1.90 times and 1.86 times, respectively. Furthermore, the CT triaxial test results indicate that the improved 3D SIFT method improves the feature point matching rate by approximately 1.82 times over that of the current algorithm. This work indicates that the accuracy of key directions is crucial to the matching performance of the 3D SIFT algorithm. The improved 3D SIFT algorithm shows great potential in the quantitative analysis of internal deformation.

Isotropic binary compound semiconductors have been shown to exhibit high optical nonlinearity, which renders them particularly promising for compact optoelectronic and photonic device development through microscale structuring. However, there is a paucity of research tools that provide volumetric imaging of such patterns with high spatial resolution and crystal orientation determination. In this study, the scope of polarization second harmonic generation (SHG) microscopy was expanded to encompass the imaging of single crystalline grains from the deep layers of optically isotropic polycrystalline bulk materials such as chemical vapor deposition zinc selenium (ZnSe). It was demonstrated that focal-plane-localized second harmonic generation can be achieved using a short coherence length and tight focusing, and that grain interfaces provide the majority of the nonlinear signal. The imaging of grain arrangement across a 2 mm-thickness ZnSe sample allowed us to investigate the evolution of spatial resolution with depth provided by a high numerical aperture objective. The developed theoretical approach was used to retrieve the three-dimensional (3D) orientations of individual microcrystallites inside the sample. The present study has expanded the use of the inverse pole figure mapping technique to visualize the crystallographic direction orientations of each grain on the image obtained by polarization SHG microscopy. The demonstrated 3D optical diagnostics method was shown to be a viable investigative tool for isotropic polycrystalline semiconductor materials with a face-centered cubic lattice structure, offering a high spatial resolution and frame rate.

The rapid advance of genetically encoded fluorescent functional indicators has transformed neuroscience research. Fluorescence-based optical neural recording offers excellent sensitivity and spatiotemporal resolutions. A major limitation of optical measurement is the superficial access depth due to the random light scattering in the mammalian brain. Currently, implanting miniature gradient-index (GRIN) lenses has become the preferred method for deep brain optical imaging. However, the image quality and throughput are majorly impacted by the severe optical aberration of GRIN lenses. In this work, we present an easy-to-adopt solution to overcome these challenges and improve the image quality, volume, and throughput. Specifically, we develop a correction objective lens that corrects the aberration of a GRIN lens to enable high-throughput volumetric functional imaging with a ~ 400% larger field-of-view (FOV). We demonstrate the capabilities of in vivo large-FOV 3D volumetric calcium imaging by recording over 1000 neurons in deep brain regions through a 0.5 mm diameter GRIN lens. The simplicity and robust performance of the method promise broad applications in neuroscience research.

ABSTRACT Exploration of neural activity at high spatiotemporal resolution in live animals is essential for advancing the understanding of brain function. Multiphoton microscopy has emerged over the past three decades as a powerful tool for in vivo neuroimaging, providing 3D subcellular spatial resolution and sub‐second temporal resolution. However, its imaging depth is fundamentally limited to approximately 2 mm due to light scattering, leaving most subcortical brain regions inaccessible in mammals. Gradient refractive index (GRIN) lens‐based multiphoton microendoscopy offers a minimally invasive approach that extends imaging depth up to 10 cm while maintaining 3D µm resolution. The technique, however, remains constrained by intrinsic optical aberrations of GRIN lenses, which degrade image quality and limit both the field of view and the imaging volume. Recent advances, including adaptive optics, aspheric correctors, and geometric transformation techniques, provide state‐of‐the‐art aberration correction and expand the volume of view to the cubic millimeter scale. Applications of GRIN multiphoton microendoscopy in functional neuroimaging demonstrate its potential for high‐throughput volumetric imaging with enhanced spatiotemporal resolution. These innovations enable longitudinal studies of large‐scale neural dynamics and support the development of next‐generation photonic systems for deep brain connectome mapping.

Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strategy (FAST), a deep-learning framework designed for high-speed fluorescence neural imaging, including in vivo calcium, voltage, and volumetric time-lapse imaging. FAST balances spatial and temporal redundancy across neighboring pixels, preserving structural fidelity while preventing over-smoothing of rapidly evolving fluorescence signals. Utilizing an ultra-light convolutional neural network, FAST enables real-time processing at speeds exceeding 1000 frames per second, substantially surpassing the acquisition rates of most high-speed imaging systems. We also introduce an intuitive graphical user interface that integrates FAST into standard imaging workflows, providing a real-time denoising tool for recorded neural activity and enabling downstream analysis in neuroscience research that requires millisecond-scale temporal precision, particularly in closed-loop studies.

Photoacoustic computed tomography (PACT) reveals biological structures, pharmacokinetics, and physiological functions. Although a premium PACT system with many ultrasound (US) transducers delivers high-quality volumetric imaging, it suffers from high system costs and slow temporal resolution. Here, using a limited number of US elements, a hybrid diffusion model (HD-PACT) is demonstrated that enhances dynamic multiparametric (structural, functional, and contrast-enhanced) 3D PACT. Using just 256 out of the 1024 elements in a premier hemispherical US array for PACT, HD-PACT improves structural images acquired in different planes, organisms, and wavelengths. In functional imaging, HD-PACT enables 256-element PACT to observe hypoxia, pharmacokinetics, and angiogenesis during tumor progression. Lastly, HD-PACT is transferable to low-end PACT (only 128 US elements), where it dynamically captures contrast-free/enhanced organs, oxygen-perturbed brains, and cardiac dynamics with high spatiotemporal resolution in live animals. It is believed that HD-PACT will be valuable in oncology, cardiology, pharmacology, and endocrinology.

Droplet microfluidics (DMF) generates, manipulates and processes discrete sub-microlitre droplets, which allows for precise control and high efficiency in conducting biological assays. On-chip 3D characterization of droplets and samples moving within them is challenging. Light field microscopy (LFM) based on a microlens array (MLA) has emerged as an instantaneous volumetric imaging method, with application to DMF where it can rapidly capture 3D information. However, the trade-off between spatial resolution and depth of field and challenges with reconstruction artifacts have so far limited LFM applications in microfluidics. In this work, a novel integrated system is introduced that combines a DMF device and a bifocal MLA-based LFM system. The system enables precise droplet manipulation alongside on-chip 3D imaging and tracking of particles and live cells in a volume exceeding 500 × 500 × 300 μm3 with a temporal resolution of 100 ms. The LFM has higher spatial resolution and less reconstruction artifacts compared to LFM systems based on conventional MLAs. Experiments applying microbeads and SW480 cells validate the system's capability for effective on-chip sample manipulation and 3D characterization with a best lateral resolution of approximately 1.83 μm and an axial resolution of about 6.8 μm. Additionally, the system successfully demonstrates manipulating rapid on-chip cell lysis and 3D monitoring with a temporal resolution of 300 ms over several minutes, highlighting the synergistic benefits of combining LFM with DMF.

BackgroundTo quantitatively compare 3D choroidal parameters between fellow eyes in individuals with varying degrees of myopic anisometropia and evaluate their correlations with interocular differences in spherical equivalent (△SE) and axial length (△AL).MethodsThis retrospective cross-sectional study enrolled 102 participants (204 eyes) categorized into low (△SE < 2.0D), moderate (2.0D ≤ △SE < 3.0D), and high (△SE ≥ 3.0D) anisometropia groups. Choroidal parameters, including choroidal vascular volume (CVV), choroidal stroma volume (CSV), and 3D choroidal vascularity index (3D-CVI), were measured using swept-source optical coherence tomography (SS-OCT). Retinal vessel density (VD) and regional variations were analyzed.ResultsCVV and CSV were significantly lower in more myopic eyes across all groups (P < 0.05), with the greatest reductions observed in the high anisometropia group. Nasal regions exhibited the strongest correlations between △CVV/△CSV and △SE/△AL (coefficients up to 0.403, P < 0.001). No interocular differences in 3D-CVI were detected (P > 0.05). Retinal VD in the high anisometropia group was elevated in more myopic eyes (P < 0.05), suggesting compensatory microvascular changes.ConclusionThree-dimensional choroidal metrics, particularly CVV and CSV, reflect myopic severity in anisometropia, with nasal choroid demonstrating heightened vulnerability to axial elongation. These findings highlight the potential of volumetric choroidal imaging for evaluating myopic structural changes and underscore the need for further longitudinal studies to elucidate causal mechanisms.

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes are physically limited by objective-to-specimen distance, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed a photochemical strategy for spatially precise sectioning of specimens. By combining serial photochemical sectioning with lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelin sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory bulb-wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative brain, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach.

Abstract Accurate segmentation of medical images can assist doctors in computer-aided diagnosis and clinical treatment. Due to the complexity of the object region features (e.g., size, location, and shape), it is challenging to fully extract semantic features in medical image segmentation. To address these issues, we propose a 3D multiscale progressive difference and cross-scale attention network for medical image segmentation. Specifically, we propose a dual encoder-decoder network architecture comprising a multi-scale progressive difference (MSPD) branch and a group scale-cross attention (GSCA) branch. In the MSPD branch, we introduce a progressive difference module as the basic skip connection layer to enrich more discriminative and detailed features across multiple scales and resolve scale conflicts. In the GSCA branch, a group scale-cross attention module is designed to enhance the receptive field and build long-term dependencies between voxels. By combining the features from the MSPD and GSCA branches, the hierarchical group feature aggregation (HGFA) module is designed to fuse the multi-scale global information and local spatial information. We conducted qualitative and quantitative evaluations on three publicly available datasets, including LiTS2017, 3Dircadb, and WORD. Experimental results show that our model can achieve better segmentation performance than the state-of-the-art models on these three datasets.

The forces exerted by cells upon the fibers of the extracellular matrix play a decisive role in cell motility in physiopathology. How the local physical properties of the matrix (density, stiffness, orientation) affect cellular forces remains, however, poorly understood. Existing approaches to measure cell three-dimensional (3D) traction forces within fibrous substrates lack control over the local properties and rely on continuum approaches, not suited for measuring forces at the scale of individual fibers. Herein, an approach is proposed to fabricate multilayer arrays of suspended deformable fibers spanning a wide range of fine-tunable geometrical and mechanical properties using two-photon polymerization. Atomic Force Microscopy is used to thoroughly investigate the properties of individual fibers, including Young's modulus and stiffness. This approach is combined with a reference-free method for measuring traction forces in 3D, which relies on automated segmentation of the fibers coupled with finite element modeling. The force measurement pipeline is applied to study forces exerted by endothelial cells, fibroblasts, or macrophages, and reveals how these forces are influenced by fiber density and stiffness. Additionally, coupling to fast volumetric imaging with lattice light-sheet microscopy enables the measurement of the low-intensity and short-lived tractions exerted by amoeboid cells, such as dendritic cells. Our technology will be instrumental for monitoring and studying cell behavior at the single-fiber level at extracellular matrix density interfaces, which play a crucial role in both physiological and pathological contexts, such as tumor boundaries.