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We present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam profile considerations, to image reconstruction. We outline key practical decisions in constructing a home-built system and provide insight into the alignment and calibration processes. We briefly discuss the conditions necessary for constructing a continuous 3D image and introduce our home-built code for data analysis. By providing this guide, we aim to alleviate the challenges associated with designing and constructing LSM systems and offer scientists new to LSM a valuable resource in navigating this complex field. [ABSTRACT FROM AUTHOR]

Micro-CT imaging is a powerful tool for generating high-resolution, isotropic, three-dimensional datasets of whole, centimeter-scale model organisms. At histological resolutions, micro-CT can be used for whole-animal qualitative and quantitative characterization of tissue and organismal structure in health and disease. The small size, global freshwater distribution, wide range of cell size and structures of micron scale, and common use of Daphnia magna in toxicological and environmental studies make it an ideal model for demonstrating the potential power of micro-CT-enabled whole-organism phenotyping. This protocol details the steps involved in D. magna samples preparation for micro-CT, including euthanasia, fixation, staining, and resin embedding. Micro-CT reconstructions of samples imaged using synchrotron micro-CT reveal histological (microanatomic) features of organ systems, tissues, and cells in the context of the entire organism at sub-micron resolution and in 3D. The enabled "3D histology" and 3D renderings can be used for morphometric analyses across cells, tissues, and organ systems. [ABSTRACT FROM AUTHOR]

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. Here, a cryoEM calibration sample consisting of a mixture of compatible macromolecules is introduced that can not only be used for resolution optimization, but also provides multiple reference points for evaluating instrument performance, data quality and image‐processing workflows in a single experiment. This combined test specimen provides researchers with a reference point for validating their cryoEM pipeline, benchmarking their methodologies and testing new algorithms. [ABSTRACT FROM AUTHOR]

Octopus arms, notable for their complex anatomy and remarkable flexibility, have sparked significant interest within the neuroscience community. However, there remains a dearth of knowledge about the neurochemical organization of various cell types in the arm's nervous system. To address this gap, we used hybridization chain reaction (HCR) to identify distinct neuronal types in the axial nerve cords of the pygmy octopus, Octopus bocki , including putative dopaminergic, octopaminergic, serotonergic, GABAergic, glutamatergic, cholinergic, and peptidergic cells. We obtained high-resolution multiplexed fluorescent images at 0.28 × 0.28 × 1.0 μm voxel size from 10 arm base and arm tip cross sections (each 50 μm thick) and created three-dimensional reconstructions of the axial ganglia, illustrating the spatial distribution of multiple neuronal populations. Our analysis unveiled anatomically distinct and molecularly diverse scattered neurons, while also highlighting multiple populations of dense small neurons that appear uniformly distributed throughout the cortical layer and potential glial cells in the neuropil. Our data provide new insights into how different types of neurons may contribute to an octopus's ability to interact with its environment and execute complex tasks. In addition, our findings establish a benchmark for future studies, allowing pioneering exploration of octopus arm molecular neuroanatomy and offering exciting new avenues in invertebrate neuroscience research. [Display omitted] • 3D octopus arm cord maps reveal stratified monoaminergic and peptidergic neurons • Significant neurochemical differences between the arm tip and base ganglia • Cholinergic and glutamatergic neurons are abundant; GABAergic neurons are scarce • Novel identification of neuronal cotransmission and possible protoplasmic glia Winters-Bostwick et al. reveal complex neurochemical organization within the arm nerve cord of Octopus bocki , using HCR to identify diverse neuronal types. High-resolution 3D imaging and reconstruction demonstrate spatial distribution of cell types, offering new insights into octopus neuroanatomy and enhancing understanding of limb function. [ABSTRACT FROM AUTHOR]

Single-photon emission computed tomography (SPECT) systems with pinhole collimators are becoming increasingly important in clinical and preclinical nuclear medicine investigations as they can provide a superior resolution-sensitivity tradeoff compared to conventional parallel-hole and fanbeam collimators. Previously, open-source software did not exist for reconstructing tomographic images from pinhole-SPECT datasets. A 3D SPECT system matrix modelling library specific for pinhole collimators has recently been integrated into STIR, an open-source software package for tomographic image reconstruction. The pinhole-SPECT library enables corrections for attenuation and the spatially variant collimator-detector response by incorporating their effects into the system matrix. Attenuation correction can be calculated with a simple single line of response or a full model. The spatially variant collimator-detector response can be modelled with a point spread function and depth of interaction corrections for increased system matrix accuracy. In addition, improvements to computational speed and memory requirements can be made with image masking. This work demonstrates the flexibility and accuracy of STIR's support for pinhole-SPECT datasets using measured and simulated single-pinhole SPECT data from which reconstructed images were analysed quantitatively and qualitatively. The extension of the opensource STIR project with advanced pinhole-SPECT modelling will enable the research community to study the impact of pinhole collimators in several SPECT imaging scenarios and with different scanners. [ABSTRACT FROM AUTHOR]

Expansion Microscopy (ExM) is an innovative and cost-effective super-resolution microscopy technique that has become popular in cell biology research. It achieves super-resolution by physically expanding specimens. Since its introduction, ExM has undergone continuous methodological developments to enhance its resolution and labeling capabilities. However, ExM imaging often encounters sample drift during image acquisition due to the physical movement of the expanded hydrogel, posing a significant challenge for accurate image reconstruction. Despite many proposed experimental solutions to mitigate sample drift, a universal solution has yet to be established. In response to this challenge, we developed 3D-Aligner, an advanced and user-friendly image analysis tool designed to computationally correct drift in ExM images for precise three-dimensional image reconstruction and downstream quantification. We demonstrate that 3D-Aligner effectively determines and corrects drift in ExM images with different expansion rates and various fluorescently labeled biological targets, showcasing its capabilities and robustness in drift correction. Additionally, we validate the precision of 3D-Aligner by comparing drift values across different labeled targets and highlight the importance of drift correction in quantification of biological structures. This study presents 3D-Aligner, a powerful software tool that corrects image distortions from sample drift with nanometer precision, particularly for Expansion Microscopy (ExM) imaging, a cutting-edge super-resolution method in cell biology research. [ABSTRACT FROM AUTHOR]

Screw loosening is a common complication of pedicle screw internal fixation surgery. This study aimed to investigate whether the application of a porous scaffold structure can increase the contact area between screws and bone tissue by comparing the bone ingrowth and screw-bone interface of porous scaffold core pedicle screws (PSCPSs) and hollow lateral hole pedicle screws (HLHPSs) in the lumbar spine of Bama pigs. Sixteen pedicle screws of both types were implanted into the bilateral pedicles of the L1–4 vertebrae of 2 Bama pigs. All Bama pigs were sacrificed and the lumbar spine was freed into individual vertebrae at 16 weeks postoperatively. After the vertebrae were made into screw-centered specimens, micro-computed tomography analysis and histological observation were performed to assess the screw-bone interface and bone growth around and within the screws. We found that the bone condition around PSCPSs and HLHPSs did not show significant differences on micro-computed tomography three-dimensional reconstruction images. CT transverse views showed different bone growth inside the 2 screws. In PSCPSs, bone tissue was seen to fill the internal pores and was evenly distributed around each strut. Inside HLHPSs, bone growth was confined to 1 side of the screw and did not fill the entire cavity. Osteometric analysis showed that bone volume fraction and trabecular number, the parameters representing bone mass, were higher in PSCPSs than in HLHPSs. These differences were not statistically significant (P > 0.05). Histological observations visualized that the osseointegration within PSCPSs was superior to that of HLHPSs, and the tight integration of bone tissue with the porous scaffold resulted in a larger screw-bone integration area in PSCPSs than in HLHPSs. Compared with HLHPSs, PSCPSs possessing a porous scaffold core could promote bone ingrowth and osseointegration, resulting in an effective enhancement of the combined area of the screw-bone interface. [ABSTRACT FROM AUTHOR]

For missile-borne platforms, traditional SAR technology consistently encounters two significant shortcomings: geometric distortion of 2D images and the inability to achieve forward-looking imaging. To address these issues, this paper explores the feasibility of using a maneuvering trajectory to enable forward-looking and three-dimensional imaging by analyzing the maneuvering characteristics of an actual missile-borne platform. Additionally, it derives the corresponding resolution characterization model, which lays a theoretical foundation for future applications. Building on this, the paper proposes a three-dimensional super-resolution imaging algorithm that combines axis rotation with compressed sensing. The axis rotation not only realizes the dimensionality reduction of data, but also can expand the observation scenario in the cross-track dimension. The proposed algorithm first focuses on the track-vertical plane to extract 2D position parameters. Then, a compressed sensing-based process is applied to extract reflection coefficients and super-resolution cross-track position parameters, thereby achieving precise 3D imaging reconstruction. Finally, numerical simulation results confirm the effectiveness and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The ID10 beamline of the SESAME (Synchrotron‐light for Experimental Science and Applications in the Middle East) synchrotron light source in Jordan was inaugurated in June 2023 and is now open to scientific users. The beamline, which was designed and installed within the European Horizon 2020 project BEAmline for Tomography at SESAME (BEATS), provides full‐field X‐ray radiography and microtomography imaging with monochromatic or polychromatic X‐rays up to photon energies of 100 keV. The photon source generated by a 2.9 T wavelength shifter with variable gap, and a double‐multilayer monochromator system allow versatile application for experiments requiring either an X‐ray beam with high intensity and flux, and/or a partially spatial coherent beam for phase‐contrast applications. Sample manipulation and X‐ray detection systems are designed to allow scanning samples with different size, weight and material, providing image voxel sizes from 13 µm down to 0.33 µm. A state‐of‐the‐art computing infrastructure for data collection, three‐dimensional (3D) image reconstruction and data analysis allows the visualization and exploration of results online within a few seconds from the completion of a scan. Insights from 3D X‐ray imaging are key to the investigation of specimens from archaeology and cultural heritage, biology and health sciences, materials science and engineering, earth, environmental sciences and more. Microtomography scans and preliminary results obtained at the beamline demonstrate that the new beamline ID10‐BEATS expands significantly the range of scientific applications that can be targeted at SESAME. [ABSTRACT FROM AUTHOR]

Objective Pediatric endodontics has become popular due to advancements in cleaning, shaping, and irrigation systems, resulting in faster and effective removal of infected pulp, saving time, and creating a pathogen-free environment. The patented rotary file system, Kedo S, designed for primary teeth, introduced a single file generation for efficient pulp therapy. However, there are currently no studies assessing canal preparation in primary mandibular canine using nano-computed tomography (nano-CT). This study aims to evaluate the efficacy of rotary file systems (Kedo S plus, Kedo SG blue) against traditional hand files in root canal preparation of primary mandibular canines using nano-CT. Materials and Methods This in vitro study was performed in extracted primary mandibular canine based on certain inclusion and exclusion criteria. Samples were prepared and working length was determined before the preoperative scan using a high-resolution nano-CT device (SkyScan 2214, Bruker, Kontich, Belgium). A single well-experienced pediatric dentist prepared the canals using three file systems: Kedo S plus, Kedo SG blue, and hand K-files. A postoperative scan was performed similar to preoperative scan. Image reconstruction was performed with NRecon software for three-dimensional volumetric visualization and analysis of the root canals. Results Kedo S plus displayed significant alterations in volumetric, surface area, and instrumented areas postpreparation compared with Kedo SG blue and hand files. Statistical analysis revealed significant differences in volumetric and surface changes between Kedo S plus and hand files. Conclusion Rotary file systems, especially Kedo S plus, resulted in increased canal volume and surface area with minimal uninstrumented areas, showing promise for primary dentition root canal preparations. Further clinical assessments are warranted to validate these findings. [ABSTRACT FROM AUTHOR]

With the continuous development of science and technology, laser ranging technology will become more efficient, convenient, and widespread, and it has been widely used in the fields of medicine, engineering, video games, and three-dimensional imaging. A time-of-flight (ToF) camera is a three-dimensional stereo imaging device with the advantages of small size, small measurement error, and strong anti-interference ability. However, compared to traditional sensors, ToF cameras typically exhibit lower resolution and signal-to-noise ratio due to inevitable noise from multipath interference and mixed pixels during usage. Additionally, in environments with scattering media, the information about objects gets scattered multiple times, making it challenging for ToF cameras to obtain effective object information. To address these issues, we propose a solution that combines ToF cameras with single-pixel imaging theory. Leveraging intensity information acquired by ToF cameras, we apply various reconstruction algorithms to reconstruct the object's image. Under undersampling conditions, our reconstruction approach yields higher peak signal-to-noise ratio compared to the raw camera image, significantly improving the quality of the target object's image. Furthermore, when ToF cameras fail in environments with scattering media, our proposed approach successfully reconstructs the object's image when the camera is imaging through the scattering medium. This experimental demonstration effectively reduces the noise and direct ambient light generated by the ToF camera itself, while opening up the potential application of ToF cameras in challenging environments, such as scattering media or underwater. [ABSTRACT FROM AUTHOR]

Purpose: Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion‐resolved imaging are desired for accurate delineation of the target and various organs‐at‐risk and patient‐tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)–based technique to produce volumetric, motion‐resolved, multicontrast images for abdominal radiotherapy treatment planning. Methods: The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization‐prepared (saturation recovery and T2 preparations) spoiled gradient‐echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. Results: In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast‐weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). Conclusion: The developed MT‐based, abdomen‐dedicated technique is capable of providing motion‐resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

X‐ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub‐parts and can be a time‐consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open‐access and user‐friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High‐resolution microCT images resulted in more accurate boundary‐preserved segmentation, nonetheless significantly lower‐quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add‐on, a stand‐alone Windows executable, and a Python library. We believe this accessible and user‐friendly segmentation tool can benefit the wider anatomical community. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) image reconstruction techniques have found extensive applications in fields such as medicine, education, and computer science, enabling high-precision 3D images to enhance work efficiency. However, traditional methods of 3D image reconstruction solely rely on acoustic information, resulting in limited accuracy. Therefore, a novel method based on virtual reality (VR) and multimodal deep learning is proposed for 3D image reconstruction. First, VR technology is employed to capture 3D image information, followed by de-noising and removal of redundant information. Second, a logarithmic transformation method is employed to enhance the details in the 3D image. Finally, a multimodal deep learning method is utilized to reconstruct the 3D image from the perspectives of imagery, sound, and video. Experimental results demonstrate that the proposed method achieves superior 3D image reconstruction with an accuracy of over 90%. The reconstruction process is efficient and exhibits low signal-to-noise ratio, while the average registration error is less than 0.04%. These findings highlight the practical value and potential applications of the proposed method. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction based on optical satellite images has always been a research hotspot in the field of photogrammetry. In particular, the 3D reconstruction of building areas has provided great help for urban planning, change detection and emergency response. The results of 3D reconstruction of satellite images are greatly affected by the input images, and this paper proposes an improvement method for 3D reconstruction of satellite images based on the generative adversarial network (GAN) image enhancement. In this method, the perceptual loss function is used to optimize the network, so that it can output high-definition satellite images for 3D reconstruction, so as to improve the completeness and accuracy of the reconstructed 3D model. We use the public benchmark dataset of satellite images to test the feasibility and effectiveness of the proposed method. The experiments show that compared with the satellite stereo pipeline (S2P) method and the bundle adjustment (BA) method, the proposed method can automatically reconstruct high-quality 3D point clouds. [ABSTRACT FROM AUTHOR]

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning. [ABSTRACT FROM AUTHOR]

Cone‐beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x‐ray cone‐beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time‐resolved information. To overcome this limitation, 4D‐CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration‐correlated 4D‐CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D‐CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D‐CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x‐ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low‐dose, and high‐quality 4D‐CBCT. This paper aims to review the technical developments surrounding 4D‐CBCT comprehensively. It will explore conventional algorithms and recent deep learning‐based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D‐CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D‐CBCT's capabilities and potential to enhance radiation therapy. [ABSTRACT FROM AUTHOR]

A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm. [ABSTRACT FROM AUTHOR]

Various imaging modalities allow for time-dependent image reconstructions from measurements where its acquisition also has a time-dependent nature. Magnetic particle imaging (MPI) falls into this class of imaging modalities and it thus also provides a dynamic inverse problem. Without proper consideration of the dynamic behavior, motion artifacts in the reconstruction become an issue. More sophisticated methods need to be developed and applied to the reconstruction of the time-dependent sequences of images. In this context, we investigate the incorporation of motion priors in terms of certain flow-parameter-dependent PDEs in the reconstruction process of time-dependent three dimensional (3D) images in magnetic particle imaging. The present work comprises the method development for a general 3D+time setting for time-dependent linear forward operators, analytical investigation of necessary properties in the MPI forward operator, modeling aspects in dynamic MPI, and extensive numerical experiments on 3D+time imaging including simulated data as well as measurements from a rotation phantom and in vivo data from a mouse. [ABSTRACT FROM AUTHOR]

A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image. [ABSTRACT FROM AUTHOR]

Fresnel incoherent correlation holography (FINCH) can achieve high-precision and non-scanning 3D imaging. However, as a holographic imaging technology, the huge bandwidth requirements and the amount of holographic data transmitted have always been one of the important factors limiting its application. In addition, the hardware cost of pixel array-based CCD or CMOS imaging is very high under high resolution or specific wavelength conditions. Accordingly, a single-pixel Fresnel incoherent correlation holography (SP-FINCH) compressed imaging method is proposed, which replaces pixel array detector with single-pixel detector and designs a Trumpet network to achieve low-cost and high-resolution imaging. Firstly, a modified FINCH imaging system is constructed and data acquisition is carried out using a single-pixel detector. Secondly, a Trumpet network is constructed to directly map the relationship between one-dimensional sampled data and two-dimensional image in an end-to-end manner. Moreover, by comparing the reconstructed images using neural network with that using commonly used single-pixel reconstruction methods, the results indicate that the proposed SP-FINCH compressed imaging method can significantly improve the quality of image reconstruction at lower sampling rate and achieve imaging without phase-shifting operation. The proposed method has been shown to be feasible and advantageous through numerical simulations and optical experiment results. [ABSTRACT FROM AUTHOR]

Significance: Photoacoustic computed tomography (PACT) is a promising noninvasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. Aim: In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. Approach: We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Results: Notably, our framework can achieve an effective rate of ~871 times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Conclusions: Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine. [ABSTRACT FROM AUTHOR]

Knowledge of the venous anatomy is essential for appropriately treating dural arteriovenous fistulas (AVFs). It is challenging to determine the overall venous structure despite performing selective angiography for dural AVFs with feeder from multiple selected arteries. This is because only a part of the veins can be observed through the shunt in the selected artery. Therefore, after performing selective angiography of all vessels to understand the approximate venous anatomy, the venous anatomy can be easily understood by closely examining the source image of computed tomographic angiography or magnetic resonance angiography. Through this, it is possible to specify the vein that is to be blocked (target embolization), thereby avoiding extensive blocking of the vein and avoiding various complications. In the case of dural AVF with feeder from single selected artery, if the multiplanar reconstruction image of the three-dimensional rotational computed tomography obtained by performing angiography is analyzed thoroughly, a shunted pouch can be identified. If embolization is performed by targeting this area, unnecessary sinus total packing can be avoided. [ABSTRACT FROM AUTHOR]

Background: Magnetically assisted capsule endoscopy (MACE) showed the feasibility for upper gastrointestinal examination. To further enhance the performance of conventional MACE, it is necessary to provide quality-improved and three-dimensional images. The aim of this clinical study was to determine the efficacy and safety of novel three-dimensional MACE (3D MACE) for upper gastrointestinal and small bowel examination at once. Methods: This was a prospective, single-center, non-randomized, and sequential examination study (KCT0007114) at Dongguk University Ilsan Hospital. Adult patients who visited for upper endoscopy were included. The study protocol was conducted in two stages. First, upper gastrointestinal examination was performed using 3D MACE, and a continuous small bowel examination was performed by conventional method of capsule endoscopy. Two hours later, an upper endoscopy was performed for comparison with 3D MACE examination. The primary outcome was confirmation of major gastric structures (esophagogastric junction, cardia/fundus, body, angle, antrum, and pylorus). Secondary outcomes were confirmation of esophagus and duodenal bulb, accuracy for gastric lesions, completion of small bowel examination, 3D image reconstruction of gastric lesion, and safety. Results: Fifty-five patients were finally enrolled. The examination time of 3D MACE was 14.84 ± 3.02 minutes and upper endoscopy was 5.22 ± 2.39 minutes. The confirmation rate of the six major gastric structures was 98.6% in 3D MACE and 100% in upper endoscopy. Gastric lesions were identified in 43 patients during 3D MACE, and 40 patients during upper endoscopy (Sensitivity 0.97). 3D reconstructed images were acquired for all lesions inspected by 3D MACE. The continuous small bowel examination by 3D MACE was completed in 94.5%. 3D MACE showed better overall satisfaction (3D MACE 9.55 ± 0.79 and upper endoscopy 7.75 ± 2.34, p<0.0001). There were no aspiration or significant adverse event or capsule retention in the 3D MACE examination. Conclusions: Novel 3D MACE system is more advanced diagnostic modality than the conventional MACE. And it is possible to perform serial upper gastrointestinal and small bowel examination as a non-invasive and one-step test. It would be also served as a bridge to pan-endoscopy. [ABSTRACT FROM AUTHOR]

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data. Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST). Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured. Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods. [ABSTRACT FROM AUTHOR]

The article is devoted to the experience of constructing three-dimensional models of two churches on Mount Anakopia (Abkhazia), which houses one of the richest lapidary collections with relief images (X-XI centuries) in Abkhazia. Two monographs by one of the authors of this study (E. Endoltseva) were devoted to a detailed study of this lapidary collection, the themes and plots presented on the reliefs, their classification and dating, as well as the architectural features of these buildings. This study continues these research efforts. The article describes in detail the additional research carried out and the principles of reconstruction of the two churches. The text is accompanied by drawings of reconstructed facades and altar barriers from these churches published for the first time. The reconstruction used fragments of stone plastic found on Mount Anakopia. The reconstructed images of churches are placed in the artistic context of the era (Middle Byzantine period, 9th - 11th centuries). [ABSTRACT FROM AUTHOR]

Doppler tomography (DT) is a method that allows the reconstruction of 2D or 3D images of the inside of the examined object. For this purpose, a two-transducer ultrasound probe is used. In this method, the Doppler phenomenon and the so-called Doppler signal are used to obtain an image. Therefore, the probe is one of the most important components of the measurement system of this method. It should be noted that Doppler tomography differs significantly from the well-known Doppler method of measuring blood flow in blood vessels. In the DT method, stationary cross-sectional images of the object under examination are obtained. In order to produce the Doppler effect in this case, the probe can move around or along the object being imaged. This paper will present a simulation of the effect of the frequency of ultrasonic wave generated by the ultrasound probe on the imaging quality for a single inclusion. [ABSTRACT FROM AUTHOR]

In our research, we provide an enhanced and effective Krawtchouk moments parameter p optimization approach using the Artificial Bee Colony (ABC) algorithm optimization technique for the purpose of completing tasks including the reconstruction and classification of 3D and 2D images with high quality. The proposed method, which uses the ABC algorithm to generate a suitable parameter based on Krawtchouck moments, aims to accurately characterize the image as a vector known as the descriptor vector of moments, which is then used to reconstruct the image for each order. The simulation and analysis of the findings demonstrate the high quality and effectiveness of the suggested KM-ABC approach for image reconstruction, as well as its excellent accuracy in images with and without noise. [ABSTRACT FROM AUTHOR]

Objective. The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy. Approach. Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints. Main results. The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods. Significance. The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model's generalizability suggests it has the potential applicable in various healthcare settings. [ABSTRACT FROM AUTHOR]

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in vivo 3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes. [ABSTRACT FROM AUTHOR]

Advances in fluorescence microscopy and tissue-clearing have revolutionised 3D imaging of fluorescently labelled tissues, organs and embryos. However, the complexity and high cost of existing software and computing solutions limit their widespread adoption, especially by researchers with limited resources. Here, we present Acto3D, an open-source software, designed to streamline the generation and analysis of high-resolution 3D images of targets labelled with multiple fluorescent probes. Acto3D provides an intuitive interface for easy 3D data import and visualisation. Although Acto3D offers straightforward 3D viewing, it performs all computations explicitly, giving users detailed control over the displayed images. Leveraging an integrated graphics processing unit, Acto3D deploys all pixel data to system memory, reducing visualisation latency. This approach facilitates accurate image reconstruction and efficient data processing in 3D, eliminating the need for expensive highperformance computers and dedicated graphics processing units. We have also introduced a method for efficiently extracting lumen structures in 3D. We have validated Acto3D by imaging mouse embryonic structures and by performing 3D reconstruction of pharyngeal arch arteries while preserving fluorescence information. Acto3D is a cost-effective and efficient platform for biological research. [ABSTRACT FROM AUTHOR]

In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score. [ABSTRACT FROM AUTHOR]

Reconstruction of 2-dimensional transcerebellar images into 3-dimensional transcerebellar has an important role in diagnosis and treatment planning in neurology. In this study, we propose the 3D-FHNet method to produce an accurate three-dimensional representation of transcerebellar images. The process begins with the selection of images and the application of image augmentation and enhancement techniques to improve the quality of the initial image. Furthermore, segmentation was carried out using two different architectures, namely U-Net and LinkNet, to compare the performance of the two. After training, both architectures can process object segmentation properly. The best performance was produced by U-Net with a pixel accuracy of 99.83%, Mean IU of 89.71%, FPR of 0.91%, Precision of 85.78%, Recall of 85.31%, and F1 Score of 85.31%. With this accuracy, cross-validation was carried out using a 10-Fold. The experimental results show that U-Net gives better results in terms of transcerebellar image segmentation. After the segmentation process, an initial reconstruction is carried out using the PiFUHD architecture which produces a three-dimensional object. The results of this reconstruction are then taken from four sides to be introduced to the 3D-FHNet architecture, because the main concept of 3DFHNet is to use multiview input. To measure the accuracy of the model, the IoU (Intersection over Union) metric is used for the ground truth obtained from the PiFUHD method. This metric is used to compare the similarities between the reconstruction results and the ground truth, so that information can be obtained about the extent to which the model has succeeded in reconstructing three-dimensional objects accurately. Model performance can be calculated by using the input as ground truth or the IoU (Intersection over Union) method with an average of 76.76%. By using the 3D-FHNet method, three-dimensional image reconstruction of the ultrasound image of the fetal head can be performed accurately. The experimental results show that the 3D-FHNet method produces a more accurate three-dimensional representation of the transcerebellar image compared to the previous method. These results demonstrate great potential in improving diagnosis and treatment planning in neurology, as well as making important contributions to the development of medical image processing technologies. [ABSTRACT FROM AUTHOR]

A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the quality of 3D reconstruction from WCE images. In this paper, we employ a single-image-based 3D reconstruction method on an artificial colon captured with an endoscope that behaves like WCE. The shape from shading (SFS) algorithm can reconstruct the 3D shape using a single image. Therefore, it has been employed to reconstruct the 3D shapes of the colon images. The camera of the endoscope has also been subjected to comprehensive geometric and radiometric calibration. Experiments are conducted on well-defined primitive objects to assess the method's robustness and accuracy. This evaluation involves comparing the reconstructed 3D shapes of primitives with ground truth data, quantified through measurements of root-mean-square error and maximum error. Afterward, the same methodology is applied to recover the geometry of the colon. The results demonstrate that our approach is capable of reconstructing the geometry of the colon captured with a camera with an unknown imaging pipeline and significant noise in the images. The same procedure is applied on WCE images for the purpose of 3D reconstruction. Preliminary results are subsequently generated to illustrate the applicability of our method for reconstructing 3D models from WCE images. [ABSTRACT FROM AUTHOR]

Dynamic three‐dimensional computed tomography (CT) imaging of liquid transport in porous media has primarily been conducted at high brilliance synchrotrons thus allowing fast, sometimes sub‐second, temporal resolution to be obtained. University laboratory CT instruments lack the photon flux available at synchrotrons, limiting the obtainable spatiotemporal resolution. Here, we discuss our experiences with instrumentation and software methods to conduct time‐resolved micro‐computed tomography (4D‐CT) experiments of flow in porous media, based on a conventional CT instrument operated with a highly undersampled number of projections. An experimental stage outfitted with syringe pumps placed on a slip ring allowed two‐phase flow experiments to be carried out with continuous unidirectional rotation and without obstruction of the liquid supply lines. An iterative reconstruction algorithm based on a priori information was used to provide high image quality and ∼30 s time resolution despite the few and low‐exposed projections compared to standard protocols. The experimental technique was demonstrated with imbibition and drainage in glass bead‐pack and Bentheimer sandstone samples with sub‐minute temporal resolution, allowing the liquid configurations just before and after fast dynamic phenomena such as cooperative pore‐filling events and Haines jumps to be captured. Power law scaling exponents for burst volumes associated with imbibition and drainage were estimated and compared with the literature. That 4D‐CT experiments can be carried out using conventional CT instruments to challenge contemporary permeability models is of high importance for many geo‐, bio‐ and environmental physics challenges. Plain Language Summary: Computed Tomography or CT is well‐known as a technique for investigating objects in 3D non‐destructively. In principle, 3D movies, referred to as "dynamic CT" or "4D CT" is a simple extension, requiring just a series of standard CT experiments to be carried out in sequence. In practice, however, there is a high demand for clever methods and algorithms that can simultaneously obtain high spatial and temporal resolutions. When more than one fluid is being transported through a porous medium, a plethora of phenomena take place, and 4D‐CT has proven a powerful tool to better understand the physics of porous media. Most 4D‐CT studies in the past have been carried out at synchrotron facilities, which are extremely high flux X‐ray sources, but also a scarce and expensive resource. We describe our experiences with 4D‐CT measurements of fluid transport in porous media, as performed in our home laboratory. Key Points: Time‐resolved 3D imaging of flow in porous mediaDynamic micro‐computed tomography [ABSTRACT FROM AUTHOR]

Purpose: Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods. Methods: An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12–100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM). Results: While taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively. Conclusion: Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging. [ABSTRACT FROM AUTHOR]

Cryo-Electron Microscopy (cryo-EM) is a widely used and effective method for determining the three-dimensional (3D) structure of biological molecules. For ab-initio Cryo-EM 3D reconstruction using single particle analysis (SPA), estimating the projection direction of the projection image is a crucial step. However, the existing SPA methods based on common lines are sensitive to noise. The error in common line detection will lead to a poor estimation of the projection directions and thus may greatly affect the final reconstruction results. To improve the reconstruction results, multiple candidate common lines are estimated for each pair of projection images. The key problem then becomes a combination optimization problem of selecting consistent common lines from multiple candidates. To solve the problem efficiently, a physics-inspired method based on a kinetic model is proposed in this work. More specifically, hypothetical attractive forces between each pair of candidate common lines are used to calculate a hypothetical torque exerted on each projection image in the 3D reconstruction space, and the rotation under the hypothetical torque is used to optimize the projection direction estimation of the projection image. This way, the consistent common lines along with the projection directions can be found directly without enumeration of all the combinations of the multiple candidate common lines. Compared with the traditional methods, the proposed method is shown to be able to produce more accurate 3D reconstruction results from high noise projection images. Besides the practical value, the proposed method also serves as a good reference for solving similar combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

Multiaxial neutron/x-ray imaging and three-dimensional (3D) reconstruction techniques play a crucial role in gaining valuable insights into the generation and evolution mechanisms of pulsed radiation sources. Owing to the short emission time (∼200 ns) and drastic changes of the pulsed radiation source, it is necessary to acquire projection data within a few nanoseconds in order to achieve clear computed tomography 3D imaging. As a consequence, projection data that can be used for computed tomography image reconstruction at a certain moment are often available for only a few angles. Traditional algorithms employed in the process of reconstructing 3D images with extremely incomplete data may introduce significant distortions and artifacts into the final image. In this paper, we propose an iterative image reconstruction method using cylindrical harmonic decomposition and a self-supervised denoising network algorithm based on the deep image prior method. We augment the prior information with a 2D total variation prior and a 3D deep image prior. Single-wire Z-pinch imaging experiments have been carried out at Qin-1 facility in five views and four frames, with a time resolution of 3 ns for each frame and a time interval of 40 ns between adjacent frames. Both numerical simulations and experiments verify that our proposed algorithm can achieve high-quality reconstruction results and obtain the 3D intensity distribution and evolution of extreme ultraviolet and soft x-ray emission from plasma. [ABSTRACT FROM AUTHOR]

Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, they still have limitations, such as knowing the information of the structure in advance, only processing simple structures, limited effectiveness for structures with a depth of about 15 mm, and the need to use separated deep learning networks for de-blurring and estimating information. Furthermore, the current technique cannot handle multiple structures distributed at different depths next to each other in the same image. To overcome the mentioned limitations in transillumination imaging, this study proposed a pixel-by-pixel scanning technique in combination with deep learning networks (Attention Res-UNet for scattering suppression and DenseNet-169 for depth estimation) to estimate the existence of each pixel and the relative structural depth information. The efficacy of the proposed method was evaluated through experiments that involved a complex model within a tissue-equivalent phantom and a mouse, achieving a reconstruction error of 2.18 % compared to the dimensions of the ground truth when using the fully convolutional network. Furthermore, we could use the depth matrix obtained from the convolutional neural network (DenseNet-169) to reconstruct the absorbing structures using a binary thresholding method, which produced a reconstruction error of 6.82 %. Therefore, only one convolutional neural network (DenseNet-169) must be used for depth estimation and explicit image reconstruction. Therefore, it reduces time and computational resources. With depth information at each pixel, reconstruction of 3D image of the de-blurred structures could be performed even from a single blurred image. These results confirm the feasibility and robustness of the proposed pixel-by-pixel scanning technique to restore the internal structure of the body, including intricate networks such as blood vessels or abnormal tissues. [ABSTRACT FROM AUTHOR]

Heart muscle has the unique property that it can never rest; all cardiomyocytes contract with each heartbeat which requires a complex control mechanism to regulate cardiac output to physiological requirements. Changes in calcium concentration regulate the thin filament activation. A separate but linked mechanism regulates the thick filament activation, which frees sufficient myosin heads to bind the thin filament, thereby producing the required force. Thick filaments contain additional nonmyosin proteins, myosin-binding protein C and titin, the latter being the protein that transmits applied tension to the thick filament. How these three proteins interact to control thick filament activation is poorly understood. Here, we show using 3-D image reconstruction of frozen-hydrated human cardiac muscle myofibrils lacking exogenous drugs that the thick filament is structured to provide three levels of myosin activation corresponding to the three crowns of myosin heads in each 429Å repeat. In one crown, the myosin heads are almost completely activated and disordered. In another crown, many myosin heads are inactive, ordered into a structure called the interacting heads motif. At the third crown, the myosin heads are ordered into the interacting heads motif, but the stability of that motif is affected by myosin-binding protein C. We think that this hierarchy of control explains many of the effects of length-dependent activation as well as stretch activation in cardiac muscle control. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (XCT) has become a powerful technique for studying lithium-ion batteries, allowing non-destructive 3D imaging across multiple spatial scales. Image quality is particularly important for observing the internal structure of lithium-ion batteries. During multiple rotations, the existence of cumulative errors and random errors in the rotary table leads to errors in the projection angle, affecting the imaging quality of XCT. The accuracy of the projection angle is an important factor that directly affects imaging. However, the impact of the projection angle on XCT reconstruction imaging is difficult to quantify. Therefore, the required precision of the projection angle sensor cannot be determined explicitly. In this research, we selected a common 18650 cylindrical lithium-ion battery for experiments. By setting up an XCT scanning platform and installing an angle sensor to calibrate the projection angle, we proceeded with image reconstruction after introducing various angle errors. When comparing the results, we found that projection angle errors lead to the appearance of noise and many stripe artifacts in the image. This is particularly noticeable in the form of many irregular artifacts in the image background. The overall variation and residual projection error in detection indicators can effectively reflect the trend in image quality. This research analyzed the impact of projection angle errors on imaging and improved the quality of XCT imaging by installing angle sensors on a rotary table. [ABSTRACT FROM AUTHOR]

Single-view cone-beam X-ray luminescence computed tomography (CB-XLCT) has recently gained attention as a highly promising imaging technique that allows for the efficient and rapid three-dimensional visualization of nanophosphor (NP) distributions in small animals. However, the reconstruction performance is hindered by the ill-posed nature of the inverse problem and the effects of depth variation as only a single view is acquired. To tackle this issue, we present a methodology that integrates an automated restarting strategy with depth compensation to achieve reconstruction. The present study employs a fast proximal gradient descent (FPGD) method, incorporating L0 norm regularization, to achieve efficient reconstruction with accelerated convergence. The proposed approach offers the benefit of retrieving neighboring multitarget distributions without the need for CT priors. Additionally, the automated restarting strategy ensures reliable reconstructions without the need for manual intervention. Numerical simulations and physical phantom experiments were conducted using a custom CB-XLCT system to demonstrate the accuracy of the proposed method in resolving adjacent NPs. The results showed that this method had the lowest relative error compared to other few-view techniques. This study signifies a significant progression in the development of practical single-view CB-XLCT for high-resolution 3−D biomedical imaging. [ABSTRACT FROM AUTHOR]

This article focuses on generation of diffractive optical elements (DOEs) and computer holograms for forming three-dimensional (3D) images. We intend to analyze the possibilities of increasing (1) the speed of generation of DOEs, and (2) the quality of 3D objects created by the DOEs generated. For this, four methods of optical element generation are analyzed, which are based on division of 3D objects into plane layers. We assess the quality of 3D-object image reconstruction and the resource intensity of the methods with respect to synthesis of DOEs. We conduct a computer simulation of reconstruction of images of 3D objects via the generated DOEs. In optical experiments on formation of 3D objects, the DOEs generated are displayed on a liquid crystal spatial light modulator. We experimentally establish that the method of parallel computation of plane layers and the method of nonconvex optimization are optimum for formation of 3D objects vis-à-vis quality of their reconstruction. Taking into account the computational resource intensity of the considered methods, the iterative method of parallel computation of plane layers yielded the optimal DOE generation results in terms of reconstruction quality-to-synthesis speed ratio. The possibility of fast formation of high-quality 3D objects comprising dozens of layers has been demonstrated, which can be used in high-resolution 3D video communication systems. [ABSTRACT FROM AUTHOR]

The objective of the current study was to develop and evaluate a DEep learning‐based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)‐based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high‐resolution spiral real‐time cine imaging, including 2D balanced steady‐state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U‐Net–based image reconstruction network and 2D U‐Net–based image segmentation network were proposed and evaluated. Low‐rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal‐to‐noise ratio, normalized root‐mean‐square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p > 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL‐based image reconstruction and quantification techniques enabled 1‐min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high‐resolution spiral real‐time cine imaging with excellent performance. [ABSTRACT FROM AUTHOR]

Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for three‐dimensional (3D) non‐Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cartesian imaging, coil compression inherently loses signal energy, producing shading artifacts that compromise image quality for 3D non‐Cartesian imaging. We propose coil sketching, a general and versatile method for computationally‐efficient iterative MR image reconstruction. Theory and Methods: We based our method on randomized sketching algorithms, a type of large‐scale optimization algorithms well established in the fields of machine learning and big data analysis. We adapt the sketching theory to the MRI reconstruction problem via a structured sketching matrix that, similar to coil compression, considers high‐energy virtual coils obtained from principal component analysis. But, unlike coil compression, it also considers random linear combinations of the remaining low‐energy coils, effectively leveraging information from all coils. Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non‐Cartesian datasets. The resulting design yielded both improved computatioanal efficiency and preserved signal‐to‐noise ratio (SNR) as measured by the inverse g‐factor. Then, we verified the efficacy of our approach on high‐dimensional non‐Cartesian 3D cones datasets, where coil sketching yielded up to three‐fold faster reconstructions with equivalent image quality. Conclusion: Coil sketching is a general and versatile reconstruction framework for computationally fast and memory‐efficient reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current‐induced artifacts incurred by strong diffusion‐sensitizing gradients up to 300 mT/m used in high b‐value diffusion‐weighted (DW) EPI. Methods: A dynamic field camera equipped with 16 1H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi‐shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring‐based image reconstruction was demonstrated on high‐gradient strength, submillimeter resolution whole‐brain ex vivo diffusion MRI. A 3D multi‐shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera. Results: Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k‐space trajectory. During the readout, phase modulations between odd and even EPI echoes become non‐static and diffusion encoding direction‐dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator‐ and structured low‐rank‐based methods or MUSE followed by image‐based distortion correction with the FSL tool "eddy." Conclusion: Strong eddy current artifacts characteristic of high‐gradient strength DW‐EPI can be well corrected with dynamic field monitoring‐based image reconstruction. [ABSTRACT FROM AUTHOR]

Lensless imaging allows for designing imaging systems that are free from the constraints of traditional imaging architectures. As a broadly investigated technique, mask-modulated lensless imaging encodes light signals via a mask plate integrated with the image sensor, which is more compacted, with scalability and compressive imaging abilities. Here, we review the latest advancements in mask-modulated lensless imaging, lensless image reconstruction algorithms, related techniques, and future directions and applications. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) technology is widely used in the field of security imaging. However, existing imaging systems have shortcomings such as numerous array units, high hardware costs, and low imaging resolutions. In this paper, a sparse array-based frequency modulated continuous wave (FMCW) millimeter wave imaging system, operating in the W-band, is presented. In order to reduce the number of transceiver units of the system and lower the hardware cost, a linear sparse array with a periodic structure was designed using the MIMO technique. The system operates at 70~80 GHz, and the high operating frequency band and 10 GHz bandwidth provide good imaging resolution. The system consists of a one-dimensional linear array, a motion control system, and hardware for signal generation and image reconstruction. The channel calibration technique was used to eliminate inherent errors. The system combines mechanical and electrical scanning, and uses FMCW signals to extract distance information. The three-dimensional (3-D) fast imaging algorithm in the wave number domain was utilized to quickly process the detection data. The 3-D imaging of the target in the near-field was obtained, with an imaging resolution of 2 mm. The imaging ability of the system was verified through simulations and experiments. [ABSTRACT FROM AUTHOR]

It is time-consuming to acquire complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Under-sampling in the 3D k-space is promising in accelerating such 3D MRI process. Although 3D under-sampling can be conducted according to predefined probability density, the density-based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D under-sampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific under-sampling patterns for the fully sampled 3D k-space data. Innovatively, our under-sampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of under-sampled data. Through end-to-end training, we obtain an optimized 3D under-sampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrate that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian under-sampling schemes, our end-to-end learned under-sampling pattern considerably improves the reconstruction quality of under-sampled MRI images. [ABSTRACT FROM AUTHOR]