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The adverse effects of thawing permafrost on transportation infrastructure in northern regions are exacerbated by climate change. To address this issue, remote sensing techniques can be employed to track deformations in these structures over time. This will allow us to identify regions that are most vulnerable to permafrost degradation, and implement climate adaptation strategies accordingly. The Sentinel-2 mission provides highly suitable data for multitemporal analysis due to its high temporal resolution and multispectral coverage. However, the geometrical misalignment of Sentinel-2 imagery presents a significant challenge for such analysis. In this study, we propose an automatic sub-pixel coregistration algorithm for satellite image time series, specifically focusing on estimating the deformation of linear infrastructure in northern Canada. Our approach involves utilizing a deep learning model to generate binary masks of roads, which are then used to match and align the images. We demonstrate the feasibility of achieving sub-pixel coregistration through road alignment on a small dataset of high-resolution Sentinel-2 images from the town of Gillam in northern Canada. This represents an initial step toward training a road deformation prediction model, which can ultimately contribute to improved infrastructure resilience and adaptation to changing climatic conditions. [ABSTRACT FROM AUTHOR]

Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands. However, the computational complexity of traditional methods makes the algorithms difficult to be deployed on board satellites. This is especially true for small satellites with limited computational and power resources. Moreover, the existing band selection techniques often require the hypercube to be processed at least at Level-1B product, i.e., the bands need to be finely aligned before selecting them, demanding more computational resources for the onboard computer. This study presents a novel neural-network-based approach for onboard band selection using data with coarse band-to-band aligned. This methodology not only simplifies the preprocessing requirements but also opens new possibilities for efficient hyperspectral imaging from space onboard small satellites, such as classification, change, and target detection.

Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes. Coregistration of terrain observation by progressive scans (TOPS) data is a critical step in its application. TOPS data must be fundamentally co-registered with an accuracy of 0.001 pixels. However, various decorrelation factors due to natural vegetation and seasonal effects affect the coregistration accuracy of TOPS data. This paper proposed an enhanced spectral diversity coregistration method for dual-polarimetric (PolESD) Sentinel-1A/B TOPS data. The PolESD method suppresses speckle noise based on a unified non-local framework in dual-pol Synthetic Aperture Radar (SAR), and extracts the phase of the optimal polarization channel from the denoised polarimetric interferometric coherency matrix. Compared with the traditional ESD method developed for single-polarization data, the PolESD method can obtain more accurate coherence and phase and get more pixels for azimuth-offset estimation. In bare areas covered with low vegetation, the number of pixels selected by PolESD is more than the Boxcar method. It can also correct misregistration more effectively and eliminate phase jumps in the burst edge. Therefore, PolESD will help improve the application of TOPS data in low-coherence scenarios.

The noradrenergic locus coeruleus (LC) is one of the protein pathology epicenters in neurodegenerative diseases. In contrast to PET (positron emission tomography), MRI (magnetic resonance imaging) offers the spatial resolution necessary to investigate the 3–4 mm wide and 1.5 cm long LC. However, standard data postprocessing is often too spatially imprecise to allow investigating the structure and function of the LC at the group level. Our analysis pipeline uses a combination of existing toolboxes (SPM12, ANTs, FSL, FreeSurfer), and is tailored towards achieving suitable spatial precision in the brainstem area. Its effectiveness is demonstrated using 2 datasets comprising both younger and older adults. We also suggest quality assessment procedures which allow to quantify the spatial precision obtained. Spatial deviations below 2.5 mm in the LC area are achieved, which is superior to current standard approaches. Relevant for ageing and clinical researchers interested in brainstem imaging, we provide a tool for more reliable analyses of structural and functional LC imaging data which can be also adapted for investigating other nuclei of the brainstem. [ABSTRACT FROM AUTHOR]

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration. [ABSTRACT FROM AUTHOR]

Navigated transcranial magnetic stimulation (nTMS) is a valuable tool for non-invasive brain stimulation. Currently, nTMS requires fixing of markers on the patient's head. Head marker displacements lead to changes in coil placement and brain stimulation inaccuracy. A markerless neuronavigation method is needed to increase the reliability of nTMS and simplify the nTMS protocol. In this study, we introduce and release MarLe, a Python markerless head tracker neuronavigation software for TMS. This novel software uses computer-vision techniques combined with low-cost cameras to estimate the head pose for neuronavigation. A coregistration algorithm, based on a closed-form solution, was designed to track the patient's head and the TMS coil referenced to the individual's brain image. We show that MarLe can estimate head pose based on real-time video processing. An intuitive pipeline was developed to connect the MarLe and nTMS neuronavigation software. MarLe achieved acceptable accuracy and stability in a mockup nTMS experiment. MarLe allows real-time tracking of the patient's head without any markers. The combination of face detection and a coregistration algorithm can overcome nTMS head marker displacement concerns. MarLe can improve reliability, simplify, and reduce the protocol time of brain intervention techniques such as nTMS. [ABSTRACT FROM AUTHOR]

Background and Purpose: Artifact from cochlear implant electrodes degrades image resolution on CT. Here, we describe the use of coregistered pre‐ and postoperative CT images to reduce metallic artifact from the electrodes to assess its position more accurately within the cochlear lumen. Methods: Pre‐ and postoperative CTs were reviewed after coregistration/overlay of both exams. Images were evaluated by two neuroradiologists for scalar location of electrodes tip (± scalar translocation), tip fold over, and angular depth of insertion. Results: Thirty‐four patients were included in the final cohort. Transscalar migration was present in three (8.8%) cases (one case demonstrated tip fold over), with initial disagreement regarding transscalar migration in 1 out of 34 patients (2.9%). Agreement regarding depth of insertion was present in 31 (91.1%) cases. Five‐point Likert scales were used to compare the ability to resolve the proximity of electrodes to the lateral/outer cochlear wall without and with overlay, which is a qualitative measure of artifact from the array. Likert scores showed definitive benefit of metal artifact reduction using overlayed images with an average score of 4.34. Conclusion: This study demonstrates a novel technique of using fused coregistration of pre‐ and postoperative CTs for the purpose of artifact reduction/electrode localization. It is anticipated that this technique will permit more accurate localization of the electrodes for improvement in surgical technique and electrode array design. [ABSTRACT FROM AUTHOR]

A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA.To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S0) as well as DTI parameters: MD, FA, in-plane FA (FAIP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach.Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.

Laser speckle imaging (LSI) is a wide-field optical technique that enables superficial blood flow quantification. LSI is normally performed in a mounted configuration to decrease the likelihood of motion artifact. However, mounted LSI systems are cumbersome and difficult to transport quickly in a clinical setting for which portability is essential in providing bedside patient care. To address this issue, we created a handheld LSI device using scientific grade components. To account for motion artifact of the LSI device used in a handheld setup, we incorporated a fiducial marker (FM) into our imaging protocol and determined the difference between highest and lowest speckle contrast values for the FM within each data set (Kbest and Kworst). The difference between Kbest and Kworst in mounted and handheld setups was 8% and 52%, respectively, thereby reinforcing the need for motion artifact quantification. When using a threshold FM speckle contrast value (KFM) to identify a subset of images with an acceptable level of motion artifact, mounted and handheld LSI measurements of speckle contrast of a flow region (KFLOW) in in vitro flow phantom experiments differed by 8%. Without the use of the FM, mounted and handheld KFLOW values differed by 20%. To further validate our handheld LSI device, we compared mounted and handheld data from an in vivo porcine burn model of superficial and full thickness burns. The speckle contrast within the burn region (KBURN) of the mounted and handheld LSI data differed by

Percutaneous coronary intervention is increasingly guided by coronary physiology and optimized using intravascular imaging. Pressure-based measurements determine the significance of a stenosis using hyperemic or nonhyperemic pressure ratios (eg, the instantaneous wave-free ratio). Intravascular ultrasound and optical coherence tomography provide cross-sectional and longitudinal detail regarding plaque composition and vessel characteristics. These facilitate lesion preparation and optimization of stent sizing and positioning. This review explores the evidence-base and practical aspects of coregistering pressure gradient assessment and intravascular imaging with angiography. We then discuss gaps in the evidence and what is needed to help integrate these techniques into clinical practice. [ABSTRACT FROM AUTHOR]

Background:  Goal: To develop a 3D visualization tool that accurately and intuitively displays critical cerebral components in epilepsy pre/post surgery.Limitations of current technology:2D images; Not patient-specific; Lock of coregistration; Non user-friendly. Faculty Adviser:  Daniel Shrey, Ph.D., Children’s Hospital Orange County; Beth Lopour, Ph.D., UC Irvine Department of Biomedical Engineering | Joffre Olaya, M.D., Pediatric Neurosurgeon

The coregistration of single-look complex (SLC) SAR images for InSAR or offset tracking applications is often performed by using an accurate DEM and precise orbital information. However, in cold regions, such DEMs are rare over high-latitude areas or not up-to-date over fast melting glaciers for instance. To overcome this difficulty, we propose in this article a coregistration method preserving InSAR phase information that only requires a 3D point of reference instead of a full DEM. Developed in a Python toolbox called LabSAR, the proposed method only uses orbital information to coregister the images on the sphere centered on the Earth center passing by the ground control point (GCP). Thanks to the use of the orbital information, the so-called orbital fringes are compensated without having to estimate them. This coregistration method is compared to other approaches in two different types of applications, InSAR and offset tracking, on a PAZ Dual-Pol Temporal Stack covering the Mont Blanc massif (western European Alps). First, InSAR measurements from LabSAR are compared with the results of the Sentinel-1 ESA toolbox (SNAP). The LabSAR interferograms exhibit clearer topographical fringes, with fewer parameters to set. Second, offset tracking based on LabSAR coregistated images is used to measure the displacement of the Bossons glacier. The results are compared with those obtained by a conventional approach developed in the EFIDIR tools. By evaluating the uncertainties of both approaches using displacements over stable areas and the temporal closure error, similar uncertainty values are found. However, velocity values differ between the two approaches, especially in areas where the altitudes are different from the altitude of the reference point. The difference can reach up to 0.06 m/day, which is in the range of the glacier velocity measurement uncertainty given in the literature. The impact of the altitude of the reference point is limited: this single GCP can be chosen at the median altitude of the study area. The error margin on the knowledge of this altitude is 1,000 m, which is sufficient for the altitude to be considered as known for a wide range of study area in the world.

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration.

Recent developments in satellite processing tools allow low-cost, fast and automatic processing of a large amount of information from Earth observation, enhancing the capability of detecting coastal changes from space at different temporal scales. Some works have assessed the quality of these data and applied it to detect coastal evolution locally, most of them focusing on mid-term and long-term changes in the coastline. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using 1D (coastlines) and 3D (bathymetry) satellite-derived data obtained from site-specific processing methods. Local characteristics were included in several phases of the development of the satellite products used here: i) geolocated very high resolution images from each pilot site were used in the coregistration process to enhance geolocation accuracy in images from different missions, ii) different spectral indices were tested at each pilot site to obtain more reliable detection of the coastline at all sites and iii) measured topobathymetry data were used to obtain datum-based satellite shorelines and bathymetry. The accuracy and skill of those satellite products were assessed at several pilot sites in Spain. The results indicated high horizontal accuracy (RMSE < pixel size), with errors on the order of half of the pixel size (RMSE = 5.0 m and for Sentinel-2 and 18.8 m for Landsat5). Furthermore, the coastlines used here presented errors comparable to those obtained from the widely used open-source tool CoastSat. Time-series analysis using satellite-derived shorelines showed that coastal change processes can be detected at several temporal and spatial scales, such as short-term erosion and accretion events on a small beach, seasonal beach rotation, and long-term trends at local and regional scales. However, the results from satellite-derived bathymetry indicated that the quantitative assessment of the coastal morphology with 3D products is still limited. Some in situ measurements are necessary to obtain satellite data that represent site-specific conditions. However, the quantity of this auxiliary in situ measurements required to obtain reliable time series of satellite derived shorelines and bathymetry is significantly lower than the quantity required by traditional monitoring methods. The results are discussed, highlighting the gaps that need to be filled in the future so that satellite-derived products can be used in usual coastal change monitoring practices. • We assess the capability to monitor coastal changes with 1D and 3D satellite data. • Local characteristics are included in the development of the satellite data. • Coastal changes are detected at several temporal and spatial scales. • Long-term shoreline change in local and regional scales, and seasonal beach rotation are successfully detected. • Storm-induced erosion is monitored although cloud cover affects data availability. [ABSTRACT FROM AUTHOR]

The coregistration of terrestrial laser point clouds is widely investigated where different techniques are presented to solve this problem. The techniques are divided either as target-based or targetless approaches for coarse and fine coregistration. The targetless approach is more challenging since no physical reference targets are placed in the field during the scanning. Mainly, targetless methods are imagebased and they are applied through projecting the point clouds back to the scanning stations. The projected 360˚ point cloud images are normally in the form of panoramic images utilizing either intensity or RGB values, and an image matching is followed to align the scan stations together. However, the point cloud coregistration is still a challenge since ICP like methods are applicable for fine registration. Furthermore, image-based approaches are restricted when there is: a limited overlap between point clouds, no RGB data accompanied to intensity values, and unstructured scanned objects in the point clouds. Therefore, we present in this paper the concept of a multi surrounding scan MSS image-based approach to overcome the difficulty to register point clouds in challenging cases. The multi surrounding scan approach means to create multi-perspective images per laser scan point cloud. These multi-perspective images will offer different viewpoints per scan station to overcome the viewpoint distortion that causes the failure of the image matching in challenging situations. Two experimental tests are applied using point clouds collected in Enschede city and the published 3D toolkit data set in Bremen city. The experiments showed a successful coregistration approach even in challenging settings with different constellations. [ABSTRACT FROM AUTHOR]

Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online. • Converting images to edge maps allows users to overlay one image onto another. • Edge maps help users ensure success of spatial coregistration and correction. • Many tools define edge maps based on voxel intensity, which is rarely calibrated. • Difference of Gaussian edges are based on spatial size, which is typically known. • We provide Matlab, Python, Web and command line tools to compute DoG. [ABSTRACT FROM AUTHOR]

Very high requirement of co-registration accuracy better than 0.001 pixels for Sentinel-1 TOPS (Terrain Observation by Progressive Scan) mode presents a great challenge for application of SAR (synthetic aperture radar) interferometry in Earth Observation. The state-of-the-art techniques have demonstrated that the low coherence scenarios and abrupt loss of coherence between two arbitrary acquisitions are main sources of error to degrade the performance of TOPS time-series co-registration. In this paper, we present a modification to overcome both limitations through the coherence enhancement. The motive behind this is to improve the quality of observations before co-registration and meanwhile avoiding the coherence loss caused by fast decorrelation. To this end, principal components analysis based spatio-temporal filtering is first used to remove the artifacts in burst interferograms over strong noise areas. Rather than heuristically choosing a sub-set of interferograms as a small baseline technique does, we use Dijkstra's shortest path algorithm under graph theory framework to maximize the coherence of a sub-set interferograms. The performance of presented method against the state-of-the-art techniques is fully evaluated by synthetic data and a Sentinel-1A stack over a low coherence scene in Indonesia. Comprehensive comparisons demonstrate 9%-17% uncertainty reduction of time-series co-registration when applying our method.

This study aimed to evaluate the utility and feasibility of physiological maps coregistered with angiograms using the pullback of a pressure guidewire with continuous instantaneous wave-free ratio (iFR) measurements. iFR pullback was obtained for 70 lesions from 70 patients with stable angina pectoris using SyncVision (Philips Corp.). Physiological maps were created, whereby the post-intervention iFR (post-iFR) was predicted as iFRpred. The iFR gap was defined if the difference between the iFRpred and post-iFR was ≥ 0.3. The lesion morphology changed from that during the physiological assessment to that during the angiographic assessment in 26 lesions (37.1%). In particular, 22.6% of angiographic tandem lesions changed to physiological focal lesions. The mean pre-intervention iFR, post-iFR, and iFRpred were 0.73 ± 0.17, 0.90 ± 0.06, and 0.93 ± 0.05, respectively. The mean difference between the iFRpred and post-iFR was 0.029 ± 0.099, with 95% limits of agreement of -0.070–0.128. iFR gaps occurred in 28 patients (40%). Notably, a new iFR gradient causing a ≥ 0.03 iFR drop after stenting occurred in 11 (15.7%) cases. The study patients were divided into two groups according to biases between post-iFR and iFRpred < 0.03 (good concordance group) or ≥ 0.03 (poor concordance group). The pre-intervention heart rate was the only independent predictor of poor concordance (odds ratio, 0.936; 95% confidence interval 0.883–0.992; p = 0.027). Physiological maps under resting conditions may contribute to a reduction in unnecessary stent placements without missing lesions requiring treatment. However, the predictive accuracy of post-iFR performance in the present study was slightly lower than that in the previous reports. [ABSTRACT FROM AUTHOR]

Geometric misalignment between Landsat and Sentinel-2 data sets as well as multitemporal inconsistency of Sentinel-2A and −2B data sets currently complicate multitemporal analyses. Operational coregistration of Sentinel-2A and -2B imagery is thus required. We present a modification of the established Landsat Sentinel Registration (LSReg) algorithm. The modifications enabled LSReg to be included in an operational preprocessing workflow to automatically coregister large volumes of Sentinel-2 imagery with Landsat base images that represent multiannual monthly spectral average values. The modified LSReg was tested for the complete Sentinel-2 archive covering Crete, Greece, which is a particularly challenging region due to steep topographic gradients and high shares of water in Sentinel-2 tiles. A coregistration success rate of 87.5% of all images was obtained with a mean coregistration precision of 4.4 m. The mean shifts of 14.0 m in the x-direction and 13.4 m in the y-direction before coregistration were found, with maxima exceeding four pixels. Time series noise in locations with land cover transitions (n = 585) was effectively reduced by 43% using the presented approach. The multitemporal geometric consistency of the Sentinel-2 data set was substantially improved, thus enabling time series analyses within the Sentinel-2 data record, as well as integrated Landsat and Sentinel-2A and -2B data sets. The modified algorithm is implemented in the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) version 3.0 (https://github.com/davidfrantz/force). [ABSTRACT FROM AUTHOR]

Background: MRI follow-up is widely used for longitudinal assessment of astrocytoma, yet reading can be tedious and error-prone, in particular when changes are subtle.Purpose/hypothesis: To determine the effect of automated, color-coded coregistration (AC) of fluid attenuated inversion recovery (FLAIR) sequences on diagnostic accuracy, certainty, and reading time compared to conventional follow-up MRI assessment of astrocytoma patients.Study Type: Retrospective.Population: In all, 41 patients with neuropathologically confirmed astrocytoma.Field Strength/sequence: 1.0-3.0T/FLAIR ASSESSMENT: The presence or absence of tumor progression was determined based on FLAIR sequences, contrast-enhanced T1 sequences, and clinical data. Three radiologists assessed 47 MRI study pairs in a conventional reading (CR) and in a second reading supported by AC after 6 weeks. Readers determined the presence/absence of tumor progression and indicated diagnostic certainty on a 5-point Likert scale. Reading time was recorded by an independent assessor.Statistical Tests: The Wilcoxon test was used to assess reading time and diagnostic certainty. Differences in diagnostic accuracy, sensitivity, and specificity were analyzed with the McNemar mid-p test.Results: Readers attained significantly higher overall sensitivity (0.86 vs. 0.75; P < 0.05) and diagnostic accuracy (0.84 vs. 0.73; P < 0.05) for detection of progressive nonenhancing tumor burden when using AC compared to CR. There was a strong trend towards higher specificity within the AC-augmented reading, yet without statistical significance (0.83 vs. 0.71; P = 0.08). Sensitivity for unequivocal disease progression was similarly high in both approaches (AC: 0.94, CR: 0.92), while for marginal disease progressions, it was significantly higher in AC (AC: 0.78, CR: 0.58; P < 0.05). Reading time including application loading time was comparable (AC: 38.1 ± 16.8 sec, CR: 36.0 ± 18.9 s; P = 0.25).Data Conclusion: Compared to CR, AC improves comparison of FLAIR signal hyperintensity at MRI follow-up of astrocytoma patients, allowing for a significantly higher diagnostic accuracy, particularly for subtle disease progression at a comparable reading time.Evidence Level: 3 TECHNICAL EFFICACY STAGE: 6 J. Magn. Reson. Imaging 2020;52:1197-1206. [ABSTRACT FROM AUTHOR]

The capability to detect changes in an underwater scene has many applications, including environmental monitoring, surveillance of strategic maritime waterways, and naval mine countermeasures. This paper examines the automated detection of changes in repeat-pass synthetic aperture sonar (SAS) imagery by exploiting the loss of phase coherence caused by differences in the scene. A renavigation method based on track registration is developed for performing the coregistration of images. The method first determines the corrections to be applied to the repeat-pass navigation data by optimizing a linear track model using the estimated across-track pixel displacements. A second optimization is then performed using the along-track pixel displacements to determine a velocity correction for each ping location of the repeat-pass image to compensate for residual motion errors. The corrected navigation solution is then used to rebeamform the repeat-pass image onto the same focal points as the reference image. The method is demonstrated on a pair of repeat-pass SAS images acquired using the AquaPix INSAS2 sonar, where it is shown that it is not necessary to have access to raw element data to coregister images accurately enough for coherent change detection requirements. This technique is then applied to the second data set obtained using the HISAS 1030 sonar for the purpose of comparing both coherent and noncoherent (amplitude-only) approaches to change detection in scenes with low and high clutters. False alarms caused by the acoustic shadows of objects in high-clutter areas are mitigated using the single-pass interferometric coherence as a reference mask. The results show the successful use of the repeat-pass coherence to detect changes between SAS images with temporal baselines of several days on scenes consisting of mud, gravel, and sand, and demonstrate that a coherent approach is capable of detecting large-scale differences (caused by the introduction or removal of targets) as well as subtle scene changes that are not detectable using noncoherent methods. [ABSTRACT FROM AUTHOR]

Abstract Background Implementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation. Direct visual comparison of PET/CT slices to whole prostate sections is hampered by considerable uncertainties from imperfect coregistration and fundamentally different image modalities. In the current study, we present a novel method for advanced voxel-wise comparison of histopathology from excised prostates to pre-surgical PET. Resected prostates from eight patients who underwent PSMA-PET/CT were scanned (ex vivo CT) and thoroughly pathologically prepared. In vivo and ex vivo CT including histopathology were coregistered with three different methods (manual, semi−/automatic). Spatial overlap after CT-based registration was evaluated with dice similarity (DSC). Furthermore, we constructed 3D cancer distribution models from histopathologic information in various slices. Subsequent smoothing reflected the intrinsically limited spatial resolution of PSMA-PET. The resulting histoPET models were used for quantitative analysis of spatial histopathology-PET pattern agreement focusing on p values and coefficients of determination (R 2). We examined additional rigid mutual information (MI) coregistration directly based on PSMA-PET and histoPET. Results Mean DSC for the three different methods (ManReg, ScalFactReg, and DefReg) were 0.79 ± 0.06, 0.82 ± 0.04, and 0.90 ± 0.02, respectively, while quantification of PET-histopathology pattern agreement after CT-based registration revealed R 2 45.7, 43.2, and 41.3% on average with p

Percutaneous coronary intervention is increasingly guided by coronary physiology and optimized using intravascular imaging. Pressure-based measurements determine the significance of a stenosis using hyperemic or nonhyperemic pressure ratios (eg, the instantaneous wave-free ratio). Intravascular ultrasound and optical coherence tomography provide cross-sectional and longitudinal detail regarding plaque composition and vessel characteristics. These facilitate lesion preparation and optimization of stent sizing and positioning. This review explores the evidence-base and practical aspects of coregistering pressure gradient assessment and intravascular imaging with angiography. We then discuss gaps in the evidence and what is needed to help integrate these techniques into clinical practice., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The variation of the azimuth to Doppler center frequency of the terrain observation by progressive scans (TOPS) mode leads to the relative distortion of the phase of the interferogram. Therefore, high registration accuracy is required for interference processing. The Canadian Synthetic Aperture Radar Remote Sensing Satellite (RADARSAT‐2) TOPS mode image is less useful due to poor orbital accuracy and geometric registration accuracy, which makes the interference registration more complicated than it in sentinel SAR remote sensing (Sentinel‐1) satellite TOPS mode. In order to perform the interference processing of the TOPS mode data, this paper uses the cross‐correlation registration to correct the geometric registration results, and then uses the spectral diversity for the second geometric registration correction, and finally uses the enhanced spectral diversity to implement the final azimuth offset correction. Typically, the Doppler centroid estimation from satellite ephemeris parameters and focusing imaging parameters alone does not provide sufficient accuracy. Therefore, a phase increment method is proposed to optimize the Doppler center estimation and the estimated Doppler center is verified by the image bearing power spectrum in this paper. [ABSTRACT FROM AUTHOR]

Clinical scientists can use a continuum of registration efforts that vary in their disclosure and timing relative to data collection and analysis. Broadly speaking, registration benefits investigators by offering stronger, more powerful tests of theory with particular methods in tandem with better control of long-run false positive error rates. Registration helps clinical researchers in thinking through tensions between bandwidth and fidelity that surround recruiting participants, defining clinical phenotypes, handling comorbidity, treating missing data, and analyzing rich and complex data. In particular, registration helps record and justify the reasons behind specific study design decisions, though it also provides the opportunity to register entire decision trees with specific endpoints. Creating ever more faithful registrations and standard operating procedures may offer alternative methods of judging a clinical investigator's scientific skill and eminence because study registration increases the transparency of clinical researchers' work. (PsycINFO Database Record (c) 2019 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

In this work, we investigate the coregistration of multimodal data, such as photogrammetric/LiDAR point clouds, digital surface models, orthoimages, or 3D CAD city models, using corresponding line segments. The lines are analytically derived as intersections of adjacent planar surfaces, which can be determined more robustly and are deemed more accurate compared to single point based features. We propose a two-stage approach, which first focuses on finding optimal line correspondences between the datasets using a scale-invariant graph matching method, and then utilizes the found matching as a basis for calculating the optimal coregistration transform. By decoupling the correspondence search from the transform calculation, our approach can use more line pairs for determining the optimal transform than would be practicable with a combined, sampling-style approach. As opposed to competing methods, our transform computation is based on explicitly minimizing the average L1 distance on the matched line set. The assumed model accounts for an isotropic scaling factor, three translations and three rotation angles. We conducted experiments on two publicly available ISPRS datasets: Vaihingen and Dortmund, and compared the performance of several variations of our approach with three competing methods. The results indicate that the L1 methods decreased the median matched line distance by up to one third in case of pre-aligned Z axes. Moreover, when coregistering two photogrammetric datasets acquired from distinct viewing perspectives, our method was able to triple the number of matched lines (under a strict proximity-based criterion) compared to its competitor. Our results show that it is worthwhile to base the transform calculation on significantly more line pairs than is customary for sample consensus-based approaches. Our established validation dataset for line-based coregistration has been published and made available online (https://doi.org/10.17632/dmp7tkn8kc.2). [ABSTRACT FROM AUTHOR]

Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes.

Abstract Unmanned aerial vehicle Laser Scanning (ULS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies applicable for monitoring forested environments in unprecedented detail from complementary perspectives. Although ground-based backpack techniques provide detailed information about the forest understory and terrain, the measured point clouds based on SLAM techniques are stitched together gradually and normally expressed in a less-accurate arbitrary coordinate system. Conversely, ULS point clouds are acquired from above and usually georeferenced, yet the point density and penetrability near the ground may still suffer from dense overstory despite the low attitude operation. Coregistering the ground and aerial point clouds in the ULS coordinate system therefore provides a method for fusing understory and overstory information at single tree level without the time consuming procedure of applying ground control points. Since the ULS and BLS acquisition viewpoints differ greatly, standard coregistration methods requiring 3D point-level correspondences are likely to fail. This paper presents an object-level coregistration approach which instead operates on two sets of tree positions, with the goal of finding the optimal 3D transformation (consisting of rotation, translation and scaling) between the respective coordinate systems. The entire task is decomposed into separate problems of computing the common Z axis, estimating the scale, and 2D coregistration. In contrast to existing methods, our approach does not require additional information such as tree diameters or heights. We evaluated our method on real test plots involving diverse stem densities and tree species situated in forest farm of the eastern coastal region of Jiangsu, China. The tree positions for ground and aerial data were obtained respectively by cylinder fitting and tree segmentation. On 3 coniferous (dawn redwood) plots, 46–81% trees were matched with a distance below 50 cm, and mean position deviation of 27–36 cm. For 4 broadleaf (poplar) plots, no more than 50% trees were matched below a 1 m threshold and mean error of 54–67 cm, which can be attributed to the broadleaf trees' more irregular shape and lack of a well defined tree top. Moreover, we show that the introduction of scaling into the transform can increase the matched tree count by up to 20 percentage points and decrease the mean matched distance by up to 13% compared to a strictly rigid transform. [ABSTRACT FROM AUTHOR]

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual’s head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be <4mm and <10∘, respectively. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The objective of this study was to develop and quantitatively evaluate a radiology-pathology fusion method for spatially mapping tissue regions corresponding to different chemoradiation therapy-related effects from surgically excised whole-mount rectal cancer histopathology onto preoperative magnetic resonance imaging (MRI).Materials and Methods: This study included six subjects with rectal cancer treated with chemoradiation therapy who were then imaged with a 3-T T2-weighted MRI sequence, before undergoing mesorectal excision surgery. Excised rectal specimens were sectioned, stained, and digitized as two-dimensional (2D) whole-mount slides. Annotations of residual disease, ulceration, fibrosis, muscularis propria, mucosa, fat, inflammation, and pools of mucin were made by an expert pathologist on digitized slide images. An expert radiologist and pathologist jointly established corresponding 2D sections between MRI and pathology images, as well as identified a total of 10 corresponding landmarks per case (based on visually similar structures) on both modalities (five for driving registration and five for evaluating alignment). We spatially fused the in vivo MRI and ex vivo pathology images using landmark-based registration. This allowed us to spatially map detailed annotations from 2D pathology slides onto corresponding 2D MRI sections.Results: Quantitative assessment of coregistered pathology and MRI sections revealed excellent structural alignment, with an overall deviation of 1.50 ± 0.63 mm across five expert-selected anatomic landmarks (in-plane misalignment of two to three pixels at 0.67- to 1.00-mm spatial resolution). Moreover, the T2-weighted intensity distributions were distinctly different when comparing fibrotic tissue to perirectal fat (as expected), but showed a marked overlap when comparing fibrotic tissue and residual rectal cancer.Conclusions: Our fusion methodology enabled successful and accurate localization of post-treatment effects on in vivo MRI. [ABSTRACT FROM AUTHOR]

Background Several attempts have been made to coregister in vivo MRI with the histopathology of surgical samples, aiming to validate new MRI biomarkers and improve the detection of epileptogenic lesions. As a further implementation, we propose a method to reconstruct the anatomical localization of the intracerebral electrodes on the histological sections, developing a coregistration protocol to match the in vivo MRI onto the ex vivo MRI obtained from the surgical specimen. New method Since the ex vivo MRI is natively in geometrical correspondence with histology slices, the goal of the coregistration process is to compute the transform function mapping the clinical MRI space to the ex vivo MRI. Electrodes and leads, identified in CT-MRI, can then be segmented and translated onto the histological slices. Results Step-by-step, qualitative visual inspection showed an improved matching of the anatomical structures or boundaries and electrodes positions between the two modalities. The quantitative evaluation of the coregistration protocol reported a mean error ranging between 0.82 and 1.27 mm when a sufficient number of landmarks, particularly in the core of the specimen, were clearly identified. Comparison with existing methods Because histology was performed according to ex vivo MRI geometry we chose to transform the in vivo onto the ex vivo MRI, differently from other methods. Conclusions Interesting applications of the method will include correlating the locally-generated pathological electrical activity with the subtle morphological alterations of the tissue, and histologically validating the origin of signal alterations or quantitative parameter variations in MRI studies. [ABSTRACT FROM AUTHOR]

This work presents the coregistered, orthorectified and mosaiced high-resolution products of the MC11 quadrangle of Mars, which have been processed using novel, fully automatic, techniques. We discuss the development of a pipeline that achieves fully automatic and parameter independent geometric alignment of high-resolution planetary images, starting from raw input images in NASA PDS format and following all required steps to produce a coregistered geotiff image, a corresponding footprint and useful metadata. Additionally, we describe the development of a radiometric calibration technique that post-processes coregistered images to make them radiometrically consistent. Finally, we present a batch-mode application of the developed techniques over the MC11 quadrangle to validate their potential, as well as to generate end products, which are released to the planetary science community, thus assisting in the analysis of Mars static and dynamic features. This case study is a step towards the full automation of signal processing tasks that are essential to increase the usability of planetary data, but currently, require the extensive use of human resources. [ABSTRACT FROM AUTHOR]

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account--optical sequences, SAR sequences, digital elevation model--so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May-November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators. [ABSTRACT FROM AUTHOR]

The constellation of Landsat-8 and Sentinel-2 optical satellites offers opportunities for a wide range of Earth Observation (EO) applications and scientific studies in Earth sciences mainly related to geohazards. The multi-temporal co-registration accuracy of images provided by both missions is, however, currently not fully satisfactory for change detection, time-series analysis and in particular Earth surface motion measurements. The objective of this work is the development, implementation and test of an automatic processing chain for correcting co-registration artefacts targeting accurate alignment of Sentinel-2 and Landsat-8 imagery for time series analysis. The method relies on dense sub-pixel offset measurements and robust statistics to correct for systematic offsets and striping artefacts. Experimental evaluation at sites with diverse environmental settings is conducted to evaluate the efficiency of the processing chain in comparison with previously proposed routines. The experimental evaluation suggests lower residual offsets than existing methods ranging between RMSExy = 2.30 and 2.91 m remaining stable for longer time series. A first case study demonstrates the utility of the processor for the monitoring of continuously active landslides. A second case study demonstrates the use of the processor for measuring co-seismic surface displacements indicating an accuracy of 1/5 th of a pixel after corrections and 1/10th of a pixel after calibration with ground measurements. The implemented processing chain is available as an open source tool to support a better exploitation of the growing archives of Sentinel-2 and Landsat-8. [ABSTRACT FROM AUTHOR]

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template-based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template-based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template-based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within-dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task-based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12-25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used. Hum Brain Mapp 38:5331-5342, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Despite rapid development of marine renewable energy, relatively little is known of the immediate and future impacts on the surrounding ecosystems. Quantifying the behavior and distribution of animals around marine renewable energy devices is crucial for understanding, predicting, and potentially mitigating any threats posed by these installations. The Flow and Benthic Ecology 4D (FLOWBEC) autonomous seabed platform integrated an Imagenex multibeam echosounder and a Simrad EK60 multifrequency echosounder to monitor marine life in a 120 ^\circ sector over ranges up to 50 m, seven to eight times per second. Established target detection algorithms fail within MRE sites, due to high levels of backscatter generated by the turbulent physical dynamics, limiting and biasing analysis to only periods of low current speed. This study presents novel algorithms to extract diving seabirds, fish, and fish schools from the intense backscatter caused by turbulent dynamics in flows of 4 m s$^{{-1}}$. Filtering, detection, and tracking using a modified nearest neighbor algorithm provide robust tracking of animal behavior using the multibeam echosounder. Independent multifrequency target detection is demonstrated using the EK60 with optimally calculated thresholds, scale-sensitive filters, morphological exclusion, and frequency-response characteristics. This provides sensitive and reliable detection throughout the entire water column and at all flow speeds. Dive profiles, depth preferences, predator–prey interactions, and fish schooling behavior can be analyzed, in conjunction with the hydrodynamic impacts of marine renewable energy devices. Coregistration of targets between the acoustic instruments increases the information available, providing quantitative measures including frequency response from the EK60, and target morphology and behavioral interactions from the multibeam echosounder. The analyses draw on deployments at a tidal energy site in Scotland to compare the presence and absence of renewable energy structures across a range of physical and trophic levels over complete spring-neap tidal cycles. These results can be used to inform how animals forage in these sites and whether individuals face collision risks. This quantitative information can de-risk the licensing process and, with a greater mechanistic understanding at demonstration scales, its predictive power could reduce the monitoring required at future arrays. [ABSTRACT FROM AUTHOR]