Coregistration Errors Research Articles

Ground moving target indication of polarimetric interferometric synthetic aperture radar using joint scattering vector

AbstractGround moving target indication (GMTI) plays a critical role in both civilian applications and military applications. The accuracy of complex images coregistration is a major factor for multichannel synthetic aperture radar (SAR) GMTI. Moreover, polarimetric interferometric SAR (PolInSAR) represents a growing strength in multi‐function radar systems. In response to this, we propose a new SAR‐GMTI approach tailored for the PolInSAR system. To address the deterioration in clutter suppression performance caused by the coregistration errors, we construct a joint scattering vector (JSV) for polarimetric SAR (PolSAR) by incorporating the neighbouring vectors of the central pixel. Subsequently, the covariance matrix of JSV is estimated and subjected to eigen‐decomposed. Clutter suppression of the PolInSAR system for GMTI is performed using eigen‐subspace projection, where the JSV is projected onto noise subspace. This method is robust against coregistration errors since all ground moving target information is encapsulated within the JSV. Furthermore, the improvement factors with different clutter backgrounds (by changing the signal‐to‐clutter‐plus‐noise‐ratio, SCNR) are analysed. The effectiveness of the proposed JSV‐based method is validated using simulated PolInSAR data.

Performance Characteristics of a New Generation 148-cm Axial Field-of-View uMI Panorama GS PET/CT System with Extended NEMA NU 2-2018 and EARL Standards.

The uMI Panorama GS PET/CT system is a new long-axial-field-of-view scanner featuring high sensitivity, time-of-flight (TOF) resolution, spatial resolution, and count rate performance. The aim of this study is to assess the PET system on the basis of the National Electrical Manufacturers Association (NEMA) NU 2-2018 and European Association of Nuclear Medicine Research Limited (EARL) standards. Methods: Spatial resolution, count rate performance, sensitivity, accuracy, image quality, TOF resolution, and coregistration accuracy were evaluated following the NEMA NU 2-2018 standard. Additional experiments included energy resolution, 200-cm-long line sources for sensitivity, a 175-cm-long scatter phantom for count rate and TOF resolution, as well as the compliance with the EARL guideline. Moreover, an 18F-FDG PET patient study was reconstructed with various frame durations. Results: The PET system achieved sub-3-mm transaxial and axial spatial resolutions at a 1-cm radial offset. The sensitivities with the 70-cm-long and 200-cm-long line sources were observed to be 176.3 and 90.8 kcps/MBq, respectively, at the center of the field of view. The noise-equivalent count rates (NECRs) of the 70-cm-long and 175-cm-long scatter phantoms were measured to be 3.35 Mcps at 57.57 kBq/mL and 2.24 Mcps at 33.27 kBq/mL, respectively. The TOF resolutions for both phantoms were approximately 189 ps at 5.3 kBq/mL and lower than 200 ps below the NECR peaks. The absolute count rate errors of all 34 acquisitions were less than 3% below the NECR peak for the 70-cm-long scatter phantom. With the standard NEMA image quality phantom experiment, the contrast recovery coefficient varied from 68.17% to 94.20% and the background variabilities were all below 2%. The maximum PET/CT coregistration error was 1.33 mm. Regarding EARL compliance, the gaussian filter of 5-mm full width at half maximum could produce acceptable images. The patient data demonstrate visually satisfactory image quality with short frames (less than 1 min). Conclusion: The uMI Panorama GS exhibits spatial resolution and TOF resolution similar to those of the uMI Panorama system (35-cm axial field of view), despite the extended axial field of view. The 148-cm axial coverage, sub-200-ps TOF resolution, high sensitivity, and count rate performances are expected to yield superior image quality and offer new opportunities for various clinical applications.

Method for co-registration of high-resolution specimen PET-CT with histopathology to improve insight into radiotracer distributions

BackgroundAs the spatial resolution of positron emission tomography (PET) scanners improves, understanding of radiotracer distributions in tissues at high resolutions is important. Hence, we propose a method for co-registration of high-resolution ex vivo specimen PET images, combined with computed tomography (CT) images, and the corresponding specimen histopathology.MethodsWe applied our co-registration method to breast cancer (BCa) specimens of patients who were preoperatively injected with 0.8 MBq/kg [18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F]fluorodeoxyglucose ([18F]FDG). The method has two components. First, we used an image acquisition scheme that minimises and tracks tissue deformation: (1) We acquired sub-millimetre (micro)-PET-CT images of ±2 mm-thick lamellas of the fresh specimens, enclosed in tissue cassettes. (2) We acquired micro-CT images of the same lamellas after formalin fixation to visualise tissue deformation. (3) We obtained 1 hematoxylin and eosin (H&E) stained histopathology section per lamella of which we captured a digital whole slide image (WSI). Second, we developed an automatic co-registration algorithm to improve the alignment between the micro-PET-CT images and WSIs, guided by the micro-CT of the fixated lamellas. To estimate the spatial co-registration error, we calculated the distance between corresponding microcalcifications in the micro-CTs and WSIs. The co-registered images allowed to study standardised uptake values (SUVs) of different breast tissues, as identified on the WSIs by a pathologist.ResultsWe imaged 22 BCa specimens, 13 cases of invasive carcinoma of no special type (NST), 6 of invasive lobular carcinoma (ILC), and 3 of ductal carcinoma in situ (DCIS). While the cassette framework minimised tissue deformation, the best alignment between the micro-PET-CT images and WSIs was achieved after deformable co-registration. We found an overall average co-registration error of 0.74 ± 0.17 mm between the micro-PET images and WSIs. (Pre)malignant tissue (including NST, ILC, and DCIS) generally showed higher SUVs than healthy tissue (including healthy glandular, connective, and adipose tissue). As expected, inflamed tissue and skin also showed high uptake.ConclusionsWe developed a method to co-register micro-PET-CT images of surgical specimens and WSIs with an accuracy comparable to the spatial resolution of the micro-PET images. While currently, we only applied this method to BCa specimens, we believe this method is applicable to a wide range of specimens and radiotracers, providing insight into distributions of (new) radiotracers in human malignancies at a sub-millimetre resolution.

A comparison of methods for in vivo activity and absorbed dose quantification with PET/CT following yttrium-90 radioembolization.

Yttrium-90 (90Y) positron emission tomography (PET)/computed tomography (CT) imaging is increasingly being used to perform tumor (T) and normal liver (NL) voxel dosimetry after 90Y-radioembolization (90Y-RE). Yet, the accuracy of in vivo 90Y-PET/CT imaging, subject to motion blur and co-registration inaccuracies, and 90Y-PET/CT dose quantification, subject to availability of different voxel dosimetry algorithms, are not well understood. The purpose of this study was to investigate the accuracy of 90Y-PET/CT-based activity estimates following 90Y-RE and characterize differences between 90Y-PET/CT-based voxel dosimetry algorithms. Thirty-five patients underwent 90Y-PET/CT imaging after 90Y-RE with TheraSphere. The net administered 90Y activity (Aadmin) was determined using a dose calibrator and pre- and post-procedure exposure rate measurements. The summation of image-based activity (Aimage) was extracted from perfused volume (PV) and 3D-isotropically 2-cm expanded PV contour (PV+2cm). Absorbed doses were calculated using voxel S-value (VSV), local deposition method (LDM), and LDM with known activity (LDMKA) dosimetry algorithms. Linear regression and Bland-Altman analysis quantified the relationship between Aimage and Aadmin and between mean dose estimates (DLDM, DVSV, DLDM-KA) for PV, T, and perfused NL volumes. While Aadmin and Aimage in PV were highly correlated (R2>0.95), the mean bias±standard error (SE) and (95% limits of agreement, LOA) was significantly non-zero with -22.7±4.7% (±28.4%). In PV+2cm, the mean bias±SE (±LOA) decreased to 1.3±3.4% (±18.0%) consistent with zero mean error. DLDM and DVSV were highly correlated (R2>0.99) for all volumes of interest (VOIs) and the meanbias±SE(±LOA) was 2.2±0.2% (±1.0%), 0.7±0.4% (±2.8%), and 3.2±0.5% (±2.8%) for PV, T, and NL, respectively. DLDM-KA and DVSV were correlated with R2=0.86, 0.80, and 0.86 for PV, T, and NL, respectively. The meanbias±SE(±LOA) between DLDM-KA and DVSV was significantly non-zero with -19.6±5.1% (±31.0%), -20.8±4.4% (±29.0%), and -18.1±5.3% (±31.1%) for PV, T, and NL, respectively. The summation of Aimage in PV was underestimated relative to Aadmin. Only by accounting for respiratory motion, limited spatial resolution, and PET/CT co-registration errors through VOI expansion was Aimage, on average, equal to Aadmin. The differences between DLDM and DVSV were not clinically relevant, though DLDM-KA was approximately 20% greater than DVSV. Given the high quantitative accuracy of dose calibrators and challenges associated with accurate 90Y-PET/CT quantification, LDMKA is the preferred algorithm for accurate 90Y-PET/CT-based dosimetry following 90Y-RE.

Evaluating the accuracy of binary classifiers for geomorphic applications

Abstract. Increased access to high-resolution topography has revolutionized our ability to map out fine-scale topographic features at watershed to landscape scales. As our “vision” of the land surface has improved, so has the need for more robust quantification of the accuracy of the geomorphic maps we derive from these data. One broad class of mapping challenges is that of binary classification whereby remote sensing data are used to identify the presence or absence of a given feature. Fortunately, there is a large suite of metrics developed in the data sciences well suited to quantifying the pixel-level accuracy of binary classifiers. This analysis focuses on how these metrics perform when there is a need to quantify how the number and extent of landforms are expected to vary as a function of the environmental forcing (e.g., due to climate, ecology, material property, erosion rate). Results from a suite of synthetic surfaces show how the most widely used pixel-level accuracy metric, the F1 score, is particularly poorly suited to quantifying accuracy for this kind of application. Well-known biases to imbalanced data are exacerbated by methodological strategies that calibrate and validate classifiers across settings where feature abundances vary. The Matthews correlation coefficient largely removes this bias over a wide range of feature abundances such that the sensitivity of accuracy scores to geomorphic setting instead embeds information about the size and shape of features and the type of error. If error is random, the Matthews correlation coefficient is insensitive to feature size and shape, though preferential modification of the dominant class can limit the domain over which scores can be compared. If the error is systematic (e.g., due to co-registration error between remote sensing datasets), this metric shows strong sensitivity to feature size and shape such that smaller features with more complex boundaries induce more classification error. Future studies should build on this analysis by interrogating how pixel-level accuracy metrics respond to different kinds of feature distributions indicative of different types of surface processes.

Evaluating error sources to improve precision in the co-registration of underwater 3D models

Change detection is an essential and widely used approach for investigating ecosystem dynamics. Multi-temporal 3D models increasingly underpin photogrammetry-based analyses of change for many ecologically relevant attributes. To detect change, it is necessary to accurately align 3D models collected at different times using a process referred to as co-registration. However, achieving precise co-registration is difficult in underwater habitats due to practical challenges intrinsic to surveying them. These include a lack of accurate georeferencing information, variable light, turbidity and weather conditions, and diving restrictions dictated by the diver's pressure exposure over time. Here we present an efficient co-registration workflow for 3D models that directly addresses these challenges, derived from underwater structure-from-motion methods. To test our approach, we used 3D models from across a wide range of coral reef habitats covering all those that one may encounter in shallow reefs (15 m depth and above). We then identified and empirically estimated four key sources of error: co-registration, 3D processing, image acquisition, and reference and scaling features (RSF) placement, and quantified their relative contributions to the overall error. Our proposed co-registration workflow had a mean precision of 1.37 ± 16.55 mm. Image acquisition and RSF placement errors contributed the most to the total workflow error (37% and 53%, respectively), while the contribution of co-registration and 3D processing errors was minimal (3% and 7%, respectively). As a result of our analysis, we provide ‘good practice’ guidelines to reduce errors associated with photogrammetric workflows and to facilitate efficient and reliable detection of 3D change in complex underwater ecosystems.

Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites

Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data-driven or model-driven) constitutes a key question when retrieving GAI from satellite observations.This study compares a data-driven and a model-driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The data-driven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model-driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L-systems) model.Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel-2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model-driven approach (R2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data-driven approach (R2 = 0.80, RMSE = 0.88, Accuracy = −0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground-measured GAI and the size and diversity of the training datasets limit the data-driven approach. In contrast, the model-driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values.Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R2 = 0.86, RMSE = 0.75, A = −0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R2 = 0.85, RMSE = 0.76, A = −0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both data-driven and model-driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m × 12 m (4 × 4 pixels) is recommended to minimize the co-registration errors and increase estimate precision.

SOmicsFusion: Multimodal coregistration and fusion between spatial metabolomics and biomedical imaging

We present SOmicsFusion, a software toolbox for ’fusing’ spatial omics with classical biomedical imaging modalities, capitalizing on their inherent correspondences and complementarity when characterizing the same subject. By augmenting radiological and histological images with spatially resolved molecular profiling, this fusion offers a panoramic characterization of the biochemical perturbations underlying pathological conditions, thereby advancing our understanding of diseases like brain disorders and cancers. The cornerstone of SOmicsFusion is a coregistration tool that leverages an innovative two-stage machine learning pipeline to tackle the longstanding challenge of spatially aligning data from fundamentally different modalities, priming them for subsequent fusion analysis that often requires precise pixel-wise correspondence between the datasets. Specifically, the pipeline utilizes an original dimension reduction algorithm for representational domain alignment, followed by a Deep Learning-based method for spatial domain alignment. SOmicsFusion is demonstrated using mass spectrometry imaging (MSI)-mediated spatial metabolomics and four other modalities: magnetic resonance imaging (MRI), microscopy, brain atlas, and spatial transcriptomics. By reducing coregistration errors by 38–69% compared to existing pipelines, SOmicsFusion enhances the precision of associating molecule distribution with anatomy and pathology features, ultimately leading to more statistically robust findings. Furthermore, SOmicsFusion incorporates various downstream analysis tools, including overlay visualization, spatial correlation/co-expression analysis, pansharpening, and automated anatomy annotation. These tools facilitate the extraction of biological insights that would be unattainable through individual modalities alone. For instance, the coregistration and correlation between MSI and in vivo MRI datasets unveil that the spatial heterogeneity in metabolites stems from the temporal heterogeneity in the development of cerebral ischemia-reperfusion injury.

Multitemporal SAR Images Change Detection Considering Ambiguous Co-Registration Errors: A Unified Framework

Multitemporal SAR Images Change Detection Considering Ambiguous Co-Registration Errors: A Unified Framework

Co-registration accuracy between Landsat-8 and Sentinel-2 orthorectified products

Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90.

Optical Co-Registration Method of Triaxial OPM-MEG and MRI.

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X- and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

Accuracy and precision of navigated transcranial magnetic stimulation

Objective. Transcranial magnetic stimulation (TMS) induces an electric field (E-field) in the cortex. To facilitate stimulation targeting, image-guided neuronavigation systems have been introduced. Such systems track the placement of the coil with respect to the head and visualize the estimated cortical stimulation location on an anatomical brain image in real time. The accuracy and precision of the neuronavigation is affected by multiple factors. Our aim was to analyze how different factors in TMS neuronavigation affect the accuracy and precision of the coil–head coregistration and the estimated E-field. Approach. By performing simulations, we estimated navigation errors due to distortions in magnetic resonance images (MRIs), head-to-MRI registration (landmark- and surface-based registrations), localization and movement of the head tracker, and localization of the coil tracker. We analyzed the effect of these errors on coil and head coregistration and on the induced E-field as determined with simplistic and realistic head models. Main results. Average total coregistration accuracies were in the range of 2.2–3.6 mm and 1°; precision values were about half of the accuracy values. The coregistration errors were mainly due to head-to-MRI registration with average accuracies 1.5–1.9 mm/0.2–0.4° and precisions 0.5–0.8 mm/0.1–0.2° better with surface-based registration. The other major source of error was the movement of the head tracker with average accuracy of 1.5 mm and precision of 1.1 mm. When assessed within an E-field method, the average accuracies of the peak E-field location, orientation, and magnitude ranged between 1.5 and 5.0 mm, 0.9 and 4.8°, and 4.4 and 8.5% across the E-field models studied. The largest errors were obtained with the landmark-based registration. When computing another accuracy measure with the most realistic E-field model as a reference, the accuracies tended to improve from about 10 mm/15°/25% to about 2 mm/2°/5% when increasing realism of the E-field model. Significance. The results of this comprehensive analysis help TMS operators to recognize the main sources of error in TMS navigation and that the coregistration errors and their effect in the E-field estimation depend on the methods applied. To ensure reliable TMS navigation, we recommend surface-based head-to-MRI registration and realistic models for E-field computations.

PAX (Passive-Active Crossing) Method for Sub-Millimeter Coregistration of Passive Acoustic Mapping and B-Mode Images.

Nonlinear ultrasonic emissions produced during a therapeutic ultrasound procedure can be detected, localized, and quantified through a class of methods that can be referred to as passive acoustic mapping (PAM). While a variety of PAM beamforming algorithms may be employed, they share a common limitation that a single sound speed is specified for both phase steering of array elements and for calculation of source power or energy. The specified value may be inadequate whether derived from B-mode-based metrics or literature values for constituent materials. This study employed experiments and simulations with linear and curvilinear array geometries to investigate the impact of in situ sound speed uncertainties on source localization in layered media. The data were also used to evaluate a new method for optimizing coregistration of PAM and B-mode images. Coregistration errors as large as 10 mm were observed with the curvilinear array, which also showed much greater sound speed sensitivity than the linear array. Errors with both array geometries were typically reduced to the order of 0.1 mm using the proposed optimization method regardless of beamformer choice or whether the array was calibrated. In a further step toward reliable implementation of PAM, the current work provides an approach that can help ensure that therapeutic ultrasound procedures are accurately guided by cavitation emissions.

Co-Registration Methods and Error Analysis for Four Decades (1979–2018) of Glacier Elevation Changes in the Southern Patagonian Icefield

The main goal of this paper is to compare two co-registration methods for geodetic mass balance (GMB) calculation in 28 glaciers making up the Upper Santa Cruz River basin, Southern Patagonian Icefield (SPI), from 1979 to 2018. For this purpose, geospatial data have been used as primary sources: Hexagon KH-9, ASTER, and LANDSAT optical images; SRTM digital radar elevation model; and ICESat elevation profiles. After the analyses, the two co-registration methods, namely M1, based on horizontal displacements and 3D shift vectors, and M2, based on three-dimensional transformations, turned out to be similar. The errors in the GMB were analyzed through a k index that considers, among other variables, the error in elevation change by testing four interpolation methods for filling gaps. We found that, in 63% of the cases, the relative error in elevation change contributes 90% or more to k index. The GMB throughout our study area reported that a loss value of −1.44 ± 0.15 m w. e. a−1 (−3.0 Gt a−1) and an ice thinning median of −1.38 ± 0.11 m a−1 occurred within the study period. The glaciers that showed the most negative GMB values were Upsala, with an annual elevation change median of −2.07 ± 0.18 m w. e. a−1, and Ameghino, with −2.31 ± 0.22 m w. e. a−1.

Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach

SummaryIn recent years, optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has shown potential for analyzing brain activity. It has a flexible sensor configuration and comparable sensitivity to conventional SQUID-MEG. We constructed a 32-channel OPM-MEG system and used it to measure cortical responses to median and ulnar nerve stimulations. Traditional magnetic source imaging methods tend to blur the spatial extent of sources. Accurate estimation of the spatial size of the source is important for studying the organization of brain somatotopy and for pre-surgical functional mapping. We proposed a new method called variational free energy-based spatial smoothing estimation (FESSE) to enhance the accuracy of mapping somatosensory cortex responses. A series of computer simulations based on the OPM-MEG showed better performance than the three types of competing methods under different levels of signal-to-noise ratios, source patch sizes, and co-registration errors. FESSE was then applied to the source imaging of the OPM-MEG experimental data.

Co-Registration Error Analysis and Array Calibration for OPM-MEG System

Optically pumped magnetometer (OPM-magneto- encephalography (MEG) source localization necessitates precise co-registration of the MRI and MEG sensor array. The co-registration error decreases the spatial resolution of MEG source estimation. To clarify and reduce the co-registration error, the data acquisition and co-registration processes are theoretically modeled and analyzed in this article. The results show that the error caused by the substituting of the designed sensor array model for the manufactured array in data acquisition is widely ignored. The amount of data used for transformation calculation is a decisive factor for the transformation accuracy. The error sources of co-registration in the OPM-MEG system are analyzed. The position error and orientation error of the sensitive axis of the sensor are two main error sources. In the range of 250 mm, the calibration experiment achieves a position accuracy of 2.5 mm and an orientation accuracy of 2° of the sensor sensitive axis. Real-time acquisition and correction of sensor position and orientation in the OPM-MEG array can be realized by optimizing the configuration of calibration scheme, and the accuracy of the OPM-MEG source localization can be improved.

Change Detection in VHR Imagery With Severe Co-Registration Errors Using Deep Learning: A Comparative Study

Change detection (CD) through Earth observation techniques can offer very significant information for monitoring tasks in a time-efficient manner. Very high-resolution (VHR) images can display objects in fine detail, thus making it possible to rapidly perceive isolated changes. However, this is a challenging task because of the increased within-class variance and geometric registration errors caused by different satellite view directions and angles. Lately, deep learning (DL) CD methods have proven very appealing for the CD problem because of their flexibility to combine and process different types of information along with the increased availability of higher processing power systems. Even though previous research has developed several notable DL methodologies, it has mostly focused on images with minor co-registration errors. Based on that, the goal of this study is to evaluate the performance of five state-of-the-art DL CD methods, two unsupervised and three supervised, on VHR images with severe co-registration errors. The methods are implemented on four urban European areas of versatile morphology. In addition, before applying the CD process, four popular automatic co-registration methods were evaluated because of the importance of this pre-processing step for the successful output of the CD problem. It was shown that phase correlation used on the Fourier-Mellin Transform produced the most satisfactory co-registration results and STANet detected building-related changes most successfully. Its success can be attributed to its particular attention mechanism and its training dataset. The rest of the co-registration and CD methods showed low performance.

Volumetric overestimation of small branches in 3D reconstructions of &lt;i&gt;Fraxinus excelsior&lt;/i&gt;

Terrestrial laser scanning (TLS) has been applied to estimate forest wood volume based on detailed 3D tree reconstructions from point cloud data. However, sources of uncertainties in the point cloud data (alignment and scattering errors, occlusion, foliage...) and the reconstruction algorithm type and parameterisation are known to affect the reconstruction, especially around finer branches. To better understand the impacts of these uncertainties on the accuracy of TLS-derived woody volume, high-quality TLS scans were collected in leaf-off conditions prior to destructive harvesting of two forest-grown common ash trees ( L.; diameter at breast height ~28 cm, woody volume of 732 and 868 L). We manually measured branch diameters at 265 locations in these trees. Estimates of branch diameters and tree volume from Quantitative Structure Models (QSM) were compared with these manual measurements. The accuracy of QSM branch diameter estimates decreased with smaller branch diameters. Tree woody volume was overestimated (+336 L and +392 L) in both trees. Branches measuring &lt; 5 cm in diameter accounted for 80% and 83% of this overestimation respectively. Filtering for scattering errors or improved coregistration approximately halved the overestimation. Range filtering and modified scanning layouts had mixed effects. The small branch overestimations originated primarily in limitations in scanner characteristics and coregistration errors rather than suboptimal QSM parameterisation. For TLS-derived estimates of tree volume, a higher quality point cloud allows smaller branches to be accurately reconstructed. Additional experiments need to elucidate if these results can be generalised beyond the setup of this study.Fraxinus excelsior

Effects of Different Head Models in Wearable OPM-MEG

Optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has attracted significant attention due to its flexible sensor configuration and closer proximity to the scalp. The source localization technique of MEG is used in neuroscience and clinical research. Different head models have been developed to provide a forward solution for MEG source localization. However, the effects of different head models have not yet been studied under the new OPM-MEG measurement scenario. In this simulation study, we investigated the effects of eight head models (a local sphere, a corrected-sphere, a boundary element method-based, and five finite element method (FEM)-based models, including isotropic three-, four-, and five-compartment and two anisotropic five-compartment head models) for forward and inverse solutions. In addition, we studied the effects of these eight head models in a more realistic environment, such as under sensor co-registration error and measurement noise. The results showed that the source localization error between the simplified head model and isotropic five-compartment FEM model can reach 4&#x2013;5 mm, emphasizing the necessity of using an elaborate head model. Additionally, in the comparison of forward and inverse problems, the distinction of cerebrospinal fluid (CSF) showed the most obvious improvement. Thus, a FEM head model with a CSF compartment is recommended. Further, a high level of measurement noise (&#x003E;80%, the ratio of the root-mean-square of the noise matrix to the data matrix) and high co-registration errors (rotation error&#x003E;2&#x00B0;) significantly reduced the contributions of realistic head models.

RFSDAF: A New Spatiotemporal Fusion Method Robust to Registration Errors

Spatiotemporal fusion is commonly used in remote sensing to generate fine spatial–temporal resolution images. Among most present spatiotemporal fusion methods, the registration error results in a mismatch in the reflectance and class abundance information within the MODIS pixel to be analyzed during the spatiotemporal fusion. In this article, a new spatiotemporal fusion model, robust flexible spatiotemporal data fusion (RFSDAF), is proposed; in other words, this method is robust to registration errors. The RFSDAF method uses a multiscale fusion strategy that is adaptive to different degrees of coregistration error. It incorporates multiscale information, which extends the analysis of the reflectance and class fraction from per-MODIS pixel to inter-MODIS pixels, and it is robust to coregistration errors. This method does not require high-accuracy preprocession coregistration algorithms and can automatically reduce the effect of the registration error to a great extent. The RFSDAF method is compared with four spatiotemporal image fusion algorithms, and the effectiveness of the method in resolving the registration error is demonstrated using both a simulated dataset and two actual satellite datasets. The experimental results show that the RFSDAF method can better reduce the impact of the registration error than the spatial and temporal adaptive reflectance fusion model (STARFM) and FSDAF, which adopts the phase cross-correlation algorithm to preprocess and coregister the MODIS–Landsat image through real image experiments. Consequently, the RFSDAF method has a great robustness for real spatiotemporal image fusion with registration errors and has the potential for monitoring land surface dynamics.