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\n\n \n \n \n \n \n \n Using Smart Offices to Predict Occupational Stress.\n \n \n \n \n\n\n \n Alberdi, A.; Aztiria, A.; Basarab, A.; and Cook, D. J.\n\n\n \n\n\n\n
International Journal of Industrial Ergonomics, 67: 13–26. septembre 2018.\n
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@Article{ Al2018.2,\r\nauthor = {Alberdi, Ane and Aztiria, Asier and Basarab, Adrian and Cook, Diane J.},\r\ntitle = "{Using Smart Offices to Predict Occupational Stress}",\r\njournal = {International Journal of Industrial Ergonomics},\r\npublisher = {Elsevier},\r\naddress = {http://www.elsevier.com/},\r\nyear = {2018},\r\nmonth = {septembre},\r\nvolume = {67},\r\npages = {13--26},\r\nlanguage = {anglais},\r\nURL = {http://oatao.univ-toulouse.fr/22405/}\r\n}\r\n
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\n\n \n \n \n \n \n \n An axially-variant kernel imaging model applied to ultrasound image reconstruction.\n \n \n \n \n\n\n \n Florea, M. I.; Basarab, A.; Kouamé, D.; and Vorobyov, S. A.\n\n\n \n\n\n\n
IEEE Signal Processing Letters, 25(7): 961–965. juillet 2018.\n
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@Article{ Fl2018.2,\r\nauthor = {Florea, Mihai I. and Basarab, Adrian and Kouamé, Denis and Vorobyov, Sergiy A.},\r\ntitle = "{An axially-variant kernel imaging model applied to ultrasound image reconstruction}",\r\njournal = {IEEE Signal Processing Letters},\r\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\r\naddress = {http://www.ieee.org/},\r\nyear = {2018},\r\nmonth = {juillet},\r\nvolume = {25},\r\nnumber = {7},\r\npages = {961--965},\r\nlanguage = {anglais},\r\nURL = {http://oatao.univ-toulouse.fr/22383/}\r\n}\r\n
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\n\n \n \n \n \n \n Smart home-based prediction of multi-domainsymptoms related to Alzheimer's Disease.\n \n \n \n\n\n \n Alberdi, A.; Weakley, A.; Schmitter-Edgecombe, M.; J. Cook, D.; Aztiria, A.; Basarab, A.; and Barrenechea, M.\n\n\n \n\n\n\n
IEEE Journal of Biomedical and Health Informatics, 22(6): 1720–1731. novembre 2018.\n
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@Article{ Al2018.1,\r\nauthor = {Alberdi, Ane and Weakley, Alyssa and Schmitter-Edgecombe, Maureen and J. Cook, Diane and Aztiria, Asier and Basarab, Adrian and Barrenechea, Maitane},\r\ntitle = "{Smart home-based prediction of multi-domainsymptoms related to Alzheimer's Disease}",\r\njournal = {IEEE Journal of Biomedical and Health Informatics},\r\npublisher = {IEEE},\r\naddress = {http://www.ieee.org/},\r\nyear = {2018},\r\nmonth = {novembre},\r\nvolume = {22},\r\nnumber = {6},\r\npages = {1720--1731},\r\nlanguage = {anglais}\r\n}\r\n
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\n\n \n \n \n \n \n \n Comparison of an adaptive local thresholding method on CBCT and µCT endodontic images.\n \n \n \n \n\n\n \n Michetti, J.; Basarab, A.; Diemer, F.; and Kouamé, D.\n\n\n \n\n\n\n
Physics in Medicine and Biology, 63: 1–10. janvier 2018.\n
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@Article{ MiBaDiKo2018.1,\r\nauthor = {Michetti, Jérôme and Basarab, Adrian and Diemer, Franck and Kouamé, Denis},\r\ntitle = "{Comparison of an adaptive local thresholding method on CBCT and µCT endodontic images}",\r\njournal = {Physics in Medicine and Biology},\r\npublisher = {IOP Science},\r\naddress = {http://iopscience.iop.org},\r\nyear = {2018},\r\nmonth = {janvier},\r\nvolume = {63},\r\npages = {1--10},\r\nlanguage = {anglais},\r\nURL = {https://doi.org/10.1088/1361-6560/aa90ff - https://oatao.univ-toulouse.fr/22111/},\r\n}\r\n
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\n\n \n \n \n \n \n Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning.\n \n \n \n\n\n \n Ouzir, N.; Basarab, A.; Liebgott, H.; Harbaoui, B.; and Tourneret, J.\n\n\n \n\n\n\n
IEEE Transactions on Image Processing, 27(1): 64–77. janvier 2018.\n
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@Article{ OuBaLiHaTo2018.1,\r\nauthor = {Ouzir, Nora and Basarab, Adrian and Liebgott, Hervé and Harbaoui, Brahim and Tourneret, Jean-Yves},\r\ntitle = "{Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning}",\r\njournal = {IEEE Transactions on Image Processing},\r\npublisher = {IEEE},\r\naddress = {http://www.ieee.org/},\r\nyear = {2018},\r\nmonth = {janvier},\r\nvolume = {27},\r\nnumber = {1},\r\npages = {64--77},\r\nlanguage = {anglais}\r\n}\r\n
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\n\n \n \n \n \n \n \n Approximate Message Passing Reconstruction of Quantitative Acoustic Microscopy Images.\n \n \n \n \n\n\n \n Kim, J.; Mamou, J.; Hill, P.; Canagarajah, N.; Kouamé, D.; Basarab, A.; and Achim, A.\n\n\n \n\n\n\n
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Special Issue on Sparsity driven methods in medical ultrasound, 65(3): 327–338. mars 2018.\n
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@Article{ KiMaHiCaKoBaAc2018.1,\r\nauthor = {Kim, Jonghoon and Mamou, Jonathan and Hill, Paul and Canagarajah, Nishan and Kouamé, Denis and Basarab, Adrian and Achim, Alin},\r\ntitle = "{Approximate Message Passing Reconstruction of Quantitative Acoustic Microscopy Images}",\r\njournal = {IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Special Issue on Sparsity driven methods in medical ultrasound},\r\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\r\naddress = {http://www.ieee.org/},\r\nyear = {2018},\r\nmonth = {mars},\r\nvolume = {65},\r\nnumber = {3},\r\npages = {327--338},\r\nlanguage = {anglais},\r\nURL = {https://doi.org/10.1109/TUFFC.2017.2731627 - https://oatao.univ-toulouse.fr/22088/},\r\nabstract = {A novel framework for compressive sensing (CS) data acquisition and reconstruction in quantitative acoustic microscopy (QAM) is presented. Three different CS patterns, adapted to the specifics of QAM systems, were investigated as an alternative to the current raster-scanning approach. They consist of diagonal sampling, a row random, and a spiral scanning pattern and can all significantly reduce both the acquisition time and the amount of sampled data. For subsequent image reconstruction, we design and implement an innovative technique, whereby a recently proposed approximate message passing method is adapted to account for the underlying data statistics. A Cauchy maximum a posteriori image denoising algorithm is thus employed to account for the non-Gaussianity of QAM wavelet coefficients. The proposed methods were tested retrospectively on experimental data acquired with a 250- or 500-MHz QAM system. The experimental data were obtained from a human lymph node sample (250 MHz) and human cornea (500 MHz). Reconstruction results showed that the best performance is obtained using a spiral sensing pattern combined with the Cauchy denoiser in the wavelet domain. The spiral sensing matrix reduced the number of spatial samples by a factor of 2 and led to an excellent peak signal-to-noise ratio of 43.21 dB when reconstructing QAM speed-of-sound images of a human lymph node. These results demonstrate that the CS approach could significantly improve scanning time, while reducing costs of future QAM systems.}\r\n}\r\n
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\n A novel framework for compressive sensing (CS) data acquisition and reconstruction in quantitative acoustic microscopy (QAM) is presented. Three different CS patterns, adapted to the specifics of QAM systems, were investigated as an alternative to the current raster-scanning approach. They consist of diagonal sampling, a row random, and a spiral scanning pattern and can all significantly reduce both the acquisition time and the amount of sampled data. For subsequent image reconstruction, we design and implement an innovative technique, whereby a recently proposed approximate message passing method is adapted to account for the underlying data statistics. A Cauchy maximum a posteriori image denoising algorithm is thus employed to account for the non-Gaussianity of QAM wavelet coefficients. The proposed methods were tested retrospectively on experimental data acquired with a 250- or 500-MHz QAM system. The experimental data were obtained from a human lymph node sample (250 MHz) and human cornea (500 MHz). Reconstruction results showed that the best performance is obtained using a spiral sensing pattern combined with the Cauchy denoiser in the wavelet domain. The spiral sensing matrix reduced the number of spatial samples by a factor of 2 and led to an excellent peak signal-to-noise ratio of 43.21 dB when reconstructing QAM speed-of-sound images of a human lymph node. These results demonstrate that the CS approach could significantly improve scanning time, while reducing costs of future QAM systems.\n
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\n\n \n \n \n \n \n \n Semi-blind ultrasound image deconvolution from compressed measurements.\n \n \n \n \n\n\n \n Chen, Z.; Basarab, A.; and Kouamé, D.\n\n\n \n\n\n\n
Ingénierie et Recherche BioMédicale, 39(1): 26–34. février 2018.\n
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@Article{ ChBaKo2018.1,\r\nauthor = {Chen, Zhouye and Basarab, Adrian and Kouamé, Denis},\r\ntitle = "{Semi-blind ultrasound image deconvolution from compressed measurements}",\r\njournal = {Ingénierie et Recherche BioMédicale},\r\npublisher = {Elsevier Masson},\r\naddress = {http://elsevier-masson.fr},\r\nyear = {2018},\r\nmonth = {février},\r\nvolume = {39},\r\nnumber = {1},\r\npages = {26--34},\r\nlanguage = {anglais},\r\nURL = {https://doi.org/10.1016/j.irbm.2017.11.002 - https://oatao.univ-toulouse.fr/24697/},\r\nabstract = {The recently proposed framework of ultrasound compressive deconvolution offers the possibility of decreasing the acquired data while improving the image spatial resolution. By combining compressive sampling and image deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. The performance of the method is evaluated on both simulated and in vivo ultrasound data.}\r\n}\r\n
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\n The recently proposed framework of ultrasound compressive deconvolution offers the possibility of decreasing the acquired data while improving the image spatial resolution. By combining compressive sampling and image deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. The performance of the method is evaluated on both simulated and in vivo ultrasound data.\n
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\n\n \n \n \n \n \n \n Reconstruction of Quantitative Acoustic Microscopy Images from RF Signals Sampled at Innovation Rate .\n \n \n \n \n\n\n \n Kim, J.; Mamou, J.; Kouamé, D.; Achim, A.; and Basarab, A.\n\n\n \n\n\n\n In
IEEE International Ultrasonics Symposium, Kobe, Japan, 22/10/2018-25/10/2018, pages 1–4, http://www.ieee.org/, octobre 2018. IEEE : Institute of Electrical and Electronics Engineers\n
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@InProceedings{ Ki2018.1,\r\nauthor = {Kim, Jonghoon and Mamou, Jonathan and Kouamé, Denis and Achim, Alin and Basarab, Adrian},\r\ntitle = "{Reconstruction of Quantitative Acoustic Microscopy Images from RF Signals Sampled at Innovation Rate }",\r\nbooktitle = "{IEEE International Ultrasonics Symposium, Kobe, Japan, 22/10/2018-25/10/2018}",\r\nyear = {2018},\r\nmonth = {octobre},\r\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\r\naddress = {http://www.ieee.org/},\r\npages = {1--4},\r\nlanguage = {anglais},\r\nURL = {https://doi.org/10.1109/ULTSYM.2018.8580075 - https://oatao.univ-toulouse.fr/24718/},\r\nabstract = {The principle of quantitative acoustic microscopy (QAM) is to form two-dimensional acoustic parameter maps from a collection of radiofrequency (RF) signals acquired by raster scanning a biological sample. Despite their relatively simple structure, i.e. two mai / mayn reflections, QAM RF signals are currently sampled at very high frequencies, e.g., at 2.5 GHz for QAM system employing a single-element transducer with a center frequency of 250-MHz. The use of such high sampling frequencies is challenging because of the potentially large amount of acquired data and the cost of the necessary analog to digital converters. In this work, we propose a sampling scheme based on the finite rate of innovation theory that exploits the limited numbers of degrees of freedom of QAM RF signals and allows the reconstruction of accurate acoustic maps from a very limited number of samples.}\r\n}\r\n
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\n The principle of quantitative acoustic microscopy (QAM) is to form two-dimensional acoustic parameter maps from a collection of radiofrequency (RF) signals acquired by raster scanning a biological sample. Despite their relatively simple structure, i.e. two mai / mayn reflections, QAM RF signals are currently sampled at very high frequencies, e.g., at 2.5 GHz for QAM system employing a single-element transducer with a center frequency of 250-MHz. The use of such high sampling frequencies is challenging because of the potentially large amount of acquired data and the cost of the necessary analog to digital converters. In this work, we propose a sampling scheme based on the finite rate of innovation theory that exploits the limited numbers of degrees of freedom of QAM RF signals and allows the reconstruction of accurate acoustic maps from a very limited number of samples.\n
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\n\n \n \n \n \n \n \n Cardiac Motion Estimation with Dictionary Learning and Robust Sparse Coding in Ultrasound Imaging .\n \n \n \n \n\n\n \n Ouzir, N. L.; Chiril, P.; Basarab, A.; and Tourneret, J.\n\n\n \n\n\n\n In
IEEE International Ultrasonics Symposium, Kobe, Japan, 22/10/2018-25/10/2018, pages 1–4, http://www.ieee.org/, octobre 2018. IEEE : Institute of Electrical and Electronics Engineers\n
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@InProceedings{ Ou2018.2,\r\nauthor = {Ouzir, Nora Leïla and Chiril, Patricia and Basarab, Adrian and Tourneret, Jean-Yves},\r\ntitle = "{Cardiac Motion Estimation with Dictionary Learning and Robust Sparse Coding in Ultrasound Imaging }",\r\nbooktitle = "{IEEE International Ultrasonics Symposium, Kobe, Japan, 22/10/2018-25/10/2018}",\r\nyear = {2018},\r\nmonth = {octobre},\r\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\r\naddress = {http://www.ieee.org/},\r\npages = {1--4},\r\nlanguage = {anglais},\r\nURL = {https://doi.org/10.1109/ULTSYM.2018.8580022 - https://oatao.univ-toulouse.fr/24717/},\r\nabstract = {Cardiac motion estimation from ultrasound images is an ill-posed problem that needs regularization to stabilize the solution. In this work, regularization is achieved by exploiting the sparseness of cardiac motion fields when decomposed in an appropriate dictionary, as well as their smoothness through a classical total variation term. The mai / mayn contribution of this work is to robustify the sparse coding step in order to handle anomalies, i.e., motion patterns that significantly deviate from the expected model. The proposed approach uses an ADMM-based optimization algorithm in order to simultaneously recover the sparse representations and the outlier components. It is evaluated using two realistic simulated datasets with available ground-truth, containing native outliers and corrupted by synthetic attenuation and clutter artefacts.}\r\n}\r\n
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\n Cardiac motion estimation from ultrasound images is an ill-posed problem that needs regularization to stabilize the solution. In this work, regularization is achieved by exploiting the sparseness of cardiac motion fields when decomposed in an appropriate dictionary, as well as their smoothness through a classical total variation term. The mai / mayn contribution of this work is to robustify the sparse coding step in order to handle anomalies, i.e., motion patterns that significantly deviate from the expected model. The proposed approach uses an ADMM-based optimization algorithm in order to simultaneously recover the sparse representations and the outlier components. It is evaluated using two realistic simulated datasets with available ground-truth, containing native outliers and corrupted by synthetic attenuation and clutter artefacts.\n
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\n\n \n \n \n \n \n \n Restoration of ultrasound images using spatially-variant kernel deconvolution .\n \n \n \n \n\n\n \n Florea, M. I.; Basarab, A.; Kouamé, D.; and Vorobyov, S. A.\n\n\n \n\n\n\n In
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Calgary, Canada, 15/04/2018-20/04/2018, pages 796–800, http://www.ieee.org/, avril 2018. IEEE : Institute of Electrical and Electronics Engineers\n
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@InProceedings{ Fl2018.1,\r\nauthor = {Florea, Mihai I. and Basarab, Adrian and Kouamé, Denis and Vorobyov, Sergiy A.},\r\ntitle = "{Restoration of ultrasound images using spatially-variant kernel deconvolution }",\r\nbooktitle = "{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Calgary, Canada, 15/04/2018-20/04/2018}",\r\nyear = {2018},\r\nmonth = {avril},\r\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\r\naddress = {http://www.ieee.org/},\r\npages = {796--800},\r\nlanguage = {anglais},\r\nURL = {https://oatao.univ-toulouse.fr/22329/}\r\n}\r\n
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\n\n \n \n \n \n \n \n Restoration of ultrasonic images using non-linear system identification and deconvolution .\n \n \n \n \n\n\n \n Hourani, M.; Basarab, A.; Kouamé, D.; Girault, J.; and Tourneret, J.\n\n\n \n\n\n\n In
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2018), Washington, 04/04/2018-07/04/2018, pages 1166–1169, http://www.ieee.org/, avril 2018. IEEE : Institute of Electrical and Electronics Engineers\n
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@InProceedings{ Ho2018.3,\r\nauthor = {Hourani, Mohamad and Basarab, Adrian and Kouamé, Denis and Girault, Jean-Marc and Tourneret, Jean-Yves},\r\ntitle = "{Restoration of ultrasonic images using non-linear system identification and deconvolution }",\r\nbooktitle = "{IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2018), Washington, 04/04/2018-07/04/2018}",\r\nyear = {2018},\r\nmonth = {avril},\r\npublisher = {IEEE : Institute of Electrical and Electronics Engineers},\r\naddress = {http://www.ieee.org/},\r\npages = {1166--1169},\r\nlanguage = {anglais},\r\nURL = {https://doi.org/10.1109/ISBI.2018.8363778 - https://oatao.univ-toulouse.fr/24698/},\r\nabstract = {This paper studies a new ultrasound image restoration method based on a non-linear forward model. A Hammerstein polynomial-based non-linear image formation model is considered to identify the system impulse response in baseband and around the second harmonic. The identification process is followed by a joint deconvolution technique minimizing an appropriate cost function. This cost function is constructed from two data fidelity terms exploiting the linear and non-linear model components, penalized by an additive-norm regularization enforcing sparsity of the solution. An alternating optimization approach is considered to minimize the penalized cost function, allowing the tissue reflectivity function to be estimated. Results on synthetic ultrasound images are finally presented to evaluate the algorithm performance.}\r\n}\r\n
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\n This paper studies a new ultrasound image restoration method based on a non-linear forward model. A Hammerstein polynomial-based non-linear image formation model is considered to identify the system impulse response in baseband and around the second harmonic. The identification process is followed by a joint deconvolution technique minimizing an appropriate cost function. This cost function is constructed from two data fidelity terms exploiting the linear and non-linear model components, penalized by an additive-norm regularization enforcing sparsity of the solution. An alternating optimization approach is considered to minimize the penalized cost function, allowing the tissue reflectivity function to be estimated. Results on synthetic ultrasound images are finally presented to evaluate the algorithm performance.\n
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\n\n \n \n \n \n \n Coupling reconstruction and motion estimation for dynamic MRI through optical flow constraint .\n \n \n \n\n\n \n Zhao, N.; O'Connor, D.; Ruan, D.; Basarab, A.; and Sheng, K.\n\n\n \n\n\n\n In
SPIE Medical Imaging, Houston, Texas United States, 10/02/18-15/02/18, volume 10574, pages (electronic medium), http://spie.org, février 2018. SPIE - Medical Imaging\n
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@InProceedings{ ZhORuBaSh2018.1,\r\nauthor = {Zhao, Ningning and O'Connor, Daniel and Ruan, Dan and Basarab, Adrian and Sheng, Ke},\r\ntitle = "{Coupling reconstruction and motion estimation for dynamic MRI through optical flow constraint }",\r\nbooktitle = "{SPIE Medical Imaging, Houston, Texas United States, 10/02/18-15/02/18}",\r\nyear = {2018},\r\nmonth = {février},\r\npublisher = {SPIE - Medical Imaging},\r\naddress = {http://spie.org},\r\nvolume = {10574},\r\npages = {(electronic medium)},\r\nlanguage = {anglais}\r\n}\r\n
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