An official website of the United States government
Digital publishing platforms and internet resources enable openness of access to scientific findings and data at scales never before realized. Unfortunately, researchers sometimes embrace lock-in systems for data generation and analysis out of necessity because meaningful alternatives do not exist. Scientific advances still take place when this occurs, but they become fragmented with discordant quality control, interoperability, reproducibility, and democratization of access. To maximize the value of these—often—publicly funded resources, disciplines are turning to FAIR Guiding Principles for data stewardship. FAIR (Findability, Accessibility, Interoperability, and Reuse) promotes the added value of widespread data sharing that is transparent, equitable, and inclusive. Here we present NoCTURN, an NSF-funded FAIR Open Science Research Coordination Network for computed tomography users. NoCTURN (the Non-clinical Computed Tomography Users Research Network) aims to address the fragmentation of tomography toolkits stemming from proprietary software, non-uniform metadata formats, and repeatability limits. In this presentation, we outline how we will achieve this aim together by 1) developing a community committed to information sharing; 2) coordinating data analysis, storage, and reporting requirements; 3) highlighting underrepresented voices in the field; 4) developing community standards inclusive of industry, research, education, and outreach stake-holders; and 5) modeling FAIR open science strategies for our colleagues and students. NoCTURN is recruiting undergraduates through established investigators from X-ray-, neutron-, and synchrotron-beam computed tomography communities—and we want to hear from you.
more »
« less
The role of networks to overcome large-scale challenges in tomography: The non-clinical tomography users research network
Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives.
more »
« less
- PAR ID:
- 10527015
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Tomography of Materials and Structures
- Volume:
- 5
- Issue:
- C
- ISSN:
- 2949-673X
- Page Range / eLocation ID:
- 100031
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The Non-Clinical Tomography Users Research Network (NoCTURN) was established in 2022 to advance Findability, Accessibility, Interoperability, and Reuse (FAIR) and Open Science (OS) practices in the computed tomographic (CT) imaging community. CT specialists utilize a shared pipeline to create digital representations of real-world objects for research, education, and outreach, and we face a shared set of challenges and limitations imposed by siloing of current workflows, best practices, and expertise. Mirroring the U.S. National Science Foundation’s “10 Big Ideas” of Convergence Research (2016), and in consideration of the White House Office of Science and Technology Policy's Nelson Memorandum (2020), NoCTURN is leveraging input from a broad community of more than 100 CT educators, researchers, curators, and industry stakeholders to propose improvements to data handling, management, and sharing that cut across scientific disciplines and extend beyond. Our primary goal is to develop practical recommendations and tools that link today's CT data to tomorrow's CT discoveries. NoCTURN is working toward this goal by providing a platform to: 1) engage the international scientific CT community via participant recruitment from imaging facilities, academic departments and museums, and data repositories across the globe; 2) stimulate improvements for CT imaging and data management standards that focus on FAIR and OS principles; and 3) work directly with private companies that manufacture the hardware and software used in CT imaging, visualization, and analysis to find common ground in documentation and interoperability that better reflects the OS standards championed by federal funding agencies. The planned deliverables from this three-year grant include a ‘Rosetta Stone’ for CT terminology, an interactive world map of CT facilities, a guide to CT repositories, and ‘Good, Better, Best’ guidelines for metadata and long-term data management. We aim to reduce the barriers to entry that isolate individuals and research labs, and we anticipate that developing community standards and improving methodological reporting will enable long-term, systemic changes necessary to aid those at all levels of experience in furthering their access to and use of CT imaging.more » « less
-
Abstract Nasal turbinals, delicate and complex bones of the nasal cavity that support respiratory or olfactory mucosa (OM), are now easily studied using high resolution micro‐computed tomography (μ‐CT). Standard μ‐CT currently lacks the capacity to identify OM or other mucosa types without additional radio‐opaque staining techniques. However, even unstained mucosa is more radio‐opaque than air, and thus mucosal thickness can be discerned. Here, we assess mucosal thickness of the nasal fossa using the cranium of a cadaveric adult dog that was μ‐CT scanned with an isotropic resolution of 30 μm, and subsequently histologically sectioned and stained. After co‐alignment of μ‐CT slice planes to that of histology, mucosal thickness was estimated at four locations. Results based on either μ‐CT or histology indicate olfactory mucosa is thicker on average compared with non‐olfactory mucosa (non‐OM). In addition, olfactory mucosa has a lesser degree of variability than the non‐OM. Variability in the latter appears to relate mostly to the varying degree of vascularity of the lamina propria. Because of this, in structures with both specialized vascular respiratory mucosa and OM, such as the first ethmoturbinal (ET I), the range of thickness of OM and non‐OM may overlap. Future work should assess the utility of diffusible iodine‐based contrast enhanced CT techniques, which can differentiate epithelium from the lamina propria, to enhance our ability to differentiate mucosa types on more rostral ethmoturbinals. This is especially critical for structures such as ET I, which have mixed functional roles in many mammals.more » « less
-
Coded aperture X-ray computed tomography is a computational imaging technique capable of reconstructing inner structures of an object from a reduced set of X-ray projection measurements. Coded apertures are placed in front of the X-ray sources from different views and thus significantly reduce the radiation dose. This paper introduces coded aperture X-ray computed tomography for robotic X-ray systems which offer positioning flexibility. While single coded-aperture 3D tomography was recently introduced for standard trajectory CT scanning, it is shown that significant gains in imaging performance can be attained by simple modifications in the CT scanning trajectories enabled by emerging dual robotic CT systems. In particular, the subject is fixed on a plane and the CT system uniformly rotates around ther −axis which is misaligned with the coordinate axes. A single stationary coded aperture is placed on front of the robotic X-ray source above the plane and the corresponding X-ray projections are measured by a two-dimensional detector on the second arm of the robotic system. The compressive measurements with misalignment enable the reconstruction of high-resolution three-dimensional volumetric images from the low-resolution coded projections on the detector at a sub-sampling rate. An efficient algorithm is proposed to generate the rotation matrix with two basic sub-matrices and thus the forward model is formulated. The stationary coded aperture is designed based on the Pearson product-moment correlation coefficient analysis and the direct binary search algorithm is used to obtain the optimized coded aperture. Simulations using simulated datasets show significant gains in reconstruction performance compared to conventional coded aperture CT systems.more » « less
-
Objective: Structural measurements after separation of cortical from trabecular bone are of interest to a wide variety of communities but are difficult to obtain because of the lack of accurate automated techniques. Methods: We d present a structure-based algorithm for separating cortical from trabecular bone in binarized images. Using the thickness of the cortex as a seed value, bone connected to the cortex within a spatially local threshold value is identified and separated from the remaining bone. The algorithm was tested on seven biological data sets from four species imaged using micro-computed tomography (μ-CT) and high-resolution peripheral quantitative computed tomography (HR-pQCT). Area and local thickness measurements were compared to images segmented manually. Results: The algorithm was approximately 11 times faster than manual measurements and the median error in cortical area was -4.47 ± 4.15%. The median error in cortical thickness was approximately 0.5 voxels for μ-CT data and less than 0.05 voxels for HR-pQCT images resulting in an overall difference of -28.1 ± 71.1 μm. Conclusion: A simple and readily implementable methodology has been developed that is repeatable, efficient, and requires few user inputs, providing an unbiased means of separating cortical from trabecular bone. Significance: Automating the segmentation of variably thick cortices will allow for the evaluation of large data sets in a time-efficient manner and allow for full-field analyses that have been previously limited to small regions of interest. The MATLAB code can be downloaded from https://github.com/TBL-UIUC/downloads.git.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.tmater.2024.100031
