High-resolution Reconstruction Research Articles

Fast descriptor-based 2D and 3D microstructure reconstruction using the Portilla–Simoncelli algorithm

AbstractReconstructing microstructures from statistical descriptors is a key enabler of computer-based inverse materials design. In the Yeong–Torquato algorithm and other common methods, the problem is approached by formulating it as an optimization problem in the space of possible microstructures. In this case, the error between the desired microstructure and the current reconstruction is measured in terms of a descriptor. As an alternative, descriptors can be regarded as constraints defining subspaces or regions in the microstructure space. Given a set of descriptors, a valid microstructure can be obtained by sequentially projecting onto these subspaces. This is done in the Portilla–Simoncelli algorithm, which is well known in the field of texture synthesis. Noting the algorithm’s potential, the present work aims at introducing it to microstructure reconstruction. After exploring its capabilities and limitations in 2D, a dimensionality expansion is developed for reconstructing 3D volumes from 2D reference data. The resulting method is extremely efficient, as it allows for high-resolution reconstructions on conventional laptops. Various numerical experiments are conducted to demonstrate its versatility and scalability. Finally, the method is validated by comparing homogenized mechanical properties of original and reconstructed 3D microstructures.

Full waveform inversion of cross-hole radio frequency electromagnetic data

Summary We consider application of full waveform inversion (FWI) to radio-frequency electromagnetic (EM) data. Radio-frequency imaging (RIM) is a cross-borehole technique to image electromagnetic subsurface properties from measurements of transmitted radio-frequency waves. It is used in coal seam imaging, ore exploration and various engineering and civil engineering applications. RIM operates at frequencies from 50 kHz to several tens of MHz. It differs from other geophysical EM methods, because the frequency band includes the transition between the wave propagation and diffusion regimes. RIM data are acquired in two-dimensional cross-hole sections in a reciprocal manner. Traditionally, radio-frequency data are inverted by straight ray tomography because it is inexpensive and easy to implement. It is argued that due to attenuation, the sensitivity of the transmitted electric field is the strongest within the first Fresnel zone of the ray connecting the transmitter and receiver. While straight ray tomography is a simple to implement and fast method, the non-linearity in the relationship between model parameters and data is often strong enough to warrant non-linear inversion techniques. FWI is an iterative high resolution technique, in which the physical properties are updated to minimize the misfit between the measured and modelled wavefields. Full waveform techniques have been used and extensively studied for the inversion of seismic data, and more recently, they have been applied to the inversion of GPR data. Non-linear inversion methods for RIM data are less advanced. Their use has been hindered by the high cost of full wave modelling and the high conductivity contrasts of many RIM targets, and, to some extent, by the limitations of the measuring instruments. We present the first application of this methodology to simultaneous conductivity and permittivity inversion of RIM data. We implement the inversion in the frequency domain in two dimensions using L-BFGS optimization. We analyze the sensitivity of the data to the model parameters and the parameter trade-off and validate the proposed methodology on a synthetic example with moderate conductivity variations and localized highly conductive targets. We then apply the FWI methodology to a field data set from Sudbury, Canada. For the field data set, we determine the most appropriate preprocessing steps that take into account specific peculiarities of RIM: the insufficient prior information about the subsurface and the limitations of the measuring equipment. We show that FWI is applicable under the conditions of RIM and is robust to imperfect prior knowledge: we obtain satisfactory model recoveries starting from homogeneous initial models in all of our examples. Just as other methods, FWI underestimates large conductivity contrasts due to the loss of sensitivity of the transmitted electric field to the conductivity variations as the conductivity increases above a certain level. The permittivity inside high conductors can not be recovered, however, recovering permittivity variations in the resistive zones helps obtain better focused conductivity images with fewer artifacts. Overall, FWI produces cleaner, less noisy and higher resolution reconstructions than the methods currently used in practice.

The role of spinal cord neuroanatomy and the variances of epidurally evoked spinal responses

BackgroundSpinal cord stimulation (SCS) has demonstrated multiple benefits in treating chronic pain and other clinical disorders related to sensorimotor dysfunctions. However, the underlying mechanisms are still not fully understood, including how electrode placement in relation to the spinal cord neuroanatomy influences epidural spinal recordings (ESRs). To characterize this relationship, this study utilized stimulation applied at various anatomical sections of the spinal column, including at levels of the intervertebral disc and regions correlating to the dorsal root entry zone.MethodTwo electrode arrays were surgically implanted into the dorsal epidural space of the swine. The stimulation leads were positioned such that the caudal-most electrode contact was at the level of a thoracic intervertebral segment. Intraoperative cone beam computed tomography (CBCT) images were utilized to precisely determine the location of the epidural leads relative to the spinal column. High-resolution microCT imaging and 3D-model reconstructions of the explanted spinal cord illustrated precise positioning and dimensions of the epidural leads in relation to the surrounding neuroanatomy, including the spinal rootlets of the dorsal and ventral columns of the spinal cord. In a separate swine cohort, implanted epidural leads were used for SCS and recording evoked ESRs.ResultsReconstructed 3D-models of the swine spinal cord with epidural lead implants demonstrated considerable distinctions in the dimensions of a single electrode contact on a standard industry epidural stimulation lead compared to dorsal rootlets at the dorsal root entry zone (DREZ). At the intervertebral segment, it was observed that a single electrode contact may cover 20-25% of the DREZ if positioned laterally. Electrode contacts were estimated to be ~0.75 mm from the margins of the DREZ when placed at the midline. Furthermore, ventral rootlets were observed to travel in proximity and parallel to dorsal rootlets at this level prior to separation into their respective sides of the spinal cord. Cathodic stimulation at the level of the intervertebral disc, compared to an ‘off-disc’ stimulation (7 mm rostral), demonstrated considerable variations in the features of recorded ESRs, such as amplitude and shape, and evoked unintended motor activation at lower stimulation thresholds. This substantial change may be due to the influence of nearby ventral roots. To further illustrate the influence of rootlet activation vs. dorsal column activation, the stimulation lead was displaced laterally at ~2.88 mm from the midline, resulting in variances in both evoked compound action potential (ECAP) components and electromyography (EMG) components in ESRs at lower stimulation thresholds.ConclusionThe results of this study suggest that the ECAP and EMG components of recorded ESRs can vary depending on small differences in the location of the stimulating electrodes within the spinal anatomy, such as at the level of the intervertebral segment. Furthermore, the effects of sub-centimeter lateral displacement of the stimulation lead from the midline, leading to significant changes in electrophysiological metrics. The results of this pilot study reveal the importance of the small displacement of the electrodes that can cause significant changes to evoked responses SCS. These results may provide further valuable insights into the underlying mechanisms and assist in optimizing future SCS-related applications.

Single-shot dual-wavelength telecentric in-line-and-off-axis hybrid digital holography with non-prior reconstruction

Dual-wavelength in-line-and-off-axis hybrid digital holography (iohDH) can achieve high-resolution holographic dynamic imaging. However, it requires the prediction of the diffraction distance and the complex amplitude of the reference beam, which is time consuming and results in complications and accuracy limitations. While telecentric imaging technique can obtain nondiffractive images without predicting the diffraction distance, it also can even eliminate spherical aberration and astigmatic aberration. Therefore, a dual-wavelength telecentric iohDH is proposed to realize non-prior high-resolution reconstruction in a single shot. Employing the dual-wavelength telecentric iohDH, our approach acquires the focused in-line-and-off-axis hologram using a color camera in a single shot. In this case, we perform wavelength conversion on the phase and low-frequency information about the off-axis hologram as constraints for in-line iteration. Then, the in-line amplitude constraints are performed in the spatial and frequency domains until the algorithm converges. Compared to the state-of-the-art dual-wavelength iohDH, our approach can streamline the reconstructed processes without demanding a priori information of the diffraction distance and the complex amplitude of the reference beam. More importantly, our approach enables higher quality and efficient reconstruction under the telecentric system. We verified our approach using simulations and experiments, and the results indicate that our approach can allow the amplitude and phase reconstruction with high resolution in a single shot.

Domain adaptation using AdaBN and AdaIN for high-resolution IVD mesh reconstruction from clinical MRI.

Deep learning has firmly established its dominance in medical imaging applications. However, careful consideration must be exercised when transitioning a trained source model to adapt to an entirely distinct environment that deviates significantly from the training set. The majority of the efforts to mitigate this issue have predominantly focused on classification and segmentation tasks. In this work, we perform a domain adaptation of a trained source model to reconstruct high-resolution intervertebral disc meshes from low-resolution MRI. To address the outlined challenges, we use MRI2Mesh as the shape reconstruction network. It incorporates three major modules: image encoder, mesh deformation, and cross-level feature fusion. This feature fusion module is used to encapsulate local and global disc features. We evaluate two major domain adaptation techniques: adaptive batch normalization (AdaBN) and adaptive instance normalization (AdaIN) for the task of shape reconstruction. Experiments conducted on distinct datasets, including data from different populations, machines, and test sites demonstrate the effectiveness of MRI2Mesh for domain adaptation. MRI2Mesh achieved up to a 14% decrease in Hausdorff distance (HD) and a 19% decrease in the point-to-surface (P2S) metric for both AdaBN and AdaIN experiments, indicating improved performance. MRI2Mesh has demonstrated consistent superiority to the state-of-the-art Voxel2Mesh network across a diverse range of datasets, populations, and scanning protocols, highlighting its versatility. Additionally, AdaBN has emerged as a robust method compared to the AdaIN technique. Further experiments show that MRI2Mesh, when combined with AdaBN, holds immense promise for enhancing the precision of anatomical shape reconstruction in domain adaptation.

Visual State Space Model for Image Deraining with Symmetrical Scanning

Image deraining aims to mitigate the adverse effects of rain streaks on image quality. Recently, the advent of convolutional neural networks (CNNs) and Vision Transformers (ViTs) has catalyzed substantial advancements in this field. However, these methods fail to effectively balance model efficiency and image deraining performance. In this paper, we propose an effective, locally enhanced visual state space model for image deraining, called DerainMamba. Specifically, we introduce a global-aware state space model to better capture long-range dependencies with linear complexity. In contrast to existing methods that utilize fixed unidirectional scan mechanisms, we propose a direction-aware symmetrical scanning module to enhance the feature capture of rain streak direction. Furthermore, we integrate a local-aware mixture of experts into our framework to mitigate local pixel forgetting, thereby enhancing the overall quality of high-resolution image reconstruction. Experimental results validate that the proposed method surpasses state-of-the-art approaches on six benchmark datasets.

Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time–photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time–photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.

Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor.

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time-photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time-photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.

Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T.

The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms. This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria. The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions (P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality (P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration (P = 0.087), image noise of T1-weighted images at 2-fold acceleration (P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration (P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions (P ≥ 0.082). This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.

MICOS Complex Loss Governs Age-Associated Murine Mitochondrial Architecture and Metabolism in the Liver, While Sam50 Dictates Diet Changes.

The liver, the largest internal organ and a metabolic hub, undergoes significant declines due to aging, affecting mitochondrial function and increasing the risk of systemic liver diseases. How the mitochondrial three-dimensional (3D) structure changes in the liver across aging, and the biological mechanisms regulating such changes confers remain unclear. In this study, we employed Serial Block Face-Scanning Electron Microscopy (SBF-SEM) to achieve high-resolution 3D reconstructions of murine liver mitochondria to observe diverse phenotypes and structural alterations that occur with age, marked by a reduction in size and complexity. We also show concomitant metabolomic and lipidomic changes in aged samples. Aged human samples reflected altered disease risk. To find potential regulators of this change, we examined the Mitochondrial Contact Site and Cristae Organizing System (MICOS) complex, which plays a crucial role in maintaining mitochondrial architecture. We observe that the MICOS complex is lost during aging, but not Sam50. Sam50 is a component of the sorting and assembly machinery (SAM) complex that acts in tandem with the MICOS complex to modulate cristae morphology. In murine models subjected to a high-fat diet, there is a marked depletion of the mitochondrial protein SAM50. This reduction in Sam50 expression may heighten the susceptibility to liver disease, as our human biobank studies corroborate that Sam50 plays a genetically regulated role in the predisposition to multiple liver diseases. We further show that changes in mitochondrial calcium dysregulation and oxidative stress accompany the disruption of the MICOS complex. Together, we establish that a decrease in mitochondrial complexity and dysregulated metabolism occur with murine liver aging. While these changes are partially be regulated by age-related loss of the MICOS complex, the confluence of a murine high-fat diet can also cause loss of Sam50, which contributes to liver diseases. In summary, our study reveals potential regulators that affect age-related changes in mitochondrial structure and metabolism, which can be targeted in future therapeutic techniques.

SeisResoDiff: Seismic resolution enhancement based on a diffusion model

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model — denoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective’s derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.

Motion robust coronary MR angiography using zigzag centric ky-kz trajectory and high-resolution deep learning reconstruction.

To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric ky-kz k-space trajectory combined with high-resolutiondeep learning reconstruction (HR-DLR). All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric ky-kz trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables. The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR. Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput.

Development and validation of an ex vivo porcine model of functional tricuspid regurgitation

Background and Aim: Ex vivo models of functional tricuspid regurgitation (FTR) are needed for pre-clinical testing of novel surgical and interventional repair strategies, but current options are costly or have not been formally validated. The objective of this research was to create and validate an ex vivo model to test novel repair methods for FTR. Methods: In explanted porcine hearts, the right atrium was excised to visualize the tricuspid valve. The pulmonary artery and aorta were clamped and cannulated, the coronary arteries ligated, and the right and left ventricles statically pressurized with air to 30 mmHg and 120 mmHg, respectively. FTR was induced by increasing right ventricular pressure to 80 mmHg for 3 h, which resulted in progressive tricuspid annular enlargement, right ventricular dilation, papillary muscle displacement, and central tricuspid malcoaptation. A structured light scanner was used to image the 3D topography of the tricuspid valve in both the native and FTR state, and images were exported into scan-to-computer-aided design software, which allowed for high-resolution 3D computational reconstruction. Relevant geometric measurements were sampled including annular circumference and area, major and minor axis diameter, and tenting height, angle, and area. Geometric measurements from the ex vivo model were compared to clinical transthoracic echocardiographic (TTE) measurements using two-sample t-tests. Results: A total of 12 porcine hearts were included in the study. Annular measurements of the native valve were comparable to published TTE data, except for the minor axis diameter, which was shorter in the ex vivo model (2.5 vs. 3.1 cm, P = 0.007). Induction of FTR in the ex vivo model resulted in annular enlargement (FTR vs. native: circumference 13.7 vs.11.8 cm, P = 0.012; area 14 vs. 11 cm2, P = 0.011). Ex vivo leaflet measurements in both the native and FTR model differed from published TTE data, but demonstrated comparable directional changes between the native and regurgitant states, including increased tenting height, area, and volume. Conclusion: The ex vivo pneumatically-pressurized porcine model closely recapitulates the geometry of both the native and regurgitant tricuspid valve complex in humans and holds promise for testing novel FTR repair strategies. Relevance for Patients: Currently available interventions for the tricuspid valve have a risk of permanent conduction abnormalities and are insufficient in addressing tricuspid disease for a subset of patients. This ex vivo model provides a platform for testing of novel interventions that address the deficiencies of current tricuspid therapies.

Highly Efficient Fluorescent Probe to Visualize Protein Interactions at the Superresolution.

Superresolution microscopy (SR microscopy) of protein-protein interactions (PPIs) occurring in subcellular structures is essential for understanding cellular functions. However, a powerful and useful technology for SR microscopy of PPIs remains elusive. Here, we develop a highly efficient photoconvertible fluorescent probe, named split-Dendra2, for SR microscopy of PPIs in the cell. We found that split-Dendra2 enables a highly efficient detection of PPIs, making it possible to perform SR microscopy of PPIs with high spatial resolution and high image reconstruction fidelity. We demonstrate the utility of split-Dendra2 by visualizing PPIs occurring in small subcellular structures at the superresolution, such as clathrin-coated pits and focal adhesions, which cannot be visualized by the existing tools. Split-Dendra2 offers a powerful and useful tool that greatly expands the possibility of SR microscopy and can contribute to revealing the function of PPIs at the nanoscale resolution.

A high contrast and resolution reconstruction algorithm in quantitative photoacoustic tomography

A framework for reconstruction of optical diffusion and absorption coefficients in quantitative photoacoustic tomography is presented. This framework is based on a Tikhonov-type functional with a regularization term promoting sparsity of the absorption coefficient and a prior involving a Kubelka–Munk absorption–diffusion relation that allows to obtain superior reconstructions. The reconstruction problem is formulated as the minimization of this functional subject to the differential constraint given by a photon-propagation model. The solution of this problem is obtained by a fast and robust sequential quadratic hamiltonian algorithm based on the Pontryagin maximum principle. Results of several numerical experiments demonstrate that the proposed computational strategy is able to obtain reconstructions of the optical coefficients with high contrast and resolution for a wide variety of objects.

PP-ISEA: An Efficient Algorithm for High-Resolution Three-Dimensional Geometry Reconstruction of Space Targets Using Limited Inverse Synthetic Aperture Radar Images.

As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious steps associated with factorization methods. Nevertheless, ISEA's neglect of valid information necessitates a high quantity of images and elongated operation times. This paper introduces a partitioned parallel 3D reconstruction method utilizing sorted-energy semi-accumulation with ISAR image sequences (PP-ISEA) to address these limitations. The PP-ISEA innovatively incorporates a two-step search pattern-coarse and fine-that enhances search efficiency and conserves computational resources. It introduces a novel objective function 'sorted-energy semi-accumulation' to discern genuine scatterers from spurious ones and establishes a redundant point exclusion module. Experiments on the scatterer model and simulated electromagnetic model demonstrate that the PP-ISEA reduces the minimum image requirement from ten to four for high-quality scatterer model reconstruction, thereby offering superior reconstruction quality in less time.

Asteroid-NeRF: A deep-learning method for 3D surface reconstruction of asteroids

Asteroids preserve important information about the origin and evolution of the Solar System. Three-dimensional (3D) surface models of asteroids are essential for exploration missions and scientific research. Regular methods for 3D surface reconstruction of asteroids, such as stereo-photogrammetry (SPG), usually struggle to reconstruct textureless areas and can only generate sparse surface models. Stereo-photoclinometry (SPC) can reconstruct pixel-wise topographic details but its performance depends on the availability of images obtained under different illumination conditions and suffers from uncertainties related to surface reflectance and albedo. This paper presents Asteroid-NeRF, a novel deep-learning method for 3D surface reconstruction of asteroids that is based on the state-of-the-art neural radiance field (NeRF) method. Asteroid-NeRF uses a signed distance field (SDF) to reconstruct a 3D surface model from multi-view posed images of an asteroid. In addition, Asteroid-NeRF incorporates appearance embedding to adapt to different illumination conditions and to maintain the geometric consistency of a reconstructed surface, allowing it to deal with the different solar angles and exposure conditions commonly seen in asteroid images. Moreover, Asteroid-NeRF incorporates multi-view photometric consistency to constrain the SDF, enabling optimised reconstruction. Experimental evaluations using actual images of asteroids Itokawa and Bennu demonstrate the promising performance of Asteroid-NeRF, complementing traditional methods such as SPG and SPC. Furthermore, due to the global consistency and pixel-wise training of Asteroid-NeRF, it produces highly detailed surface reconstructions. Asteroid-NeRF offers a new and effective solution for high-resolution 3D surface reconstruction of asteroids that will aid future exploratory missions and scientific research on asteroids.

Mitigating the Blurring Effect of CryoEM Averaging on a Flexible and Highly Symmetric Protein Complex through Sub-Particle Reconstruction.

Many macromolecules are inherently flexible as a feature of their structure and function. During single-particle CryoEM processing, flexible protein regions can be detrimental to high-resolution reconstruction as signals from thousands of particles are averaged together. This "blurring" effect can be difficult to overcome and is possibly more pronounced when averaging highly symmetric complexes. Approaches to mitigating flexibility during CryoEM processing are becoming increasingly critical as the technique advances and is applied to more dynamic proteins and complexes. Here, we detail the use of sub-particle averaging and signal subtraction techniques to precisely target and resolve flexible DARPin protein attachments on a designed tetrahedrally symmetric protein scaffold called DARP14. Particles are first aligned as full complexes, and then the symmetry is reduced by alignment and focused refinement of the constituent subunits. The final reconstructions we obtained were vastly improved over the fully symmetric reconstructions, with observable secondary structure and side-chain placement. Additionally, we were also able to reconstruct the core region of the scaffold to 2.7 Å. The data processing protocol outlined here is applicable to other dynamic and symmetric protein complexes, and our improved maps could allow for new structure-guided variant designs of DARP14.

Changes in monsoon precipitation in East Asia under a 2°C interglacial warming.

Past intervals of warming provide the unique opportunity to observe how the East Asia monsoon precipitation response happened in a warming world. However, the available evaluations are primarily limited to the last glacial-to-interglacial warming, which has fundamental differences from the current interglacial warming, particularly in changes in ice volume. Comparative paleoclimate studies of earlier warm interglacial periods can provide more realistic analogs. Here, we present high-resolution quantitative reconstructions of temperature and precipitation from north-central China over the past 800 thousand years. We found that the average precipitation increase, estimated by the interglacial data, was only around one-half of that estimated for the glacial-to-interglacial data, which is attributed to the amplification of climate change by ice volume variations. Analysis of the interglacial data suggests an increase in monsoon precipitation of ~100 mm for a warming level of 2°C on the Chinese Loess Plateau.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.