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1. Resolution-invariant measurements of small objects in polychromatic CT data

   https://doi.org/10.1117/12.2532082  

   Ketcham, Richard A. (September 2019, Developments in X-Ray Tomography XII)

   Features such as particles, pores, or cracks are challenging to measure accurately in CT data when they are small relative to the data resolution, characterized as a point-spread function (PSF). These challenges are particularly acute when paired with segmentation, as the PSF distributes some of the signal from a voxel among neighboring ones; effectively dispersing some of the signal from a given object to a region outside of it. Any feature of interest with one or more dimensions on the order of the PSF will be impacted by this effect, and measurements based on global thresholds necessarily fail. Measurements of the same features should be consistent across different instruments and data resolutions. The PVB (partial volume and blurring) method successfully compensates by quantifying features that are small in all three dimensions based on their attenuation anomaly. By calibrating the CT number of the phase of interest (in this case, gold) it is possible to accurately measure particles down to <6 voxels in data acquired on two instruments, 14 years apart, despite severe artifacts. Altogether, the PVB method is accurate, reproducible, resolution-invariant, and objective; it is also notable for its favorable error structure. The principal challenge is the need for representative effective CT numbers, which reflect not only the features of interest themselves, but also the X-ray spectrum, the size, shape and composition of the enclosing sample, and processing details such as beam-hardening correction. Empirical calibration is the most effective approach. 

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2. Optimal surface segmentation with convex priors in irregularly sampled space

   https://doi.org/https://doi.org/10.1016/j.media.2019.02.004  

   Shah, Abhay; Abamoffa, Michael D; Wu, Xiaodong (May 2019, Medical image analysis)

   Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Thus the segmentation cannot attain an accuracy greater than a single unit voxel, i.e. the distance between two adjoining nodes in graph space. Segmentation accuracy higher than a unit voxel is achievable by exploiting partial volume information in the voxels which shall result in non-equidistant spacing between adjoining graph nodes. This paper reports a generalized graph based multiple surface segmentation method with convex priors which can optimally segment the target surfaces in an irregularly sampled space. The proposed method allows non-equidistant spacing between the adjoining graph nodes to achieve subvoxel segmentation accuracy by utilizing the partial volume information in the voxels. The partial volume information in the voxels is exploited by computing a displacement field from the original volume data to identify the subvoxel-accurate centers within each voxel resulting in non-equidistant spacing between the adjoining graph nodes. The smoothness of each surface modeled as a convex constraint governs the connectivity and regularity of the surface. We employ an edge-based graph representation to incorporate the necessary constraints and the globally optimal solution is obtained by computing a minimum s-t cut. The proposed method was validated on 10 intravascular multi-frame ultrasound image datasets for subvoxel segmentation accuracy. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional segmentations. 

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3. Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

   https://doi.org/10.1175/AIES-D-23-0065.1  

   White, Charles_H; Ebert-Uphoff, Imme; Haynes, John_M; Noh, Yoo-Jeong (April 2024, Artificial Intelligence for the Earth Systems)

   Abstract Superresolution is the general task of artificially increasing the spatial resolution of an image. The recent surge in machine learning (ML) research has yielded many promising ML-based approaches for performing single-image superresolution including applications to satellite remote sensing. We develop a convolutional neural network (CNN) to superresolve the 1- and 2-km bands on the GOES-R series Advanced Baseline Imager (ABI) to a common high resolution of 0.5 km. Access to 0.5-km imagery from ABI band 2 enables the CNN to realistically sharpen lower-resolution bands without significant blurring. We first train the CNN on a proxy task, which allows us to only use ABI imagery, namely, degrading the resolution of ABI bands and training the CNN to restore the original imagery. Comparisons at reduced resolution and at full resolution withLandsat-8/Landsat-9observations illustrate that the CNN produces images with realistic high-frequency detail that is not present in a bicubic interpolation baseline. Estimating all ABI bands at 0.5-km resolution allows for more easily combining information across bands without reconciling differences in spatial resolution. However, more analysis is needed to determine impacts on derived products or multispectral imagery that use superresolved bands. This approach is extensible to other remote sensing instruments that have bands with different spatial resolutions and requires only a small amount of data and knowledge of each channel’s modulation transfer function. Significance StatementSatellite remote sensing instruments often have bands with different spatial resolutions. This work shows that we can artificially increase the resolution of some lower-resolution bands by taking advantage of the texture of higher-resolution bands on theGOES-16ABI instrument using a convolutional neural network. This may help reconcile differences in spatial resolution when combining information across bands, but future analysis is needed to precisely determine impacts on derived products that might use superresolved bands. 

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4. Dual-zone material assignment method for correcting partial volume effects in image-based bone models

   https://doi.org/10.1080/10255842.2022.2119383  

   Inglis, Brendan; Grumbles, Daniel; Dailey, Hannah L. (September 2022, Computer Methods in Biomechanics and Biomedical Engineering)

   In image-based finite element analysis of bone, partial volume effects (PVEs) arise from image blur at tissue boundaries and as a byproduct of geometric reconstruction and meshing during model creation. In this study, we developed and validated a material assignment approach to mitigate partial volume effects. Our validation data consisted of physical torsion testing of intact tibiae from N = 20 Swiss alpine sheep. We created finite element models from micro-CT scans of these tibiae using three popular element types (10-node tetrahedral, 8-node hexahedral, and 20-node hexahedral). Without partial volume management, the models over-predicted the torsional rigidity compared to physical biomechanical tests. To address this problem, we implemented a dual-zone material model to treat elements that overlap low-density surface voxels as soft tissue rather than bone. After in situ inverse optimization, the dual-zone material model produced strong correlations and high absolute agreement between the virtual and physical tests. This suggests that with appropriate partial volume management, virtual mechanical testing can be a reliable surrogate for physical biomechanical testing. For maximum flexibility in partial volume management regardless of element type, we recommend the use of the following dual-zone material model for ovine tibiae: soft-tissue cutoff density of 665 mgHA/cm3 with a soft tissue modulus of 50 MPa (below cutoff) and a density-modulus conversion slope of 10,225 MPa-cm3/mgHA for bone (above cutoff). 

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5. Application of LiDAR Derived Fuel Cells to Wildfire Modeling at Laboratory Scale

   https://doi.org/10.3390/fire6100394  

   Marcozzi, Anthony A.; Johnson, Jesse V.; Parsons, Russell A.; Flanary, Sarah J.; Seielstad, Carl A.; Downs, Jacob Z. (October 2023, Fire)

   Williamson, Grant (Ed.)

   Terrestrial LiDAR scans (TLS) offer a rich data source for high-fidelity vegetation characterization, addressing the limitations of traditional fuel sampling methods by capturing spatially explicit distributions that have a significant impact on fire behavior. However, large volumes of complex, high resolution data are difficult to use directly in wildland fire models. In this study, we introduce a novel method that employs a voxelization technique to convert high-resolution TLS data into fine-grained reference voxels, which are subsequently aggregated into lower-fidelity fuel cells for integration into physics-based fire models. This methodology aims to transform the complexity of TLS data into a format amenable for integration into wildland fire models, while retaining essential information about the spatial distribution of vegetation. We evaluate our approach by comparing a range of aggregate geometries in simulated burns to laboratory measurements. The results show insensitivity to fuel cell geometry at fine resolutions (2–8 cm), but we observe deviations in model behavior at the coarsest resolutions considered (16 cm). Our findings highlight the importance of capturing the fine scale spatial continuity present in heterogeneous tree canopies in order to accurately simulate fire behavior in coupled fire-atmosphere models. To the best of our knowledge, this is the first study to examine the use of TLS data to inform fuel inputs to a physics based model at a laboratory scale. 

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