We study the bimodal imaging of neutron and x-ray simultaneous tomography in complementary ways and feasibility test for HANARO thermal neutron facility. This approach combines the advantages of x-rays, which exhibit low penetration for high atomic numbers, with neutrons that have high cross-sections for light elements such as Li, Be, and B. This combination allows for a comprehensive understanding of the internal structure of materials, extending the exploration range beyond what is possible with conventional x-ray and neutron technologies. We additionally demonstrate the correction of scattering in neutron images using black body techniques, which successfully enhances visibility and effectively reduces scattering-induced noise. Furthermore, we discuss the potential for quantitative and qualitative comparative evaluations by applying an unrolling method as a new approach to straighten bent or twisted objects. The results suggest the future applicability of neutron and x-ray simultaneous tomography at HANARO and indicate the potential for integration with quantitative techniques such as energy-selective imaging and interferometry.
The Talbot-Lau grating interferometer advances x-ray imaging by enabling phase contrast, dark-field imaging, and differential phase contrast imaging with lab-based x-ray sources, alongside conventional absorption images. This study explores directional dark-field imaging (DDFI) to reveal microstructural details in samples. Using a Talbot-Lau setup, we measured materials like carbon fiber, demonstrating DDFI's effectiveness in visualizing anisotropy, orientation, and microstructure. By rotating the sample and analyzing scattering directions, we showcase DDFI's ability to describe complex material features. Our findings indicate DDFI's potential in materials science, offering new insights into sample characterization and analysis.
When using neutron scattering information in a conventional grating interferometer, there were significant limitations due to the configuration of the interferometer system in obtaining a graph of the autocorrelation length that confirms the internal structure of the object. To solve this problem, a simulation was conducted to obtain the autocorrelation length over a wide range by changing the system configuration using a single absorption grating (inverse pattern simulation). Using spatial harmonic imaging techniques for ancient Korean coins, we obtained images of various modalities and revealed the differences between genuine and counterfeit coins. This experiment was conducted at PSI's BOA.
We explore interior tomography, a technique facilitating the observation of a region-of-interest (ROI) in computerized tomography (CT) through a strategically adjusted detector offset. By modifying the offset, we extend the field-of-view (FOV), consequently enlarging the ROI. Our innovative approach involves offsetting the detector to cover asymmetric regions during data acquisition, overcoming challenges faced by conventional reconstruction algorithms dealing with truncated projection data in interior tomography. To address these issues, we employ a deep learning (DL) network for interior tomography with a detector offset, comparing its performance with other reconstruction methods. Our DL network leverages the weighted filtered back projection (FBP) as input and incorporates the ROI map as additional information, enabling flexible ROI image acquisition within a single network. Trained on abdominal CT projection data, our network exhibits superior performance compared to existing methods. This methodology holds promise for advancing system fusion and miniaturization, particularly in omni-tomography, as it efficiently eliminates noise and artifacts in a shorter time.
Phase-contrast computed tomography (CT) have advantages of analyzing low Z objects such as polymer and soft tissue. Especially, X-ray grating interferometer CT is a practical method to obtain phase-contrast CT, but it has limited object size because of the limitation of the grating size. So, if the object is larger, the interior problem is occurred. It is known that there is no exact solution to solve this problem. In this study, we used machine learning to reduce the artifacts due to data truncation. We prepared the first input as a filtered backprojection (FBP) output, which is a classical image reconstruction method that has severe artifacts when data is truncated. And we also prepared the second input as geometrical information to clarify the region of interest (ROI). These networks were compared in two cases; a single input, two inputs. Visual results and quantitative results were used to compare image quality about various methods. Simulation results showed the better results than other methods. Our results show that machine learning is a promising technique to solve the CT challenges, may have many applications to all imaging fields.
Over the past two years, we have developed a series of algorithms for Grangeat-type half-scan based reconstruction of a short object. These algorithms allow high temporal resolution and high temporal consistence, and suppress Feldkamp-type reconstruction related artifacts. Therefore, this scheme is promising for dynamic and/or quantitative imaging. In this paper, we extend our work into a solution to the long object problem. Our approach takes a temporally non-optimized pre-reconstruction step to transform the long object problem into a short object problem. The detector area is analytically classified into desirable, corrupted, and useless areas. The cone-beam data in the corrupted area are then corrected by the forward projection of the pre-reconstruction, while the data in the useless area are set to zero. A generalized Feldkamp algorithm is chosen for the pre-reconstruction. After the correction, the Grangeat-type half-scan based reconstruction of a short object is performed along with several shadow zone interpolation techniques for the final reconstruction. Numerical simulation is conducted to compare the proposed algorithm with a half-scan Feldkamp algorithm.
In this paper, we perform numerical studies on Feldkamp-type and Katsevich-type algorithms for cone-beam reconstruction with a nonstandard spiral locus to develop an electron-beam micro-CT scanner. Numerical results are obtained using both the approximate and exact algorithms in terms of image quality. It is observed that the two algorithms produce similar quality if the cone angle is not large and/or there is no sharp density change along the z-direction. The Katsevich-type algorithm is generally preferred due to its nature of exactness.
Currently, various cone-beam CT scanners are under rapid development for major biomedical applications. Half-scan cone-beam image reconstruction algorithms are desirable, which assume only part of a scanning turn, and are advantageous in terms of temporal resolution. While the existing half-scan cone-beam algorithms are in the Feldkamp framework, we formulate a half-scan algorithm in the Grangeat framework for circular and helical trajectories. First, we modify the Grangeat formula in the circular half-scan case. With analytically defined boundaries, the Radon space is partitioned into shadow zone, singly and doubly sampled regions, respectively. A smooth weighting scheme is designed to compensate for data redundancy and inconsistency. The sampled regions are linearly interpolated into the shadow zone for a complete data set. Then, these concepts and formulas are extended to the helical half-scan case. Extensive numerical simulation studies are performed to verify the correctness and demonstrate the performance. Our Grangeat-type half-scan algorithms allow minimization of redundant data and optimization of temporal resolution, and outperform Feldkamp-type reconstruction in terms of image artifacts. These algorithms seem promising for quantitative and dynamic biomedical applications of cone-beam tomography.
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