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Jia M, Wei Z, Gao F, Jiang M, Wang W, Yuan Z, Pogue BW. Time-gated single-pixel imaging of Cherenkov emission from a medical linear accelerator. OPTICS LETTERS 2024; 49:2425-2428. [PMID: 38691735 DOI: 10.1364/ol.518624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/01/2024] [Indexed: 05/03/2024]
Abstract
Cherenkov imaging is an ideal tool for real-time in vivo verification of a radiation therapy dose. Given that radiation is pulsed from a medical linear accelerator (LINAC) together with weak Cherenkov emissions, time-gated high-sensitivity imaging is required for robust measurements. Instead of using an expensive camera system with limited efficiency of detection in each pixel, a single-pixel imaging (SPI) approach that maintains promising sensitivity over the entire spectral band could be used to provide a low-cost and viable alternative. A prototype SPI system was developed and demonstrated here in Cherenkov imaging of LINAC dose delivery to a water tank. Validation experiments were performed using four regular fields and an intensity-modulated radiotherapy (IMRT) delivery plan. The Cherenkov image-based projection percent depth dose curves (pPDDs) were compared to pPDDs simulated by the treatment planning system (TPS), with an overall average error of 0.48, 0.42, 0.65, and 1.08% for the 3, 5, 7, and 9 cm square beams, respectively. The composite image of the IMRT plan achieved a 85.9% pass rate using 3%/3 mm gamma index criteria, in comparing Cherenkov intensity and TPS dose. This study validates the feasibility of applying SPI to the Cherenkov imaging of radiotherapy dose for the first time to our knowledge.
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Zhang H, Du K, Zhao C, Tang J, Si S, Jia W, Xue L, Li Z. Optimizing the ordering of the Hadamard masks of ghost imaging suitable for the efficient face reconstruction using the max-projection method. Sci Rep 2023; 13:22702. [PMID: 38123568 PMCID: PMC10733417 DOI: 10.1038/s41598-023-48453-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
One crucial component of ghost imaging (GI) is the encoded mask. Higher-quality reconstruction at lower sampling rates is still a major challenge for GI. Inspired by deep learning, max-projection method is proposed in the paper to reorder the Hadamard masks for its efficient and rapid reconstruction. The simulations demonstrated that max-projection ordering with only 20 face training images yielded excellent reconstruction outcomes. In noise-free simulations, at an ultralow sampling rate of 5%, the PSNR of the max-projection ordering was 1.1 dB higher than that of the cake-cutting ordering with the best performance in the reference group. In noisy simulations, at ultralow sampling rates, the retrieved images remained almost identical to their noise-free counterparts. Irrespective of the presence or absence of noise, the max-projection ordering guaranteed the highest fidelity of image reconstruction at ultralow sampling rates. The reconstruction time was reduced to mere milliseconds, thereby enabling swift visualization of dynamic phenomena. Accordingly, the max-projection ordering Hadamard matrix offers a promising solution for real-time GI due to its higher reconstruction quality, stronger noise immunity and millisecond reconstruction time.
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Affiliation(s)
- Haipeng Zhang
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China
| | - Kang Du
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Changzhe Zhao
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Tang
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shangyu Si
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenhong Jia
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China
| | - Lian Xue
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China.
| | - Zhongliang Li
- Shanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201204, China.
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Li J, Chen S, Ratner D, Blu T, Pianetta P, Liu Y. Nanoscale chemical imaging with structured X-ray illumination. Proc Natl Acad Sci U S A 2023; 120:e2314542120. [PMID: 38015849 PMCID: PMC10710092 DOI: 10.1073/pnas.2314542120] [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] [Received: 08/23/2023] [Accepted: 10/23/2023] [Indexed: 11/30/2023] Open
Abstract
High-resolution imaging with compositional and chemical sensitivity is crucial for a wide range of scientific and engineering disciplines. Although synchrotron X-ray imaging through spectromicroscopy has been tremendously successful and broadly applied, it encounters challenges in achieving enhanced detection sensitivity, satisfactory spatial resolution, and high experimental throughput simultaneously. In this work, based on structured illumination, we develop a single-pixel X-ray imaging approach coupled with a generative image reconstruction model for mapping the compositional heterogeneity with nanoscale resolvability. This method integrates a full-field transmission X-ray microscope with an X-ray fluorescence detector and eliminates the need for nanoscale X-ray focusing and raster scanning. We experimentally demonstrate the effectiveness of our approach by imaging a battery sample composed of mixed cathode materials and successfully retrieving the compositional variations of the imaged cathode particles. Bridging the gap between structural and chemical characterizations using X-rays, this technique opens up vast opportunities in the fields of biology, environmental, and materials science, especially for radiation-sensitive samples.
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Affiliation(s)
- Jizhou Li
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA94025
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Si Chen
- X-ray Science Division, Argonne National Laboratory, Lemont, IL60439
| | - Daniel Ratner
- Machine Learning Initiative, SLAC National Accelerator Laboratory, Menlo Park, CA94025
| | - Thierry Blu
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Piero Pianetta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA94025
| | - Yijin Liu
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX78705
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Konečná A, Rotunno E, Grillo V, García de Abajo FJ, Vanacore GM. Single-Pixel Imaging in Space and Time with Optically Modulated Free Electrons. ACS PHOTONICS 2023; 10:1463-1472. [PMID: 37215321 PMCID: PMC10197172 DOI: 10.1021/acsphotonics.3c00047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Indexed: 05/24/2023]
Abstract
Single-pixel imaging, originally developed in light optics, facilitates fast three-dimensional sample reconstruction as well as probing with light wavelengths undetectable by conventional multi-pixel detectors. However, the spatial resolution of optics-based single-pixel microscopy is limited by diffraction to hundreds of nanometers. Here, we propose an implementation of single-pixel imaging relying on attainable modifications of currently available ultrafast electron microscopes in which optically modulated electrons are used instead of photons to achieve subnanometer spatially and temporally resolved single-pixel imaging. We simulate electron beam profiles generated by interaction with the optical field produced by an externally programmable spatial light modulator and demonstrate the feasibility of the method by showing that the sample image and its temporal evolution can be reconstructed using realistic imperfect illumination patterns. Electron single-pixel imaging holds strong potential for application in low-dose probing of beam-sensitive biological and molecular samples, including rapid screening during in situ experiments.
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Affiliation(s)
- Andrea Konečná
- ICFO-Institut
de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona 08860, Spain
- Central
European Institute of Technology, Brno University of Technology, 612 00 Brno, Czech Republic
| | - Enzo Rotunno
- Centro
S3, Istituto di Nanoscienze-CNR, 41125 Modena, Italy
| | | | - F. Javier García de Abajo
- ICFO-Institut
de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona 08860, Spain
- ICREA-Institució
Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Giovanni Maria Vanacore
- Laboratory
of Ultrafast Microscopy for Nanoscale Dynamics (LUMiNaD), Department
of Materials Science, University of Milano-Bicocca, Via Cozzi 55, 20121 Milano, Italy
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Park J, Gao L. Cascaded compressed-sensing single-pixel camera for high-dimensional optical imaging. RESEARCH SQUARE 2023:rs.3.rs-2295079. [PMID: 36712021 PMCID: PMC9882592 DOI: 10.21203/rs.3.rs-2295079/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Single-pixel detectors are popular devices in optical sciences because of their fast temporal response, high sensitivity, and low cost. However, when being used for imaging, they face a fundamental challenge in acquiring high-dimensional information of an optical field because they are essentially zero-dimensional sensors and measure only the light intensity. To address this problem, we developed a cascaded compressed-sensing single-pixel camera, which decomposes the measurement into multiple stages, sequentially reducing the dimensionality of the data from a high-dimensional space to zero dimension. This measurement scheme allows us to exploit the compressibility of a natural scene in multiple domains, leading to highly efficient data acquisition. We demonstrated our method in several demanding applications, including enabling tunable single-pixel full-waveform hyperspectral light detection and ranging (LIDAR) for the first time.
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Affiliation(s)
- Jongchan Park
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Liang Gao
- Department of Bioengineering, University of California, Los Angeles, CA, USA
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Gao Z, Li M, Zheng P, Xiong J, Tang Z, Liu HC. Single-pixel imaging with Gao-Boole patterns. OPTICS EXPRESS 2022; 30:35923-35936. [PMID: 36258532 DOI: 10.1364/oe.464625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/09/2022] [Indexed: 06/16/2023]
Abstract
Single-pixel imaging (SPI) can perceive the world using only a single-pixel detector, but long sampling times with a series of patterns are inevitable for SPI, which is the bottleneck for its practical application. Developing new patterns to reduce the sampling times might provide opportunities to address this challenge. Based on the Kronecker product of Hadamard matrix, we here design a complete set of new patterns, called Gao-Boole patterns, for SPI. Compared to orthogonal Hadamard basis patterns with elements valued as +1 or -1, our Gao-Boole patterns are non-orthogonal ones and the element values are designed as +1 or 0. Using our Gao-Boole patterns, the reconstructed quality of a target image (N × N pixels) is as high as the Hadamard one but only with half pattern numbers of the Hadamard ones, for both full sampling (N2 for Gao-Boole patterns, 2N2 for Hadamard basis patterns) and undersampling cases in experiment. Effectively reducing the patterns numbers and sampling times without sacrificing imaging quality, our designed Gao-Boole patterns provide a superior option for structural patterns in SPI and help to steer SPI toward practical imaging application.
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Wang Z, Zhao W, Zhai A, He P, Wang D. DQN based single-pixel imaging. OPTICS EXPRESS 2021; 29:15463-15477. [PMID: 33985246 DOI: 10.1364/oe.422636] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/28/2021] [Indexed: 06/12/2023]
Abstract
For an orthogonal transform based single-pixel imaging (OT-SPI), to accelerate its speed while degrading as little as possible of its imaging quality, the normal way is to artificially plan the sampling path for optimizing the sampling strategy based on the characteristic of the orthogonal transform. Here, we propose an optimized sampling method using a Deep Q-learning Network (DQN), which considers the sampling process as decision-making, and the improvement of the reconstructed image as feedback, to obtain a relatively optimal sampling strategy for an OT-SPI. We verify the effectiveness of the method through simulations and experiments. Thanks to the DQN, the proposed single-pixel imaging technique is capable of obtaining an optimal sampling strategy directly, and therefore it requires no artificial planning of the sampling path there, which eliminates the influence of the imperfect sampling path planning on the imaging performance.
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Imaging reconstruction comparison of different ghost imaging algorithms. Sci Rep 2020; 10:14626. [PMID: 32884085 PMCID: PMC7471319 DOI: 10.1038/s41598-020-71642-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 07/22/2020] [Indexed: 01/31/2023] Open
Abstract
As an indirect and computational imaging approach, imaging reconstruction efficiency is critical for ghost imaging (GI). Here, we compare different GI algorithms, including logarithmic GI and exponential GI we proposed, by numerically analysing their imaging reconstruction efficiency and error tolerance. Simulation results show that compressive GI algorithm has the highest reconstruction efficiency due to its global optimization property. Error tolerance studies further manifest that compressive GI and exponential GI are sensitive to the error ratio. By replacing the bucket input of compressive GI with different bucket object signal functions, we integrate compressive GI with other GI algorithms and discuss their imaging efficiency. With the combination between the differential GI (or normalized GI) and compressive GI, both reconstruction efficiency and error tolerance will present the best performance. Moreover, an optical encryption is proposed by combining logarithmic GI, exponential GI and compressive GI, which can enhance the encryption security based on GI principle.
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