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Journal articles on the topic 'Patial Frequency Domain Imaging'

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Author: Grafiati

Published: 26 October 2023

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1

Lin, Jingyu, Yebin Liu, Jinli Suo, and Qionghai Dai. "Frequency-Domain Transient Imaging." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 5 (May 1, 2017): 937–50. http://dx.doi.org/10.1109/tpami.2016.2560814.

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2

Yang Hong, 杨虹, 黄远辉 Huang Yuanhui, 苗少峰 Miao Shaofeng, 宫睿 Gong Rui, 邵晓鹏 Shao Xiaopeng, and 毕祥丽 Bi Xiangli. "Frequency-domain photoacoustic imaging system." Infrared and Laser Engineering 45, no. 4 (2016): 0424001. http://dx.doi.org/10.3788/irla201645.0424001.

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3

Jiang, Shan, Meiling Guan, Jiamin Wu, Guocheng Fang, Xinzhu Xu, Dayong Jin, Zhen Liu, et al. "Frequency-domain diagonal extension imaging." Advanced Photonics 2, no. 03 (June 2, 2020): 1. http://dx.doi.org/10.1117/1.ap.2.3.036005.

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4

Zander, Dani S. "Volumetric Optical Frequency Domain Imaging." Chest 143, no. 1 (January 2013): 10–12. http://dx.doi.org/10.1378/chest.12-1864.

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5

Haworth, Kevin J., Kenneth B. Bader, Kyle T. Rich, Christy K. Holland, and T. Douglas Mast. "Frequency-domain passive cavitation imaging." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 3458. http://dx.doi.org/10.1121/1.4987172.

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6

Zhang, Guang-Ming, Derek R. Braden, David M. Harvey, and David R. Burton. "Acoustic time-frequency domain imaging." Journal of the Acoustical Society of America 128, no. 5 (November 2010): EL323—EL328. http://dx.doi.org/10.1121/1.3505760.

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7

Konecky, Soren D. "Imaging scattering orientation with spatial frequency domain imaging." Journal of Biomedical Optics 16, no. 12 (December 1, 2011): 126001. http://dx.doi.org/10.1117/1.3657823.

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8

Yun, S., G. Tearney, Johannes de Boer, N. Iftimia, and B. Bouma. "High-speed optical frequency-domain imaging." Optics Express 11, no. 22 (November 3, 2003): 2953. http://dx.doi.org/10.1364/oe.11.002953.

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9

Haworth, Kevin J., Kenneth B. Bader, Kyle T. Rich, Christy K. Holland, and T. Douglas Mast. "Quantitative Frequency-Domain Passive Cavitation Imaging." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 64, no. 1 (January 2017): 177–91. http://dx.doi.org/10.1109/tuffc.2016.2620492.

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10

Vakoc, B. J., S. H. Yun, J. F. de Boer, G. J. Tearney, and B. E. Bouma. "Phase-resolved optical frequency domain imaging." Optics Express 13, no. 14 (2005): 5483. http://dx.doi.org/10.1364/opex.13.005483.

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11

Zhao, Gao Yuan, and Chao Lu. "Time Domain/Frequency Domain SAFT Imaging in Thin-Diameter Rod." Applied Mechanics and Materials 380-384 (August 2013): 3648–52. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3648.

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The ultrasonic flaw reflected signal of the thin-diameter rod was acquired by the general ultrasonic C-scan testing device. And synthetic aperture focusing technique be used in ultrasonic imaging of thin-diameter rod. Respectively, Comparing B-scan imaging and Conventional synthetic aperture focusing technique imaging as well as Frequency domain synthetic aperture focusing technique imaging . The final results show that both time domain and frequency domain synthetic aperture method could obtain a higher signal-to-noise ratio and testing resolution.

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12

Park, Jae-Hyeung, and Kyeong-Min Jeong. "Frequency domain depth filtering of integral imaging." Optics Express 19, no. 19 (September 9, 2011): 18729. http://dx.doi.org/10.1364/oe.19.018729.

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13

Quinsac, Céline, Adrian Basarab, and Denis Kouamé. "Frequency Domain Compressive Sampling for Ultrasound Imaging." Advances in Acoustics and Vibration 2012 (May 30, 2012): 1–16. http://dx.doi.org/10.1155/2012/231317.

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Compressed sensing or compressive sampling is a recent theory that originated in the applied mathematics field. It suggests a robust way to sample signals or images below the classic Shannon-Nyquist theorem limit. This technique has led to many applications, and has especially been successfully used in diverse medical imaging modalities such as magnetic resonance imaging, computed tomography, or photoacoustics. This paper first revisits the compressive sampling theory and then proposes several strategies to perform compressive sampling in the context of ultrasound imaging. Finally, we show encouraging results in 2D and 3D, on high- and low-frequency ultrasound images.

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14

Zhdanov, Michael S., Peter Traynin, and John R. Booker. "Underground imaging by frequency‐domain electromagnetic migration." GEOPHYSICS 61, no. 3 (May 1996): 666–82. http://dx.doi.org/10.1190/1.1443995.

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A new method of the resistivity imaging based on frequency‐domain electromagnetic migration is developed. Electromagnetic (EM) migration involves downward diffusion of observed EM fields whose time flow has been reversed. Unlike downward analytical continuation, migration is a stable procedure that accurately restores the phase of the upgoing field inside the Earth. This method is indented for the processing and interpretation of EM data collected for both TE and TM modes of plane‐wave excitation. Until recently, the method could be applied only for determining the position of anomalous structures and for finding interfaces between layers of different conductivity. There were no well developed approaches to the resistivity imaging, which is the key problem in the inversion of EM data. We provide a novel approach to determining not only the position of anomalous structures but their resistivity as well. The main difficulty in the practical realization of this approach is determining the background resistivity distribution for migration. We discuss the method of the solution of this problem based on differential transformation of apparent resistivity curves. The final goal of migration is to provide a first order interpretation using a computational effort equivalent to a forward modeling calculation.

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15

Pelet, Serge, Michael J. R. Previte, Daekeun Kim, Ki Hean Kim, Tsu-Te J. SU, and Peter T. C. So. "Frequency domain lifetime and spectral imaging microscopy." Microscopy Research and Technique 69, no. 11 (2006): 861–74. http://dx.doi.org/10.1002/jemt.20361.

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16

Li, Jianan, Fabio Feroldi, Joop de Lange, Johannes M. A. Daniels, Katrien Grünberg, and Johannes F. de Boer. "Polarization sensitive optical frequency domain imaging system for endobronchial imaging." Optics Express 23, no. 3 (February 4, 2015): 3390. http://dx.doi.org/10.1364/oe.23.003390.

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17

Huang, Guo Rong, Wei Jun Zhong, Chao Wang, and Yuan Pei Wu. "A Novel Imaging Approach for Dispersive Target Electromagnetic Imaging." Applied Mechanics and Materials 427-429 (September 2013): 1972–76. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1972.

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A novel imaging approach for dispersive target electromagnetic imaging based on the time-frequency analysis is presented in this paper. On the foundation of researching the time-domain imaging principle of dispersive target, the basic theory of back projection imaging algorithm and time-frequency analysis is discussed. The sampling data obtained by simulation of target electromagnetic wave scattering which is accomplished by finite-difference time domain method (FDTD). According to the simulating data, after disposing by Wigner-Ville transfer, the sampling data is processed integral management in frequency-domain, and the imaging experiment is carried out. The experiment results show that the improved imaging arithmetic could estimate the shape of dispersive target and improve the imaging quality.

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18

Sun, Guanqun, Fangzheng Zhang, Shilong Pan, and Xingwei Ye. "Frequency-domain versus time-domain imaging for photonics-based broadband radar." Electronics Letters 56, no. 24 (November 26, 2020): 1330–32. http://dx.doi.org/10.1049/el.2020.2273.

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19

Motaghian Nezam, S. M. R., B. J. Vakoc, A. E. Desjardins, G. J. Tearney, and B. E. Bouma. "Increased ranging depth in optical frequency domain imaging by frequency encoding." Optics Letters 32, no. 19 (September 17, 2007): 2768. http://dx.doi.org/10.1364/ol.32.002768.

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20

Oh, W. Y., S. H. Yun, B. J. Vakoc, M. Shishkov, A. E. Desjardins, B. H. Park, J. F. de Boer, G. J. Tearney, and B. E. Bouma. "High-speed polarization sensitive optical frequency domain imaging with frequency multiplexing." Optics Express 16, no. 2 (January 14, 2008): 1096. http://dx.doi.org/10.1364/oe.16.001096.

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21

Cui, An-Jing, Dao-Jing Li, Jiang Wu, Kai Zhou, and Jing-Han Gao. "Sparse sampling in frequency domain and laser imaging." Acta Physica Sinica 71, no. 5 (2022): 058705. http://dx.doi.org/10.7498/aps.71.20211408.

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The monochromaticity of the laser and the characteristics of the natural image’s spectrum, including sparsity and concentrating in the low frequency range, make it possible to sample the image spectrum sparsely. Based on small-scale laser detectors and the introduced laser reference signals, a method of laser imaging with sparse sampling in frequency domain is proposed in this paper. The principle of frequency sparse sampling laser imaging and the imaging system structure are introduced. The simulation results of spectrum and complex images reconstructed are given. Both the effects of the signals’ parameters, such as the ratio of the reference laser signal amplitude to the laser echo spectrum amplitude and the initial phase of the laser reference signal, on reconstruction results are investigated. The reconstruction results are evaluated by correlation coefficient, mean square error (MSE), and structural similarity index (SSIM). For the strong correlation between phase and amplitude of the laser echo complex image, the amplitude image and the phase image are both set to be 256 × 256 diagram. The sparse laser detector plane array consists of 5 64 × 64 frequency domain laser detector arrays, which form a cross and make a sparsity rate of 31.25%(5/16). The simulation results show that the correlation coefficient, MSE and SSIM of the spectrum reconstructed are 0.96, 22.14, 1.00 and those of the complex image reconstructed are 0.96, 1857.25 and 0.67 respectively. The simulation results indicate that the method proposed is effective. However, the method requires the laser reference signal amplitude to be about 30 times the mean value of the laser echo spectrum amplitude, which reduces the dynamic range of the detectors. The initial phase of the laser reference signal has no obvious effect on the reconstruction results.

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22

Taki, Hirofumi, Takuya Sakamoto, Makoto Yamakawa, Tsuyoshi Shiina, and Toru Sato. "High Resolution Ultrasound Imaging Using Frequency Domain Interferometry." IEEJ Transactions on Electronics, Information and Systems 132, no. 10 (2012): 1552–57. http://dx.doi.org/10.1541/ieejeiss.132.1552.

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23

Bahrami, Siroos, Ahmad Cheldavi, and Ali Abdolali. "Ultrawideband Time-Reversal Imaging With Frequency Domain Sampling." IEEE Geoscience and Remote Sensing Letters 11, no. 3 (March 2014): 597–601. http://dx.doi.org/10.1109/lgrs.2013.2272033.

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24

Zamani, A., S. A. Rezaeieh, and A. M. Abbosh. "Lung cancer detection using frequency‐domain microwave imaging." Electronics Letters 51, no. 10 (May 2015): 740–41. http://dx.doi.org/10.1049/el.2015.0230.

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25

Mazhar, Amaan, David J. Cuccia, Tyler B. Rice, Stefan A. Carp, Anthony J. Durkin, David A. Boas, Bernard Choi, and Bruce J. Tromberg. "Laser speckle imaging in the spatial frequency domain." Biomedical Optics Express 2, no. 6 (May 13, 2011): 1553. http://dx.doi.org/10.1364/boe.2.001553.

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26

Oravecz, Michael G. "Frequency domain processing of scanning acoustic imaging signals." Journal of the Acoustical Society of America 118, no. 4 (2005): 2115. http://dx.doi.org/10.1121/1.2125269.

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27

Judkewitz, Benjamin, and Changhuei Yang. "Axial standing-wave illumination frequency-domain imaging (SWIF)." Optics Express 22, no. 9 (April 30, 2014): 11001. http://dx.doi.org/10.1364/oe.22.011001.

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28

Reynolds, J. S., T. L. Troy, and E. M. Sevick-Muraca. "Multipixel Techniques for Frequency-Domain Photon Migration Imaging." Biotechnology Progress 13, no. 5 (October 7, 1997): 669–80. http://dx.doi.org/10.1021/bp970085g.

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29

Birmingham, John J. "Frequency-domain lifetime imaging methods at unilever research." Journal of Fluorescence 7, no. 1 (March 1997): 45–54. http://dx.doi.org/10.1007/bf02764576.

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30

LeBoulluec, Peter, Hanli Liu, and Baohong Yuan. "A cost-efficient frequency-domain photoacoustic imaging system." American Journal of Physics 81, no. 9 (September 2013): 712–17. http://dx.doi.org/10.1119/1.4816242.

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31

Arnesano, Cosimo, Ylenia Santoro, and Enrico Gratton. "Digital parallel frequency-domain spectroscopy for tissue imaging." Journal of Biomedical Optics 17, no. 9 (September 14, 2012): 0960141. http://dx.doi.org/10.1117/1.jbo.17.9.096014.

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32

Chen, Yueli, Daniel M. de Bruin, Charles Kerbage, and Johannes F. de Boer. "Spectrally balanced detection for optical frequency domain imaging." Optics Express 15, no. 25 (2007): 16390. http://dx.doi.org/10.1364/oe.15.016390.

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33

Sevick, E. M., J. R. Lakowicz, H. Szmacinski, K. Nowaczyk, and M. L. Johnson. "Frequency domain imaging of absorbers obscured by scattering." Journal of Photochemistry and Photobiology B: Biology 16, no. 2 (October 1992): 169–85. http://dx.doi.org/10.1016/1011-1344(92)80007-i.

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34

Bialkowski, M. E., Y. Wang, A. Abu Bakar, and W. C. Khor. "Microwave imaging using ultra wideband frequency-domain data." Microwave and Optical Technology Letters 54, no. 1 (November 22, 2011): 13–18. http://dx.doi.org/10.1002/mop.26465.

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35

Zheng, Bowen, Pengcheng Ji, Yangyang Shi, and Yuanjin Yu. "Edge detection ghost imaging using frequency-domain filtering." Journal of Physics: Conference Series 2478, no. 12 (June 1, 2023): 122063. http://dx.doi.org/10.1088/1742-6596/2478/12/122063.

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Abstract Ghost imaging is a novel imaging method, which enables imaging under harsh environments or quickly extracting image information. The advantage of Ghost imaging in edge detection is that it can directly obtain the edge information of an unknown target. We propose an edge detection method based on two dimensional (2D) discrete cosine transform (DCT) - frequency domain filtering ghost imaging (FDFGI). This method combines the frequency-domain filtering and anisotropic gradient operator to extract the edge information of the unknown object under the under-sampling rate. Compared with traditional methods, this method has a higher signal-to-noise ratio of image edge information extracted under Gaussian noise. It can even achieve edge information extraction under an extremely low sampling rate of about 1.12%.

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36

Zhuang, Zeyu, Jie Zhang, Guoxuan Lian, and Bruce W. Drinkwater. "Comparison of Time Domain and Frequency-Wavenumber Domain Ultrasonic Array Imaging Algorithms for Non-Destructive Evaluation." Sensors 20, no. 17 (September 1, 2020): 4951. http://dx.doi.org/10.3390/s20174951.

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Ultrasonic array imaging algorithms have been widely developed and used for non-destructive evaluation (NDE) in the last two decades. In this paper two widely used time domain algorithms are compared with two emerging frequency domain algorithms in terms of imaging performance and computational speed. The time domain algorithms explored here are the total focusing method (TFM) and plane wave imaging (PWI) and the frequency domain algorithms are the wavenumber algorithm and Lu’s frequency-wavenumber domain implementation of PWI. In order to make a fair comparison, each algorithm was first investigated to choose imaging parameters leading to overall good imaging resolution and signal-to-noise-ratio. To reflect the diversity of samples encountered in NDE, the comparison is made using both a low noise material (aluminium) and a high noise material (copper). It is shown that whilst wavenumber and frequency domain PWI imaging algorithms can lead to fast imaging, they require careful selection of imaging parameters.

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37

Yun, S. H., G. J. Tearney, J. F. de Boer, and B. E. Bouma. "Removing the depth-degeneracy in optical frequency domain imaging with frequency shifting." Optics Express 12, no. 20 (2004): 4822. http://dx.doi.org/10.1364/opex.12.004822.

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38

Lin, Yun, Yutong Liu, Yanping Wang, Shengbo Ye, Yuan Zhang, Yang Li, Wei Li, Hongquan Qu, and Wen Hong. "Frequency Domain Panoramic Imaging Algorithm for Ground-Based ArcSAR." Sensors 20, no. 24 (December 8, 2020): 7027. http://dx.doi.org/10.3390/s20247027.

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The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear rail. It has already been used in deformation monitoring applications. This paper mainly focuses on the accurate and fast imaging algorithms for ArcSAR. The curvature track makes the image focusing challenging and, in the classical frequency domain, fast imaging algorithms that are designed for linear rail SAR cannot be readily applied. This paper proposed an efficient frequency domain imaging algorithm for ArcSAR. The proposed algorithm takes advantage of the angular shift-invariant property of the ArcSAR signal, and it deduces the accurate matched filter in the angular-frequency domain, so panoramic images in polar coordinates with wide swath can be obtained at one time without segmenting strategy. When compared with existing ArcSAR frequency domain algorithms, the proposed algorithm is more accurate and efficient, because it has neither far range nor narrow beam antenna restrictions. The proposed method is validated by both simulation and real data. The results show that our algorithm brings the quality of image close to the time domain back-projection (BP) algorithm at a processing efficiency about two orders of magnitude better, and it has better image quality than the existing frequency domain Lee’s algorithm at a comparable processing speed.

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39

Crum, William R., Elizabeth Berry, John P. Ridgway, U. Mohan Sivananthan, Lip-Bun Tan, and Michael A. Smith. "Frequency-domain simulation of MR tagging." Journal of Magnetic Resonance Imaging 8, no. 5 (September 1998): 1040–50. http://dx.doi.org/10.1002/jmri.1880080507.

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40

Dan, Mai, Meihui Liu, and Feng Gao. "Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging." Applied Sciences 12, no. 15 (July 23, 2022): 7402. http://dx.doi.org/10.3390/app12157402.

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The single-pixel imaging technique is applied to spatial frequency domain imaging (SFDI) to bring significant performance advantages in band extension and sensitivity enhancement. However, the large number of samplings required can cause severe quality degradations in the measured image when imaging a moving target. This work presents a novel method of motion deblurring for single-pixel SFDI. In this method, the Fourier coefficients of the reflected image are measured by the Fourier single-pixel imaging technique. On this basis, a motion-degradation-model-based compensation, which is derived by the phase-shift and frequency-shift properties of Fourier transform, is adopted to eliminate the effects of target displacements on the measurements. The target displacements required in the method are obtained using a fast motion estimation approach. A series of numerical and experimental validations show that the proposed method can effectively deblur the moving targets and accordingly improves the accuracy of the extracted optical properties, rendering it a potentially powerful way of broadening the clinical application of single-pixel SFDI.

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41

Guo-qing YAN, 闫国庆, 杨风暴 Feng-bao YANG, 王肖霞 Xiao-xia WANG, 陶勇 Yong TAO, and 李向燕 Xiang-yan LI. "Fusion Ghost Imaging Method Based on Frequency Domain Decomposition." ACTA PHOTONICA SINICA 49, no. 6 (2020): 610003. http://dx.doi.org/10.3788/gzxb20204906.0610003.

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42

Marone, Alessandro, Wei Tang, Youngwan Kim, Tommy Chen, George Danias, Cathy Guo, Yevgeniya Gartshteyn, et al. "Evaluation of SLE arthritis using frequency domain optical imaging." Lupus Science & Medicine 8, no. 1 (August 2021): e000495. http://dx.doi.org/10.1136/lupus-2021-000495.

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ObjectivesSystemic lupus erythematosus (SLE) affects the joints in up to 95% of patients. The diagnosis and evaluation of SLE arthritis remain challenging in both practice and clinical trials. Frequency domain optical imaging (FDOI) has been previously used to assess joint involvement in inflammatory arthritis. The objective of this study was to evaluate FDOI in SLE arthritis.MethodsNinety-six proximal interphalangeal (PIP) joints from 16 patients with SLE arthritis and 60 PIP joints from 10 age-matched, gender-matched and race/ethnicity-matched controls were examined. A laser beam with a wavelength of 670 nm, 1 mm in diameter and intensity modulated at 300 MHz and 600 MHz was directed onto the dorsal surface of each joint, scanning across a sagittal plane. The transmitted light intensities and phase shifts were measured with an intensified charge-coupled device camera. The data were analysed using Discriminant Analysis and Support Vector Machine algorithms.ResultsThe amplitude and phase of the transmitted light were significantly different between SLE and control PIPs (p<0.05). Receiver operating characteristic (ROC) analysis of cross-validated models showed an Area Under the ROC Curve (AUC)of 0.89 with corresponding sensitivity of 95%, specificity of 79%, and accuracy of 80%.ConclusionThis study is the first evaluation of optical methods in the assessment of SLE arthritis; there was a statistically significant difference in the FDOI signals between patients with SLE and healthy volunteers. The results show that FDOI may have the potential to provide an objective, user-independent, evaluation of SLE PIP joints arthritis.

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43

Shibutani, Hiroki, Kenichi Fujii, Rika Kawakami, Takahiro Imanaka, Kenji Kawai, Satoshi Tsujimoto, Koichiro Matsumura, et al. "Tangential signal dropout artefact in optical frequency domain imaging." EuroIntervention 17, no. 4 (July 2021): e326-e331. http://dx.doi.org/10.4244/eij-d-20-00014.

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44

Zhe Li, Jian Wang, and Qing Huo Liu. "Frequency-Domain Backprojection Algorithm for Synthetic Aperture Radar Imaging." IEEE Geoscience and Remote Sensing Letters 12, no. 4 (April 2015): 905–9. http://dx.doi.org/10.1109/lgrs.2014.2366156.

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45

Wojtkowski, M., A. Kowalczyk, P. Targowski, and I. Gorczyńska. "Frequency Domain Optical Coherence Tomography Techniques in Eye Imaging." Acta Physica Polonica A 102, no. 6 (December 2002): 739–46. http://dx.doi.org/10.12693/aphyspola.102.739.

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46

Liu, Zhe, Chunyang Dai, Xiaoling Zhang, and Jianyu Yang. "Elevation-Dependent Frequency-Domain Imaging for General Bistatic SAR." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 12 (December 2015): 5553–64. http://dx.doi.org/10.1109/jstars.2015.2479679.

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47

Pera, Vivian, Kavon Karrobi, Syeda Tabassum, Fei Teng, and Darren Roblyer. "Optical property uncertainty estimates for spatial frequency domain imaging." Biomedical Optics Express 9, no. 2 (January 18, 2018): 661. http://dx.doi.org/10.1364/boe.9.000661.

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48

Torabzadeh, Mohammad, Il-Yong Park, Randy A. Bartels, Anthony J. Durkin, and Bruce J. Tromberg. "Compressed single pixel imaging in the spatial frequency domain." Journal of Biomedical Optics 22, no. 3 (March 16, 2017): 030501. http://dx.doi.org/10.1117/1.jbo.22.3.030501.

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49

Schäfer, Rudolf, Ivan Soldatov, and Satoshi Arai. "Power frequency domain imaging on Goss-textured electrical steel." Journal of Magnetism and Magnetic Materials 474 (March 2019): 221–35. http://dx.doi.org/10.1016/j.jmmm.2018.10.100.

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50

Jin, H., J. Chen, E. Wu, and K. Yang. "Frequency-domain synthetic aperture focusing for helical ultrasonic imaging." Journal of Applied Physics 121, no. 13 (April 7, 2017): 134901. http://dx.doi.org/10.1063/1.4979369.

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