High-resolution Reconstruction Research Articles

Substructures within Substructures in the Complex Postmerging System A514 Unveiled by High-resolution Magellan/Megacam Weak Lensing

Abell 514 (A514) at z = 0.071 is an intriguing merging system exhibiting highly elongated (∼1 Mpc) X-ray features and three large-scale (300 ∼ 500 kpc) bent radio jets. To dissect this system with its multiwavelength data, it is critical to robustly identify and quantify its dark matter substructures. We present a weak-lensing analysis of A514 using deep Magellan/Megacam observations. Combining two optical band filter imaging data obtained under optimal seeing (∼0.″6) and leveraging the proximity of A514, we achieve a high source density of ∼46arcmin−2 or ∼6940 Mpc−2, which enables high-resolution mass reconstruction. We unveil the complex dark matter substructures of A514, which are characterized by the NW and SE subclusters separated by ∼0.7 Mpc, each exhibiting a bimodal mass distribution. The total mass of the NW subcluster is estimated to be M200cNW=1.08−0.22+0.25×1014M⊙ and is further resolved into the eastern ( M200cNWE=2.6−1.1+1.4×1013M⊙) and western ( M200cNWW=7.1−2.0+2.3×1013M⊙ ) components. The mass of the SE subcluster is M200cSE=1.55−0.26+0.28×1014M⊙ , which is also further resolved into the northern ( M200cSEN=2.9−1.3+1.8×1013M⊙ ) and southern ( M200cSES=8.5−2.6+3.1×1013M⊙ ) components. These four substructures coincide with the A514 brightest galaxies and are detected with significances ranging from 3.3σ to 4.7σ. Comparison of the dark matter substructures with the X-ray distribution suggests that A514 might have experienced an off-axis collision, and the NW and SE subclusters are currently near their apocenters.

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.

Historic ocean acidification of Loch Sween revealed by correlative geochemical imaging and high-resolution boron isotope analysis of Boreolithothamniom cf. soriferum

Ocean Acidification (OA) arises from the increase in atmospheric carbon dioxide concentration following the industrial revolution. The ecological and socio-economic consequences of OA were first identified around 10–15 years ago but remain poorly understood. This is particularly true in coastal regions where local processes can have dramatic consequences on pH trends through time, obscuring and compounding the long-term effects from rising atmospheric CO2. Here we explore the possibility of generating long records of coastal ocean pH using the skeletons of widely distributed coralline algae (CA). The skeletons of these slow growing (<1 mm/year) taxa often contain micron-scale heterogeneities, making sampling for high-resolution climate reconstructions using bulk sampling techniques difficult. Here we use laser ablation coupled to inductively coupled plasma mass spectrometers to generate high-resolution 2D images of the element/calcium ratios and boron isotope composition (δ11B) of a sample of Boreolithothamniom cf. soriferum from Loch Sween in Scotland, UK where we have been monitoring temperature since 2004 and pH during 2014. By carefully correlating the geochemical images with a scanning electron microscopy image we can segment them to remove the marginal portions of the skeleton, isolating the central growth axis to generate an age model and growth rate. The δ11B-pH is significantly elevated above the seawater pH in Loch Sween (8.4 to 8.9 vs. 7.9 to 8.1) consistent with other CA that show internal pH elevation. On a seasonal scale, internal pH is negatively correlated with temperature and also exhibits a long-term decline. By removing this temperature effect, internal pH can be correlated to seawater pH during the 2014 monitoring period allowing us to reconstruct a seawater acidification trend from 2004 to 2018 of -0.018 pH units per year, 10x higher than open ocean trends but consistent with contemporaneous monitoring efforts of UK coastal waters. Reconstructed aqueous CO2 suggests that prior to ∼2008 Loch Sween was a sink of CO2 but after this date, particularly during the early summer, it was a substantial CO2 source. Comparison of reconstructed aqueous CO2 with a record of calcification rate of our sample of Boreolithothamniom cf. soriferum suggests this acidification and associated rise in local seawater pCO2 may have freed this sample from carbon limitation leading to a recent increase in calcification.

Snapshot coherent diffraction imaging across ultra-broadband spectra

Ultrafast imaging simultaneously pursuing high temporal and spatial resolution is a key technique to study the dynamics in the microscopic world. However, the broadband spectra of ultra-short pulses bring a major challenge to traditional coherent diffraction imaging (CDI), as they result in an indistinct diffraction pattern, thereby complicating image reconstruction. To address this, we introduce, to our knowledge, a new ultra-broadband coherent imaging method, and empirically demonstrate its efficacy in facilitating high-resolution and rapid image reconstruction of achromatic objects. The existing full bandwidth limitation for snapshot CDI is enhanced to ∼60% experimentally, restricted solely by our laser bandwidth. Simulations indicate the applicability of our method for CDI operations with a bandwidth as high as ∼140%, potentially supporting ultrafast imaging with temporal resolution into ∼50-attosecond scale. Even deployed with a comb-like harmonic spectrum encompassing multiple octaves, our method remains effective. Furthermore, we establish the capability of our approach in reconstructing a super-broadband spectrum for CDI applications with high fidelity. Given these advancements, we anticipate that our method will contribute significantly to attosecond imaging, thereby advancing cutting-edge applications in material science, quantum physics, and biological research.

High-Resolution Reconstruction of a C. elegans Ribosome Sheds Light on Evolutionary Dynamics and Tissue Specificity.

Caenorhabditis elegans is an important model organism for human health and disease, with foundational contributions to the understanding of gene expression and tissue patterning in animals. An invaluable tool in modern gene expression research is the presence of a high-resolution ribosome structure, though no such structure exists for C. elegans. Here we present a high-resolution single-particle cryogenic electron microscopy (cryoEM) reconstruction and molecular model of a C. elegans ribosome, revealing a significantly streamlined animal ribosome. Many facets of ribosome structure are conserved in C. elegans, including overall ribosomal architecture and the mechanism of cycloheximide, while other facets such as expansion segments and eL28 are rapidly evolving. We identify uL5 and uL23 as two instances of tissue-specific ribosomal protein paralog expression conserved in Caenorhabditis, suggesting that C. elegans ribosomes vary across tissues. The C. elegans ribosome structure will provide a basis for future structural, biochemical, and genetic studies of translation in this important animal system.

Deep learning for the super resolution of Mediterranean sea surface temperature fields

Abstract. Sea surface temperature (SST) is one of the essential variables of the Earth's climate system. Being at the air–sea interface, SST modulates heat fluxes in and out of the ocean, provides insight into several upper and interior ocean dynamical processes, and is a fundamental indicator of climate variability potentially impacting the health of marine ecosystems. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide much better coverage than what is achievable from in situ platforms, they cannot sense the sea surface under cloudy and rainy conditions. Large gaps are present even in merged multi-sensor satellite products, and different statistical strategies, mostly based on optimal interpolation (OI) algorithms, have thus been proposed to obtain gap-free (L4) images. These techniques, however, filter out the signals below the space–time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single-image super resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging convolutional neural networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100° spatial resolution starting from 1/16° data over the Mediterranean Sea. The application of this technique shows an improvement in the high-resolution reconstruction, capturing small-scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data.

High-resolution in vivo 4D-OCT fish-eye imaging using 3D-UNet with multi-level residue decoder.

Optical coherence tomography (OCT) allows high-resolution volumetric imaging of biological tissues in vivo. However, 3D-image acquisition often suffers from motion artifacts due to slow frame rates and involuntary and physiological movements of living tissue. To solve these issues, we implement a real-time 4D-OCT system capable of reconstructing near-distortion-free volumetric images based on a deep learning-based reconstruction algorithm. The system initially collects undersampled volumetric images at a high speed and then upsamples the images in real-time by a convolutional neural network (CNN) that generates high-frequency features using a deep learning algorithm. We compare and analyze both dual-2D- and 3D-UNet-based networks for the OCT 3D high-resolution image reconstruction. We refine the network architecture by incorporating multi-level information to accelerate convergence and improve accuracy. The network is optimized by utilizing the 16-bit floating-point precision for network parameters to conserve GPU memory and enhance efficiency. The result shows that the refined and optimized 3D-network is capable of retrieving the tissue structure more precisely and enable real-time 4D-OCT imaging at a rate greater than 10 Hz with a root mean square error (RMSE) of ∼0.03.

Enhancing the Resolution of Satellite Ocean Data Using Discretized Satellite Gridding Neural Networks

Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets.

Effects of healthy aging on tongue-jaw kinematics during feeding behavior in rhesus macaques.

Several age-related oral health problems have been associated with neurodegenerative diseases such as Alzheimer's Disease (AD), yet how oromotor dysfunction in healthy aging differ from those found in pathological aging is still unknown. This is partly because changes in the cortical and biomechanical ("neuromechanical") control of oromotor behavior in healthy aging are poorly understood. To this end, we investigated the natural feeding behavior of young and aged rhesus macaques (Macaca mulatta) to understand the age-related differences in tongue and jaw kinematics. We tracked tongue and jaw movements in 3D using high-resolution biplanar videoradiography and X-ray Reconstruction of Moving Morphology (XROMM). Older subjects exhibited a reduced stereotypy in tongue movements during chews and a greater lag in tongue movements relative to jaw movements compared to younger subjects. Overall, our findings reveal age-related changes in tongue and jaw kinematics, which may indicate impaired tongue-jaw coordination. Our results have important implications for the discovery of potential neuromechanical biomarkers for early diagnosis of AD.

A time window averaging method to mitigate the impact of shell growth trends on Tridacna δ18O records

Tridacna shells have become valuable archives for high-resolution paleoclimate reconstructions due to their daily-to-annual bands. The δ18O, influenced by temperature and salinity, serves as a key geochemical proxy for Tridacna. Traditionally, linear interpolation has been the go-to method for creating evenly spaced Tridacna δ18O timeseries. However, the accuracy of this method can be influenced by the prominent negative shell growth trends. Given that Tridacna shells exhibit significantly faster growth rates during the juvenile stage than in adulthood, constant sampling intervals may lead to overly dense sampling during the juvenile phase, which could potentially introduce errors in the linearly interpolated Tridacna δ18O record. In this study, we proposed a time window averaging method to mitigate the influence of growth trends in Tridacna δ18O records from the northern South China Sea (SCS). The time window averaging method calculates the average of measured Tridacna δ18O data within a time window, whose length is determined by the sampling resolution for each year and the expected temporal resolution. Pseudo-Tridacna experiments, using the newly developed proxy system model for Tridacna δ18O, were conducted to assess the accuracy of both interpolation and averaging methods. Results indicate that the averaging method outperforms the interpolation method, especially in seasonality estimation. The pseudo-Tridacna experiments recommend employing the averaging method for monthly Tridacna δ18O records when sampling density exceeds ∼20 samples/year. Two real Tridacna δ18O records from the northern SCS demonstrate consistency with pseudo-Tridacna experiments, supporting the efficacy of the averaging method in reducing bias in Tridacna δ18O records associated with growth trends. Our results show the potential of the time window averaging method to improve Tridacna-based paleoclimate reconstructions.

Fast radiance field reconstruction from sparse inputs

Neural Radiance Field (NeRF) has emerged as a powerful method in data-driven 3D reconstruction because of its simplicity and state-of-the-art performance. However, NeRF requires densely captured calibrated images and lengthy training and rendering time to realize high-resolution reconstruction. Thus, we propose a fast radiance field reconstruction method from a sparse set of images with silhouettes. Our approach integrates NeRF with Shape from Silhouette, a traditional 3D reconstruction method that uses silhouette information to fit the shape of an object. To combine NeRF’s implicit representation with Shape from Silhouette’s explicit representation, we propose a novel explicit–implicit radiance field representation consisting of voxel grids with confidence and feature embedding for geometry and a multilayer perceptron network to decode view-dependent color emission for appearance. We propose to make the reconstructed geometry compact by taking advantage of silhouette images, which can avoid the majority of artifacts in sparse input scenarios and speed up training and rendering. We also apply voxel dilating and pruning to refine the geometry prediction. In addition, we impose a total variation regularization on our model to encourage a smooth radiance field. Experiments on the DTU and the NeRF-Synthetic datasets show that our algorithm surpasses the existing baselines in terms of efficiency and accuracy.

STDAN: Deformable Attention Network for Space-Time Video Super-Resolution.

The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low-frame-rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synthesize the missing frame embedding, which cannot fully explore the information flow of consecutive input LR frames. In addition, existing STVSR models hardly exploit the temporal contexts explicitly to assist high-resolution (HR) frame reconstruction. To address these issues, in this article, we propose a deformable attention network called STDAN for STVSR. First, we devise a long short-term feature interpolation (LSTFI) module that is capable of excavating abundant content from more neighboring input frames for the interpolation process through a bidirectional recurrent neural network (RNN) structure. Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction. Experimental results on several datasets demonstrate that our approach outperforms state-of-the-art STVSR methods. The code is available at https://github.com/littlewhitesea/STDAN.

High-resolution Holographic Image Reconstruction Based on Deep Learning

High-resolution Holographic Image Reconstruction Based on Deep Learning

Accelerating quad Airy beams-based point response for interferenceless coded aperture correlation holography.

Interferenceless-coded aperture correlation holography (I-COACH) is a promising single-shot 3D imaging method in which a coded phase mask (CPM) is used to encode 3D information about an object into an intensity distribution. However, conventional CPM encoding methods usually lead to intensity dilution, especially in the recording of point spread holograms (PSHs), resulting in low-resolution reconstruction of I-COACH. Here, we propose accelerating quad Airy beams with four mainlobes as a point response to enable weak diffraction propagation and a sharp maximum intensity in the transverse direction. Moreover, the four mainlobes exhibit lateral acceleration in 3D space, so the PSHs in different axial positions show a unique and concentrated intensity distribution on the image sensor, thereby realizing a high-resolution reconstruction of I-COACH. Compared with conventional CPM encoding methods, the proposed accelerating quad Airy-beam-encoding method has superior performance in improving the resolution of I-COACH reconstruction even in the presence of external interference.

High-resolution yeast actin structures indicate the molecular mechanism of actin filament stiffening by cations

Actin filament assembly and the regulation of its mechanical properties are fundamental processes essential for eukaryotic cell function. Residue E167 in vertebrate actins forms an inter-subunit salt bridge with residue K61 of the adjacent subunit. Saccharomyces cerevisiae actin filaments are more flexible than vertebrate filaments and have an alanine at this position (A167). Substitution of this alanine for a glutamic acid (A167E) confers Saccharomyces cerevisiae actin filaments with salt-dependent stiffness similar to vertebrate actins. We developed an optimized cryogenic electron microscopy workflow refining sample preparation and vitrification to obtain near-atomic resolution structures of wild-type and A167E mutant Saccharomyces cerevisiae actin filaments. The difference between these structures allowed us to pinpoint the potential binding site of a filament-associated cation that controls the stiffness of the filaments in vertebrate and A167E Saccharomyces cerevisiae actins. Through an analysis of previously published high-resolution reconstructions of vertebrate actin filaments, along with a newly determined high-resolution vertebrate actin structure in the absence of potassium, we identified a unique peak near residue 167 consistent with the binding of a magnesium ion. Our findings show how magnesium can contribute to filament stiffening by directly bridging actin subunits and allosterically affecting the orientation of the DNase-I binding loop of actin, which plays a regulatory role in modulating actin filament stiffness and interactions with regulatory proteins.

Infrared remote-sensing image super-resolution based on physical characteristic deduction

In this paper, we propose a high-confidence, super-resolution method for short-wave infrared remote-sensing images based on characteristic deduction form high-resolution visible images. First, based on the radiative transmission mechanism of full remote-sensing imaging link, a ground object characterization model based on the dark object method was established to achieve the inversion of the object’s ground reflection characteristics of high-resolution visible images. Second, the reflectance information of the specified infrared band can be deduced combined with the reflectance matching of the typical ground object spectral library. On this basis, combined with the characterization modeling of remote-sensing imaging link—such as atmosphere, optical system, platform, and detector—a high-resolution infrared remote-sensing image reconstruction method is proposed. Based on the imaging degradation model between high- and low-resolution infrared images, the results of high-resolution reconstruction were corrected using measured infrared images. Lastly, the final fusion-super-resolution image can be obtained. The results show that the proposed fusion-super-resolution method yields high-resolution infrared images more realistically than traditional image fusion algorithms and shows better structural similarity index and visual information fidelity results, verifying the effectiveness of the proposed infrared remote-sensing image fusion-super-resolution method.

Tropical cyclone activity over the past 1200 years at the Pelican Cays, Belize

Tropical cyclone (TC) models indicate that continued planet warming will likely increase the global proportion of powerful TCs (specifically Categories 4 and 5 hurricanes), increasingly jeopardizing low-lying coastal communities and resources such as the Pelican Cays, Belize. The combination of increased coastal development and continued relative sea-level rise puts these communities at even higher risk of damage from TCs. The short TC observational record for the western Caribbean hampers the extensive study of TC activity on centennial timescales, which hinders our ability to fully understand past TC climatology and improve the accuracy of TC models. To better assess TC risk, paleotempestological studies are necessary to put future scenarios in perspective. Here, we present a high-resolution reconstruction of coarser-grained sediment deposits associated with TC (predominately ≥ Category 2 hurricanes) passages over the past 1200 years from Elbow and Lagoon Cays, two coral reef-bounded lagoons at the northern and southern end of the Pelican Cays; the most southern Belizean paleotempestological site to date. Coincident timing of historic storms with statistically significant coarser-grained deposits within cay lagoon sediment cores allows us to determine which historic TCs likely generated event layers (tempestites) archived in the sediment record. Our compilation frequency analysis indicates one active interval (above-normal TC activity) from 1740-1950 CE and one quiet interval (below-normal TC activity) from 850-1018 CE. The active and quiet intervals in the Pelican Cays composite record are anticorrelated with those from nearby and re-analyzed TC records to the north, including the Great Blue Hole (∼100 km north) and the Northeast Yucatan (∼380 km northwest). This site-specific anticorrelation in TC activity along the western Caribbean indicates that we cannot rely on any one single TC record to represent regional TC activity. However, we cannot discount that these anticorrelated periods between the western Caribbean sites are due to randomness. To confirm that the anticorrelation in TC activity among sites from the western Caribbean is indeed a function of climate change and not randomness, an integration of more records and TC model simulations over the past millennium is necessary to assess the significance of centennial-scale variability in TC activity recorded in reconstructions from the western Caribbean.

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 in the variances of epidural spinal recordings

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.