Software and pipelines for registration and analyses of rodent brain image data in reference atlas space

Prospective motion correction of high-resolution magnetic resonance imaging data in children

Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions.Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features.Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes.Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.

Unmanned aerial vehicles (UAVs) are well suited for infrared (IR) thermography of solar photovoltaics (PV). For an autonomous UAV system, precise and accurate navigation is vital for data quality and acquisition efficiency. The navigation system of a UAV is typically based on a flight controller (FC) using global navigation satellite systems (GNSS), barometric altimeter and inertial navigation. In this paper, position estimates from an integrated FC are compared with a standalone high-performance real time kinematic (RTK) navigation system and modelled for IR fault classification scenarios. The results show that position estimation errors have a strong impact on IR image resolution and data acquisition efficiency. These results are important to consider when assessing cost effective autonomous IR inspection of large PV plants.

The main challenge in offshore Southeast Sumatra, as one of Indonesia's mature fields, is to increase production in existing assets. The approach taken by the operator is through side-track drilling from historical wells, planned as a series of infill development wells, whilst evaluating new potential plays. This case study examines the logging-while-drilling (LWD) data acquisition in the Krisna-XX well in the Krisna field, which was designed to drain the remaining reserves from the Lower Baturaja limestone formation in the Sunda basin and maximize undrained area oil potential. Based on simulation analysis, the estimated reserves in the Lower Baturaja were 581 MBO with an initial rate of 426 BOPD. Additionally, the completion strategy required a rat hole to be drilled into the basement, which elsewhere in the basin can be hydrocarbon bearing. The first two wells drilled in the Krisna Field experienced severe mud losses while drilling, which had caused significant invisible non-productive time (NPT). The losses were associated with potential natural fracture swarms in the limestones and basement formations, but the fracture apertures were below the resolution of the LWD density image tool used in those wells. Hence, to refine evaluation of the fractures, an azimuthally focused resistivity tool was utilized in the third well (Krisna-XX), providing omni-directional laterolog resistivity and high-resolution resistivity images. The high-resolution resistivity tool was run in combination with quad-combo LWD tools to drill and log the high-angle (>70° inclination) 8.5-in. reservoir section. While drilling both the reservoir and basement intervals, this well also experienced high dynamic loss rates of water-based drilling fluids, which were treated with a lost circulation material, minimizing impact on the reservoir productivity. The well was drilled to total depth (TD) without incident. The aim was to utilize borehole imaging memory data acquired from this section as a benchmark for examining the current state of the Sunda Basin's formation. Post-run analysis of the memory image data yielded excellent quality high-definition images over the interval of the carbonate reservoir, coal, claystone, and basement sequences, improving knowledge of the Sunda Basin's detailed geological structure. This result helped the operator to understand the reservoir facies characterization of the Lower Baturaja formation, while overcoming potential fluid-loss conditions of up to 250-300 bbl/hr. The images clearly showed sedimentary structures, natural fracture networks, drilling induced fractures and vugs. Subsequently, by comparing against actual production (which has reached up to 1,842 BOPD), analysis of weak zones will improve reservoir insight to be incorporated into future subsurface models. In addition, high-resolution images combined with pressure-while-drilling data complements drilling operations analysis. Correlation to the intervals of fluid loss has allowed the operator to improve time and cost efficiency of the drilling operation and forthcoming planning.

The use of genetically encoded fluorescent proteins has revolutionized the fields of cell and developmental biology and redefined our understanding of the dynamic morphogenetic processes that work to shape the embryo. Fluorescent proteins are routinely used as vital reporters to label tissues, cells, cellular organelles, or proteins of interest and in doing so provide contrasting agents enabling the acquisition of high-resolution quantitative image data. With the advent of more accessible and sophisticated imaging technologies and abundance of fluorescent proteins with different spectral characteristics, the dynamic processes taking place in situ in living embryos can now be probed. Here, we provide an overview of some recent advances in this rapidly evolving field.

The creation of “art farmland” is one of the important ways to carry out the construction of beautiful countryside in China. Satellite image recognition technology can help the planning, inspection and adjustment of art farmland. Rural construction and farmland planners should establish close contact with local high-resolution satellite image data acquisition centers for data cooperation; local high-resolution satellite image data acquisition centers can also provide accurate data and personalized services for the project according to needs. The combination of satellite image identification technology with UAV technology and ground observation can create art farmland more efficiently and help the construction of beautiful countryside.KeywordsImage recognitionImage content understandingHigh-resolution satelliteArt farmlandBeautiful countryside

Geospatial analysis of a coastal sand dune field evolution: Jockey's Ridge, North Carolina

Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage.

Geology can be defined as the study of the nature and dynamics of planetary surfaces and interiors. This field of endeavor has expanded greatly over the past few decades to include study not only of the Earth, but also of other objects in the solar system, focusing heavily on Earth's Moon, Venus, and Mars. Acquisition and analysis of imaging data have been central to increased understanding of planetary surfaces. In the case of the planets and satellites, imaging data have been of fundamental importance because of the very limited opportunities for human exploration, return of samples, or even detailedin‐situanalyses. For Earth, analyses of image data have been central to several emerging fields of interest. In this article, the focus is on key scientific objectives associated with the geology of the Earth and how appropriate imaging data sets will help meet those objectives. This focus is not meant to downgrade the importance of imaging in robotic or human exploration of the solar system. Rather it is meant to provide a coherent theme for the article and to delineate how imaging will help address a suite of fundamentally important objectives and challenges that face us over the next few decades.The article begins with an overview of remote sensing as applied to geology, focusing on imaging applications. Summaries of objectives and tasks based in part on plans put together by the United States Geological Survey for the next decade follow. These plans both focus the key scientific areas of research within geology that have significant societal benefits and provide a coherent pathway for discussing uses of imaging in geology. Specific examples are given for the major objectives to illustrate the use of imaging techniques to address particular applications.

With dental imaging data acquired at unprecedented speed and resolution, traditional serial image processing and single-node storage need to be re-examined in a “BigData” context. Furthermore, most previous dental computing has focused on the actual imaging acquisition and image analysis tools, while much less research has focused on enabling caries assessment via visual analysis of large dental imaging data. In this paper we present DENVIS, an end-to-end solution for cariologists to manage, mine, visualize, and analyze large dental imaging data for investigative carious lesion studies. DENVIS consists of two main parts: data driven image analysis modules triggered by imaging data acquisition that exploit parallel MapReduce tasks and ingest visualization archive into a distributed NoSQL store, and user driven modules that allow investigative analysis at run time. DENVIS has seen early use by our collaborators in oral health research, where our system has been used to pose and answer domain-specific questions for quantitative assessment of dynamic carious lesion activities.

The scarcity and pollution of water resources pose significant constraints on both the economic development of a region and the well-being of its inhabitants. Effective monitoring of the water environment is crucial in addressing these issues. Currently, traditional methods for river water quality supervision, such as manual sampling and laboratory analysis, are inefficient and unable to provide real-time data. This paper explored the potential of Unmanned Aerial Vehicle (UAV) remote sensing technology in periodic monitoring of rivers, emergency monitoring of flood disasters, and monitoring of suspended sediment of water. The results show that: (1) UAV-carried sensors allow for the acquisition of high-resolution image data, enabling comprehensive monitoring of rivers throughout the entire cycle. This enhances our understanding of river dynamics and facilitates effective watershed management. (2) In terms of emergency disaster monitoring, UAVs offer rapid response and real-time data transmission capabilities. It provides timely information on riverine hazards, enabling quick response and mitigation measures. In addition, UAV-based monitoring is cost-effective and ensures the safety of operators. (3) UAV remote sensing can estimate and track the concentration of pollutants in rivers, enabling the assessment of pollution levels within watersheds. This information serves as a valuable resource for environmental protection efforts and the development of appropriate mitigation strategies.

Crop water stress detection based on UAV remote sensing systems

Acquisition of high-resolution imaging data using multiple excitations without the sensitivity to fluctuations of the transverse magnetization phase, which is a major problem of multi-shot MRI. The concept of superresolution MRI based on microscopic tagging is analyzed using an analogy with the optical method of structured illumination. Sinusoidal tagging is shown to provide subpixel resolution by mixing of neighboring spatial frequency (k-space) bands. It represents a phaseless modulation added on top of the standard Fourier encoding, which allows the phase fluctuations to be discarded at an intermediate reconstruction step. Improvements are proposed to correct for tag distortions due to magnetic field inhomogeneity and to avoid the propagation of Gibbs ringing from intermediate low-resolution images to the final image. The method was applied to diffusion-weighted EPI. Artifact-free superresolution images can be obtained despite a finite duration of the tagging sequence and related pattern distortions by a field map based phase correction of band-wise reconstructed images. The ringing effect present in the intermediate images can be suppressed by partial overlapping of the mixed k-space bands in combination with an adapted filter. High-resolution diffusion-weighted images of the human head were obtained with a three-shot EPI sequence despite motion-related phase fluctuations between the shots. Due to its phaseless character, tagging-based sub-pixel encoding is an alternative to k-space segmenting in the presence of unknown phase fluctuations, in particular those due to motion under strong diffusion gradients. Proposed improvements render the method practicable in realistic conditions.

This Memoir covers recent advances in the acquisition and application of high-resolution image data to unconventional reservoirs. The value of integrating multiple techniques is a common theme. Chapters address imaging methods, recognition of artifacts, and case studies that explore nanopore systems within particular depositional settings. The importance of mineralogy, organic matter content, and fabric to reservoir quality issues such as wettability, porosity, and formation damage are addressed. This volume will prove useful to anyone interested in the methods for observing and quantifying the pore systems that control hydrocarbon storage and flow in unconventional reservoirs. Unconventional reservoirs studied include Bakken, Barnett, Bossier, Eagle Ford, Geneseo, Green River, Horn River, Marcellus, Mississippi Lime, Monterey, Niobrara, Wolfcamp, and Woodford formations.

To propose a respiratory reordered UNFOLD (RR-UNFOLD) imaging sequence to significantly reduce the amount of k-space data required for first-pass MR myocardial perfusion imaging. Rapid acquisition of high-resolution imaging data is essential to detailed quantitative analysis of first-pass myocardial perfusion. Existing MR sequences have explored the full capacity of the imaging hardware to reduce the acquisition window within each cardiac cycle while maintaining the desired spatial resolution. Further improvement in perfusion imaging will require a more efficient use of the information content of the k-space data. The method uses prospective diaphragmatic navigator echoes to ensure that temporal filtering of UNFOLD is carried out on a series of images that are spatially registered. An adaptive real-time rebinning algorithm is developed for the creation of static image subseries related to different levels of respiratory motion. Issues concerning the temporal smoothing of tracer kinetic signals are discussed, and a solution based on oversampling of the central k-space is provided. The method is assessed in 10 normal subjects without the administration of contrast agent, and further validated by administration of Gd-DTPA in 10 patients at rest. The results of this study show that RR-UNFOLD significantly extends the applicability of UNFOLD to perfusion imaging, which yields a 40% reduction in image artifact when the same amount of k-space information is used. The scan efficiency achieved can be used in combination with MR hardware improvements for extending the three-dimensional spatial coverage and shortening the data acquisition window to provide detailed information on regional myocardial perfusion abnormalities.