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Статті в журналах з теми "Spectral Photon Counting Computed Tomography":
Moghiseh, Mahdieh, Emily Searle, Devyani Dixit, Johoon Kim, Yuxi C. Dong, David P. Cormode, Anthony Butler, and Steven P. Gieseg. "Spectral Photon-Counting CT Imaging of Gold Nanoparticle Labelled Monocytes for Detection of Atherosclerosis: A Preclinical Study." Diagnostics 13, no. 3 (January 29, 2023): 499. http://dx.doi.org/10.3390/diagnostics13030499.
Balegamire, Joëlle, Marc Vandamme, Emmanuel Chereul, Salim Si-Mohamed, Samira Azzouz Maache, Eyad Almouazen, Laurent Ettouati, et al. "Iodinated polymer nanoparticles as contrast agent for spectral photon counting computed tomography." Biomaterials Science 8, no. 20 (2020): 5715–28. http://dx.doi.org/10.1039/d0bm01046d.
Sawall, Stefan, Carlo Amato, Laura Klein, Eckhard Wehrse, Joscha Maier, and Marc Kachelrieß. "Toward molecular imaging using spectral photon-counting computed tomography?" Current Opinion in Chemical Biology 63 (August 2021): 163–70. http://dx.doi.org/10.1016/j.cbpa.2021.04.002.
Steadman, Roger, Christoph Herrmann, Oliver Mülhens, and Dale G. Maeding. "ChromAIX: Fast photon-counting ASIC for Spectral Computed Tomography." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 648 (August 2011): S211—S215. http://dx.doi.org/10.1016/j.nima.2010.11.149.
Bratke, Grischa, Tilman Hickethier, Daniel Bar-Ness, Alexander Christian Bunck, David Maintz, Gregor Pahn, Philippe Coulon, Salim Si-Mohamed, Philippe Douek, and Monica Sigovan. "Spectral Photon-Counting Computed Tomography for Coronary Stent Imaging." Investigative Radiology 55, no. 2 (February 2020): 61–67. http://dx.doi.org/10.1097/rli.0000000000000610.
Tortora, Mario, Laura Gemini, Imma D’Iglio, Lorenzo Ugga, Gaia Spadarella, and Renato Cuocolo. "Spectral Photon-Counting Computed Tomography: A Review on Technical Principles and Clinical Applications." Journal of Imaging 8, no. 4 (April 15, 2022): 112. http://dx.doi.org/10.3390/jimaging8040112.
Yu, Zhicong, Shuai Leng, Zhoubo Li, and Cynthia H. McCollough. "Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography." Physics in Medicine and Biology 61, no. 18 (August 23, 2016): 6707–32. http://dx.doi.org/10.1088/0031-9155/61/18/6707.
Meloni, Antonella, Erica Maffei, Alberto Clemente, Carmelo De Gori, Mariaelena Occhipinti, Vicenzo Positano, Sergio Berti, et al. "Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases." Journal of Clinical Medicine 13, no. 8 (April 18, 2024): 2359. http://dx.doi.org/10.3390/jcm13082359.
Si-Mohamed, Salim, David P. Cormode, Daniel Bar-Ness, Monica Sigovan, Pratap C. Naha, Jean-Baptiste Langlois, Lara Chalabreysse, et al. "Evaluation of spectral photon counting computed tomography K-edge imaging for determination of gold nanoparticle biodistribution in vivo." Nanoscale 9, no. 46 (2017): 18246–57. http://dx.doi.org/10.1039/c7nr01153a.
Si-Mohamed, Salim Aymeric, Jade Miailhes, Pierre-Antoine Rodesch, Sara Boccalini, Hugo Lacombe, Valérie Leitman, Vincent Cottin, Loic Boussel, and Philippe Douek. "Spectral Photon-Counting CT Technology in Chest Imaging." Journal of Clinical Medicine 10, no. 24 (December 9, 2021): 5757. http://dx.doi.org/10.3390/jcm10245757.
Дисертації з теми "Spectral Photon Counting Computed Tomography":
Persson, Mats. "Spectral Computed Tomography with a Photon-Counting Silicon-Strip Detector." Doctoral thesis, KTH, Medicinsk bildfysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187263.
Moro, Viggo. "Deep-learning image reconstruction for photon-counting spectral computed tomography." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297560.
Xu, Cheng. "A Segmented Silicon Strip Detector for Photon-Counting Spectral Computed Tomography." Doctoral thesis, KTH, Medicinsk avbildning, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105614.
QC 20121123
Yveborg, Moa. "Quantification and Maximization of Performance Measures for Photon Counting Spectral Computed Tomography." Doctoral thesis, KTH, Medicinsk bildfysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160899.
QC 20150303
Carramate, Lara Filipa das Neves Dias. "Development of a single photon counting computed tomography system using MPGDs." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/14003.
The development of computed tomography systems with energy resolving detectors is a current challenge in medical physics and biomedical engineering. A computed tomography system of this kind allows getting complementary informations relatively to conventional systems, that can help the medical diagnosis, being of great interest in medicine. The work described in this thesis is related to the development of a computed tomography system using micropattern gaseous detectors, which allow storing, simultaneously, information about the interaction position and the energy of each single photon that interacts with the detector. This kind of detectors has other advantages concerning the cost and characteristics of operation when compared with solid state detectors. Tomographic acquisitions were performed using a MicroHole & Strip Plate based detector, which allowed reconstructing cross-sectional images using energy windows, applying the energy weighting technique and performing multi-slice and tri-dimensional reconstructions. The contrast-to-noise ratio was improved by 31% by applying the energy weighting technique, comparing with the corresponding image obtained with the current medical systems. A prototype of a computed tomography with flexibility to change the detector was developed, making it possible to apply different detectors based on Thick-COBRA. Several images acquired with these detectors are presented and demonstrate their applicability in X-ray imaging. When operating in NeCH4, the detector allowed a charge gain of 8 104, an energy resolution of 20% (full width at half maximum at 8 keV), a count rate of 1 106 Hz/mm2, a very stable operation (gain fluctuations below 5%) and a spacial resolution of 1.2 mm for an energy photon of 3.6 keV. Operating the detector in pure Kr allowed increasing the detection efficiency and achieving a charge gain of 2 104, an energy resolution of 32% (full width at half maximum at 22 keV), a count rate of 1 105 Hz/mm2, very stable operation and a spatial resolution of 500 m. The software already existing in the group was improved and tools to correct geometric misalignments of the system were also developed. The reconstructions obtained after geometrical correction are free of artefacts due to the referred misalignments.
O desenvolvimento de sistemas de tomografia computorizada que incorporem detetores com resolução em energia é um desafio atual em física médica e engenharia biomédica. Um sistema de tomografia computorizada espetral permite obter informações complementares comparativamente a um sistema convencional, que podem auxiliar no diagnóstico médico, sendo por isso de grande interesse em medicina. O trabalho exposto nesta tese prende-se com o desenvolvimento de um sistema de tomografia usando detetores gasosos microestruturados que permitem, simultaneamente, ter informação da posição de interacção e da energia de cada fotão que interage com o detetor. Este tipo de detetores possui ainda outras vantagens relativamente a custo ou características de funcionamento quando comparados com detetores de estado sólido. Foram realizadas aquisições tomográficas usando um detetor baseado numa MicroHole & Srip Plate que permitiu reconstruir imagens utilizando diferentes gamas de energia, aplicar técnicas de ponderação em energia e fazer pela primeira vez reconstrução multi-corte e obter imagens tri-dimensionais. Aplicando a técnica de ponderação em energia foi possível melhorar a relação contraste-ruído em 31% comparativamente à imagem correspondente aquela obtida nos actuais sistemas médicos. Posteriormente, foi desenvolvido um protótipo de um sistema de tomografia computorizada com flexibilidade para alterar o detetor, tornando possível utilizar vários detetores baseados na microestrutura Thick-COBRA. São apresentadas várias imagens adquiridas com estes detetores que evidenciam a sua aplicabilidade em imagiologia por raio-X. A operar no meio gasoso NeCH4 o detetor permitiu um ganho de 8 104, uma resolução em energia de 20% (largura a meia altura a 8 keV), uma taxa de contagem de 1 106 Hz/mm2, um funcionamento muito estável (variações de ganho inferiores a 5%) e uma resolução espacial de 1.2 mm para fotões de 3.6 keV. A operar em Kr puro foi possível aumentar a eficiência de deteção e alcançar um ganho de 2 104, uma resolução em energia de 32% (largura a meia altura a 22 keV), uma taxa de contagem de 1 105 Hz/mm2, um funcionamento também bastante estável e uma resolução espacial de 500 m. O software já existente no grupo para reconstrução de imagem foi melhorado e foram ainda desenvolvidas ferramentas para correcção de desalinhamentos geométricos do sistema. As reconstruções obtidas após correção geométrica surgem livres de artefactos originados pelos referidos desalinhamentos.
Liu, Xuejin. "Characterization and Energy Calibration of a Silicon-Strip Detector for Photon-Counting Spectral Computed Tomography." Doctoral thesis, KTH, Medicinsk bildteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192240.
QC 20160908
Niu, Pei. "Multi-energy image reconstruction in spectral photon-counting CT." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI022.
Spectral photon-counting CT (sCT) appeared recently as a new imaging technique presenting fundamental advantages with respect to conventional CT and duel-energy CT. However, due to the reduced number of photons in each energy bin of sCT and various artifacts, image reconstruction becomes particularly difficult. This thesis focuses on the reconstruction of multi-energy images in sCT. First, we propose to consider the ability of sCT to achieve simultaneously both anatomical (aCT) and functional imaging (fCT) in one single acquisition through reconstruction and material decomposition. aCT function of sCT is studied under the same configuration as that of conventional CT, and fCT function of sCT is investigated by applying material decomposition algorithms to the same acquired multi-energy data. Then, since noise is a particularly acute problem due to the largely reduced number of photons in each energy bin of sCT, we introduce denoising mechanism in the image reconstruction to perform simultaneous reconstruction and denoising. Finally, to improve image reconstruction, we propose to reconstruct the image at a given energy bin by exploiting information in all other energy bins. The key strategy in such approach consists of grouping the similar pixels from the reconstruction of all the energy bins into the same class, fitting within each class, mapping the fitting results into each energy bin, and denoising with the mapped information. It is used both as a post-denoising operation to demonstrate its effectiveness and as a regularization term or a combined regularization term for simultaneous reconstruction and denoising. All the above methods are evaluated on both simulation and real data from a pre-clinical sCT system
Pivot, Odran. "Scatter correction for spectral computed tomography." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI102.
Scattered radiation is a major cause of bias, loss of contrast and artifacts in x-ray computed tomography (CT). Many correction methods have been proposed for conventional CT (using energy-integrating detectors) but it is still an open research topic in the field of spectral CT, a novel imaging technique based on the use of energy-selective photon counting detectors. The main objective of the present thesis was to investigate scatter correction techniques adapted to spectral CT. The chosen solution refines a scatter correction method developed for integration-mode CT which uses a semi-transparent primary modulator mask. The attenuation of the primary modulator mask is first compensated for with a correction matrix which takes advantage of the spectral information. The other contributions are a scatter model based on B-splines allowing an accurate representation of scatter maps with the aid of a very few parameters and a cost function which takes into account the structures of the mask and the object. The accuracy of the correction matrix, the scatter model and the whole proposed scatter correction process were tested on simulated data considering photon counting detectors with various numbers of energy bins and have shown a significant bias reduction, contrast enhancement and artifact removal. In addition, physical experiments were performed using a parallel fan-beam set-up with a commercial energy-resolved detector. The method was successfully validated in the case of two phantoms dedicated to medical image quality measurements, with a remarkable improvement
Xie, Bingqing. "Image-domain material decomposition in spectral photon-counting CT for medical applications." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI021.
Material decomposition is a fundamental and primordial problem in spectral photon-counting X-ray CT (sCT). The present thesis focuses on the development of material decomposition methods using spectral and morphological information embedded in multi-energy sCT images. In this framework, three methods were developed. For the first method, by using bounded mass density, local joint sparsity and structural low-rank (DSR) in image domain, we achieve highly accurate decomposition of materials such as gadolinium, iodine and iron. The results on both numerical phantom and physical data demonstrated that the proposed DSR method leads to more accurate decomposition than usual pseudo-inverse method with singular value decomposition (SVD) and current popular sparse regularization method with L1-norm constraint. The second method works in a region-wise manner. It consists in optimizing basis materials based on spatio-energy segmentation of regions-of-interests (ROIs) in sCT images, reducing noise by averaging multi-energy spatial images, and performing a fine material decomposition involving optimized decomposition matrix, denoising regularization and sparsity regularization. The results on both digital and physical data showed that the proposed ROI-wise material decomposition method presents clearly higher reliability and accuracy compared to common decomposition methods based on total variation (TV) or L1-norm (lasso) regularization. In the third method, we propose the notion of super-energy-resolution (SER) sCT imaging, which is realized through establishing the relationship between simulation and physical phantoms by means of coupled dictionary learning in a pixel-wise way. The effectiveness of the proposed methods was validated on digital phantom, physical phantoms and in vivo data. The results showed that for the same decomposition method using lasso regularization, the proposed super-energy-resolution imaging presents much higher decomposition accuracy and detection ability compared to what can be provided by current sCT machine
Chen, Han. "Characterization and Optimization of Silicon-strip Detectors for Mammography and Computed Tomography." Doctoral thesis, KTH, Medicinsk bildfysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184092.
QC 20160401
Книги з теми "Spectral Photon Counting Computed Tomography":
Taguchi, Katsuyuki, Ira Blevis, and Krzysztof Iniewski. Spectral, Photon Counting Computed Tomography. Edited by Katsuyuki Taguchi, Ira Blevis, and Krzysztof Iniewski. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111.
Hsieh, Scott, and Krzysztof Iniewski, eds. Photon Counting Computed Tomography. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26062-9.
Heismann, Björn J. Spectral CT imaging. Bellingham, Wash: SPIE Press, 2012.
Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral Photon Counting Computed Tomography. Taylor & Francis Group, 2022.
Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.
Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.
Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.
Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.
Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.
Hsieh, Scott, and Krzysztof (Kris) Iniewski. Photon Counting Computed Tomography: Clinical Applications, Image Reconstruction and Material Discrimination. Springer International Publishing AG, 2023.
Частини книг з теми "Spectral Photon Counting Computed Tomography":
Tang, Xiangyang, Yan Ren, Huiqiao Xie, and Arthur E. Stillman. "Spectral Imaging in Photon-Counting CT with Data Acquired in Interleaved/Gapped Spectral Channels." In Photon Counting Computed Tomography, 177–97. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26062-9_9.
Lee, Okkyun, and Katsuyuki Taguchi. "Spectral Distortion Compensation for Spectral CT." In Spectral, Photon Counting Computed Tomography, 373–92. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111-20.
Schmidt, Taly Gilat. "Future Prospects of Spectral CT: Photon Counting." In Computed Tomography, 269–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26957-9_14.
Ding, Huanjun, and Sabee Molloi. "Quantitative Breast Imaging with Low-Dose Spectral Mammography." In Photon Counting Computed Tomography, 113–35. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26062-9_6.
Taguchi, Katsuyuki. "Photon Counting Detector Simulator." In Spectral, Photon Counting Computed Tomography, 345–52. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111-18.
Ding, Huanjun, and Sabee Molloi. "Quantitative Breast Lesion Characterization with Spectral Mammography: A Feasibility Study." In Photon Counting Computed Tomography, 93–111. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26062-9_5.
Rit, Simon, Cyril Mory, and Peter B. Noël. "Image Formation in Spectral Computed Tomography." In Spectral, Photon Counting Computed Tomography, 355–72. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111-19.
Fleiter, Thorsten. "Clinical Applications of Spectral Computed Tomography." In Spectral, Photon Counting Computed Tomography, 163–76. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111-9.
McNabney, Charis, Shamir Rai, and Darra T. Murphy. "Clinical Perspective on Dual Energy Computed Tomography." In Spectral, Photon Counting Computed Tomography, 35–52. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111-3.
Leng, Shuai, Shengzhen Tao, Kishore Rajendran, and Cynthia H. McCollough. "Clinical Applications of Photon-Counting Detector Computed Tomography." In Spectral, Photon Counting Computed Tomography, 75–96. First edition. | Boca Raton : CRC Press, 2020. | Series: Devices, circuits, & systems: CRC Press, 2020. http://dx.doi.org/10.1201/9780429486111-5.
Тези доповідей конференцій з теми "Spectral Photon Counting Computed Tomography":
Rajendran, Kishore, Shengzhen Tao, Amy Benike, Shuai Leng, and Cynthia H. McCollough. "Quantitative cartilage imaging using spectral photon-counting detector based computed tomography." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor Gimi and Andrzej Krol. SPIE, 2019. http://dx.doi.org/10.1117/12.2512627.
Polster, C., K. Hahn, R. Gutjahr, F. Schöck, S. Kappler, O. Dietrich, and T. G. Flohr. "Improving material decomposition by spectral optimization of photon counting computed tomography." In SPIE Medical Imaging, edited by Despina Kontos, Thomas G. Flohr, and Joseph Y. Lo. SPIE, 2016. http://dx.doi.org/10.1117/12.2216711.
Kang, D., D. Lee, M. Cho, K. Park, K. T. Lim, S. Cho, and G. Cho. "High speed photon counting readout ASIC for spectral computed tomography detectors." In 2015 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2015. http://dx.doi.org/10.1109/icce.2015.7066550.
Brun, Francesco, Vittorio Di Trapani, Diego Dreossi, Renata Longo, Pasquale Delogu, and Luigi Rigon. "K-edge spectral computed tomography with a photon counting detector and discrete reconstruction." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513425.
Evans, Connor J., Mengzhou Li, Chuang Niu, Ge Wang, and Ryan K. Roeder. "Effects of image denoising on quantitative material decomposition in photon-counting spectral computed tomography." In Physics of Medical Imaging, edited by Wei Zhao and Lifeng Yu. SPIE, 2022. http://dx.doi.org/10.1117/12.2611828.
Hein, Dennis, Konstantinos Liappis, Alma Eguizabal, and Mats Persson. "Deep learning ring artifact correction in photon-counting spectral CT with perceptual loss." In Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), edited by Joseph Webster Stayman. SPIE, 2022. http://dx.doi.org/10.1117/12.2647089.
Bhattarai, Abhisek, Jamie Lok Guan-Tai, Hongzhe Sun, and Varut Vardhanabhuti. "Photon counting spectral computed tomography in diagnosis of joint conditions using novel bismuth contrast agent." In Physics of Medical Imaging, edited by Rebecca Fahrig, John M. Sabol, and Lifeng Yu. SPIE, 2023. http://dx.doi.org/10.1117/12.2653234.
Liu, Leening P., Nadav Shapira, Pooyan Sahbaee, Harold I. Litt, Marcus Y. Chen, and Peter B. Noël. "Dual-source photon-counting CT: consistency in spectral results at different acquisition modes and heart rates." In Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), edited by Joseph Webster Stayman. SPIE, 2022. http://dx.doi.org/10.1117/12.2646718.
Steadman, Roger, Christoph Herrmann, Oliver Mülhens, Dale G. Maeding, James Colley, Ted Firlit, Randy Luhta, Marc Chappo, Brian Harwood, and Doug Kosty. "ChromAIX: a high-rate energy-resolving photon-counting ASIC for spectal computed tomography." In SPIE Medical Imaging. SPIE, 2010. http://dx.doi.org/10.1117/12.844222.
Li, Danyang, Zheng Duan, Dong Zeng, Zhaoying Bian, and Jianhua Ma. "Full-spectrum-knowledge-aware unsupervised network for photon-counting CT imaging." In Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), edited by Joseph Webster Stayman. SPIE, 2022. http://dx.doi.org/10.1117/12.2646642.