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Yu J, Li D, Dai Y, Zhang C, Chen W, Zhong J, Wang X, Xia R, Cao L, Zhou C, Ruan S. Size characterization of x-ray tube source with sphere encoded imaging method. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:013102. [PMID: 38252800 DOI: 10.1063/5.0180056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024]
Abstract
In x-ray imaging, the size of the x-ray tube light source significantly impacts image quality. However, existing methods for characterizing the size of the x-ray tube light source do not meet measurement requirements due to limitations in processing accuracy and mechanical precision. In this study, we introduce a novel method for accurately characterizing the size of the x-ray tube light source using spherical encoded imaging technology. This method effectively mitigates blurring caused by system tilting, making system alignment and assembly more manageable. We employ the Richardson-Lucy algorithm to iteratively deconvolve the image and recover spatial information about the x-ray tube source. Unlike traditional coded imaging methods, spherical coded imaging employs high-Z material spheres as coding elements, replacing the coded holes used in traditional approaches. This innovation effectively mitigates blurring caused by system tilting, making system alignment and assembly more manageable. In addition, the mean square error is reduced to 0.008. Our results demonstrate that spherical encoded imaging technology accurately characterizes the size of the x-ray tube light source. This method holds significant promise for enhancing image quality in x-ray imaging.
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Affiliation(s)
- Jian Yu
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Dikai Li
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Yanmeng Dai
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Chunhui Zhang
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Wei Chen
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Jian Zhong
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Xue Wang
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Runxiang Xia
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Leifeng Cao
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Cangtao Zhou
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
| | - Shuangchen Ruan
- Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology, Center for Advanced Material Diagnostic Technology, and College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China
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Johnson MJ, Abdelmalik MR, Baidoo FA, Badachhape A, Hughes TJ, Hossain SS. Image-guided subject-specific modeling of glymphatic transport and amyloid deposition. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023; 417:116449. [PMID: 38249440 PMCID: PMC10798618 DOI: 10.1016/j.cma.2023.116449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
The glymphatic system is a brain-wide system of perivascular networks that facilitate exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) to remove waste products from the brain. A greater understanding of the mechanisms for glymphatic transport may provide insight into how amyloid beta (A β ) and tau agglomerates, key biomarkers for Alzheimer's disease and other neurodegenerative diseases, accumulate and drive disease progression. In this study, we develop an image-guided computational model to describe glymphatic transport and A β deposition throughout the brain. A β transport and deposition are modeled using an advection-diffusion equation coupled with an irreversible amyloid accumulation (damage) model. We use immersed isogeometric analysis, stabilized using the streamline upwind Petrov-Galerkin (SUPG) method, where the transport model is constructed using parameters inferred from brain imaging data resulting in a subject-specific model that accounts for anatomical geometry and heterogeneous material properties. Both short-term (30-min) and long-term (12-month) 3D simulations of soluble amyloid transport within a mouse brain model were constructed from diffusion weighted magnetic resonance imaging (DW-MRI) data. In addition to matching short-term patterns of tracer deposition, we found that transport parameters such as CSF flow velocity play a large role in amyloid plaque deposition. The computational tools developed in this work will facilitate investigation of various hypotheses related to glymphatic transport and fundamentally advance our understanding of its role in neurodegeneration, which is crucial for the development of preventive and therapeutic interventions.
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Affiliation(s)
- Michael J. Johnson
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th St, Austin, Texas 78712, USA
| | - Michael R.A. Abdelmalik
- Department of Mechanical Engineering, Eindhoven University of Technology, Gemini, Building number 15, Groene Loper, 5612 AE Eindhoven, The Netherlands
| | - Frimpong A. Baidoo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th St, Austin, Texas 78712, USA
| | - Andrew Badachhape
- Department of Radiology, Baylor College of Medicine, 701 Fannin Street, Suite 47, Houston, Texas 77030, USA
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th St, Austin, Texas 78712, USA
| | - Shaolie S. Hossain
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24th St, Austin, Texas 78712, USA
- Molecular Cardiology Research Laboratories, The Texas Heart Institute, 6770 Bertner Avenue, Houston, Texas 77030, USA
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