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Choi S, Park S, Kim J, Kim H, Cho S, Kim S, Park J, Kim C. X-ray free-electron laser induced acoustic microscopy (XFELAM). PHOTOACOUSTICS 2024; 35:100587. [PMID: 38312809 PMCID: PMC10835452 DOI: 10.1016/j.pacs.2024.100587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/11/2024] [Accepted: 01/11/2024] [Indexed: 02/06/2024]
Abstract
The X-ray free-electron laser (XFEL) has remarkably advanced X-ray imaging technology and enabled important scientific achievements. The XFEL's extremely high power, short pulse width, low emittance, and high coherence make possible such diverse imaging techniques as absorption/emission spectroscopy, diffraction imaging, and scattering imaging. Here, we demonstrate a novel XFEL-based imaging modality that uses the X-ray induced acoustic (XA) effect, which we call X-ray free-electron laser induced acoustic microscopy (XFELAM). Initially, we verified the XA effect by detecting XA signals from various materials, then we validated the experimental results with simulation outcomes. Next, in resolution experiments, we successfully imaged a patterned tungsten target with drilled various-sized circles at a spatial resolution of 7.8 ± 5.1 µm, which is the first micron-scale resolution achieved by XA imaging. Our results suggest that the novel XFELAM can expand the usability of XFEL in various areas of fundamental scientific research.
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Affiliation(s)
- Seongwook Choi
- Pohang University of Science and Technology (POSTECH), Medical Device Innovation Center, Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Sinyoung Park
- Pohang University of Science and Technology (POSTECH), Medical Device Innovation Center, Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Jiwoong Kim
- Pohang University of Science and Technology (POSTECH), Medical Device Innovation Center, Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Hyunhee Kim
- Pohang University of Science and Technology (POSTECH), Medical Device Innovation Center, Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Seonghee Cho
- Pohang University of Science and Technology (POSTECH), Medical Device Innovation Center, Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Sunam Kim
- Pohang Accelerator Laboratory, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Jaeku Park
- Pohang Accelerator Laboratory, 77 Cheongam-ro, Pohang 37673, Republic of Korea
| | - Chulhong Kim
- Pohang University of Science and Technology (POSTECH), Medical Device Innovation Center, Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, 77 Cheongam-ro, Pohang 37673, Republic of Korea
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Pandey PK, Wang S, Sun L, Xing L, Xiang L. Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:532-543. [PMID: 38046375 PMCID: PMC10691826 DOI: 10.1109/trpms.2023.3238017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonstrate the efficacy of model-based (MB) algorithms for three-dimensional XACT and compare with the traditional algorithms. The MB algorithm is based on iterative, matrix-free approach for regularized-least-squares minimization corresponding to XACT. The matrix-free-LSQR (MF-LSQR) and the non-iterative model-backprojection (MBP) reconstructions were evaluated and compared with universal backprojection (UBP), time-reversal (TR) and fast-Fourier transform (FFT)-based reconstructions for numerical and experimental XACT datasets. The results demonstrate the capability of MF-LSQR algorithm to reduce noisy artifacts thus yielding better reconstructions. MBP and MF-LSQR algorithms perform particularly well with the experimental XACT dataset, where noise in signals significantly affects the reconstruction of the target in UBP and FFT-based reconstructions. The TR reconstruction for experimental XACT are comparable to MF-LSQR, but takes thrice as much time and filters the frequency components greater than maximum frequency supported by the grid, resulting loss of resolution. The MB algorithms are able to overcome the challenges in XACT and hence are vital for the clinical translation of XACT.
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Affiliation(s)
- Prabodh Kumar Pandey
- Department of Radiological Sciences, University of California, Irvine, CA, 92697, USA
| | - Siqi Wang
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
| | - Leshan Sun
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
| | - Lei Xing
- Department of Radiological Sciences, University of California, Irvine, CA, 92697, USA.; Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA.; Beckman Laser Institute, University of California, Irvine, CA 92612, USA
| | - Liangzhong Xiang
- Division of Medical Physics, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA,94305, USA
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Chen F, Sun M, Chen R, Li C, Shi J. Absolute Grüneisen parameter measurement in deep tissue based on X-ray-induced acoustic computed tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:1205-1215. [PMID: 36950240 PMCID: PMC10026575 DOI: 10.1364/boe.483490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The Grüneisen parameter is a primary parameter of the initial sound pressure signal in the photoacoustic effect, which can provide unique biological information and is related to the temperature change information of an object. The accurate measurement of this parameter is of great significance in biomedical research. Combining X-ray-induced acoustic tomography and conventional X-ray computed tomography, we proposed a method to obtain the absolute Grüneisen parameter. The theory development, numerical simulation, and biomedical application scenarios are discussed. The results reveal that our method not only can determine the Grüneisen parameter but can also obtain the body internal temperature distribution, presenting its potential in the diagnosis of a broad range of diseases.
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Affiliation(s)
- Feng Chen
- Zhejiang Lab, Hangzhou 311121, China
| | | | | | - Chiye Li
- Zhejiang Lab, Hangzhou 311121, China
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Choi S, Yang J, Lee SY, Kim J, Lee J, Kim WJ, Lee S, Kim C. Deep Learning Enhances Multiparametric Dynamic Volumetric Photoacoustic Computed Tomography In Vivo (DL-PACT). ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 10:e2202089. [PMID: 36354200 PMCID: PMC9811490 DOI: 10.1002/advs.202202089] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 10/09/2022] [Indexed: 05/19/2023]
Abstract
Photoacoustic computed tomography (PACT) has become a premier preclinical and clinical imaging modality. Although PACT's image quality can be dramatically improved with a large number of ultrasound (US) transducer elements and associated multiplexed data acquisition systems, the associated high system cost and/or slow temporal resolution are significant problems. Here, a deep learning-based approach is demonstrated that qualitatively and quantitively diminishes the limited-view artifacts that reduce image quality and improves the slow temporal resolution. This deep learning-enhanced multiparametric dynamic volumetric PACT approach, called DL-PACT, requires only a clustered subset of many US transducer elements on the conventional multiparametric PACT. Using DL-PACT, high-quality static structural and dynamic contrast-enhanced whole-body images as well as dynamic functional brain images of live animals and humans are successfully acquired, all in a relatively fast and cost-effective manner. It is believed that the strategy can significantly advance the use of PACT technology for preclinical and clinical applications such as neurology, cardiology, pharmacology, endocrinology, and oncology.
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Affiliation(s)
- Seongwook Choi
- Department of Electrical EngineeringConvergence IT EngineeringMechanical EngineeringSchool of Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial Intelligenceand Medical Device Innovation CenterPohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Jinge Yang
- Department of Electrical EngineeringConvergence IT EngineeringMechanical EngineeringSchool of Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial Intelligenceand Medical Device Innovation CenterPohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Soo Young Lee
- Department of Electrical EngineeringConvergence IT EngineeringMechanical EngineeringSchool of Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial Intelligenceand Medical Device Innovation CenterPohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Jiwoong Kim
- Department of Electrical EngineeringConvergence IT EngineeringMechanical EngineeringSchool of Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial Intelligenceand Medical Device Innovation CenterPohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Jihye Lee
- Department of ChemistryPOSTECH‐CATHOLIC Biomedical Engineering InstitutePohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Won Jong Kim
- Department of ChemistryPOSTECH‐CATHOLIC Biomedical Engineering InstitutePohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Seungchul Lee
- Department of Electrical EngineeringConvergence IT EngineeringMechanical EngineeringSchool of Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial Intelligenceand Medical Device Innovation CenterPohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
| | - Chulhong Kim
- Department of Electrical EngineeringConvergence IT EngineeringMechanical EngineeringSchool of Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial Intelligenceand Medical Device Innovation CenterPohang University of Science and Technology (POSTECH)77 Cheongam‐ro, Nam‐guPohangGyeongbuk37673Republic of Korea
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