Relevant bibliographies by topics / Spectral Photon Counting Computed Tomography

Academic literature on the topic 'Spectral Photon Counting Computed Tomography'

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Published: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Spectral Photon Counting Computed Tomography":

1

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.

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A key process in the development of atherosclerotic plaques is the recruitment of monocytes into the artery wall. Using spectral photon-counting computed tomography we examine whether monocyte deposition within the artery wall of ApoE-/- mouse can be detected. Primary mouse monocytes were labelled by incubating them with 15 nm gold nanoparticles coated with 11-mercaptoundecanoic acid The monocyte uptake of the particle was confirmed by electron microscopy of the cells before injection into 6-week-old apolipoprotein E deficient (ApoE-/-) mouse that had been fed with the Western diet for 10 weeks. Four days following injection, the mouse was sacrificed and imaged using a MARS spectral photon counting computed tomography scanner with a spectral range of 7 to 120 KeV with five energy bins. Imaging analysis showed the presence of X-ray dense material within the mouse aortic arch which was consistent with the spectral characteristic of gold rather than calcium. The imaging is interpreted as showing the deposition of gold nanoparticles containing monocytes within the mouse aorta. The results of our study determined that spectral photon-counting computed tomography could provide quantitative information about gold nanoparticles labelled monocytes in voxels of 90 × 90 × 90 µm3. The imaging was consistent with previous micro-CT and electron microscopy of mice using the same nanoparticles. This study demonstrates that spectral photon-counting computed tomography, using a MARS small bore scanner, can detect a fundamental atherogenic process within mouse models of atherogenesis. The present study demonstrates the feasibility of spectral photon-counting computed tomography as an emerging molecular imaging modality to detect atherosclerotic disease.
2

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.

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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.

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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.

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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.

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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.

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Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.
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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.

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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.

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Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements’ composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.
9

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.

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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.

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The X-ray imaging field is currently undergoing a period of rapid technological innovation in diagnostic imaging equipment. An important recent development is the advent of new X-ray detectors, i.e., photon-counting detectors (PCD), which have been introduced in recent clinical prototype systems, called PCD computed tomography (PCD-CT) or photon-counting CT (PCCT) or spectral photon-counting CT (SPCCT) systems. PCD allows a pixel up to 200 microns pixels at iso-center, which is much smaller than that can be obtained with conventional energy integrating detectors (EID). PCDs have also a higher dose efficiency than EID mainly because of electronic noise suppression. In addition, the energy-resolving capabilities of these detectors allow generating spectral basis imaging, such as the mono-energetic images or the water/iodine material images as well as the K-edge imaging of a contrast agent based on atoms of high atomic number. In recent years, studies have therefore been conducted to determine the potential of PCD-CT as an alternative to conventional CT for chest imaging.
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Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Spectral Photon Counting Computed Tomography":

1

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.

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Computed tomography (CT) is a widely used medical imaging modality. By rotating an x-ray tube and an x-ray detector around the patient, a CT scanner is able to measure the x-ray transmission from all directions and form an image of the patient’s interior. CT scanners in clinical use today all use energy-integrating detectors, which measure the total incident energy for each measurement interval. A photon-counting detector, on the other hand, counts the number of incoming photons and can in addition measure the energy of each photon by comparing it to a number of energy thresholds. Using photon- counting detectors in computed tomography could lead to improved signal-to-noise ratio, higher spatial resolution and improved spectral imaging which allows better visualization of contrast agents and more reliable quantitative measurements. In this Thesis, the feasibility of using a photon-counting silicon-strip detector for CT is investigated. In the first part of the Thesis, the necessary performance requirements on such a detector is investigated in two different areas: the detector element homogeneity and the capability of handling high photon fluence rates. A metric of inhomogeneity is proposed and used in a simulation study to evaluate different inhomogeneity compensation methods. Also, the photon fluence rate incident on the detector in a scanner in clinical use today is investigated for different patient sizes through dose rate measurements together with simulations of transmission through patient im- ages. In the second part, a prototype detector module is used to demonstrate new applications enabled by the energy resolution of the detector. The ability to generate material-specific images of contrast agents with iodine and gadolinium is demonstrated. Furthermore, it is shown theoretically and ex- perimentally that interfaces in the image can be visualized by imaging the so-called nonlinear partial volume effect. The results suggest that the studied silicon-strip detector is a promising candidate for photon-counting CT.
2

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.

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X-ray computed tomography (CT) has since its introduction in the early 1970s become one of the most important tools used for medical imaging. In CT, a large number of x-ray attenuation measurements are combined and reconstructed to form a three-dimensional image of the targeted area. In the recent years, a new type of detector called photon counting detector (PCD) has attracted considerable interest. This new type of detector acquires spectral information is associated with several benefits and has shown to be very valuable.  Furthermore, the use of deep learning to reconstruct images produced by CT has attracted significant attention in the last couple of years. However, the best way of incorporating deep learning into the reconstruction chain into the reconstruction chain is still incompletely understood. Additionally, the use of deep learning has mainly been investigated for the case of conventional CT and not for CT performed with PCDs. It these two points that this work aims to address.  Multiple deep learning architectures were implemented and evaluated on material images acquired by simulating a PCD. The deep-learning part of the reconstruction took the form of image-domain denoising after the material images had been obtained from the material sinograms through filtered back projection. Then, a comparison between the different deep learning architectures was made to find out which architecture is the most suited for denoising images produced by PCDs in the image domain.
3

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.

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Spectral computed tomography with energy-resolving detectors has a potential to improve the detectability of images and correspondingly reduce the radiation dose to patients by extracting and properly using the energy information in the broad x-ray spectrum. A silicon photon-counting detector has been developed for spectral CT and it has successfully solved the problem of high photon flux in clinical CT applications by adopting the segmented detector structure and operating the detector in edge-on geometry. The detector was evaluated by both the simulation and measurements. The effects of energy loss and charge sharing on the energy response of this segmented silicon strip detector with different pixel sizes were investigated by Monte Carlo simulation and a comparison to pixelated CdTe detectors is presented. The validity of spherical approximations of initial charge cloud shape in silicon detectors was evaluated and a more accurate statistical model has been proposed. A photon-counting energy-resolving application specific integrated circuit (ASIC) developed for spectral CT was characterized extensively by electrical pulses, pulsed laser and real x-ray photons from both the synchrotron and an x-ray tube. It has been demonstrated that the ASIC performs as designed. A noise level of 1.09 keV RMS has been measured and a threshold dispersion of 0.89 keV RMS has been determined. The count rate performance of the ASIC in terms of count loss and energy resolution was evaluated by real x-rays and promising results have been obtained. The segmented silicon strip detector was evaluated using synchrotron radiation. An energy resolution of 16.1% has been determined with 22 keV photons in the lowest flux limit, which deteriorates to 21.5% at an input count rate of 100 Mcps mm−2. The fraction of charge shared events has been estimated and found to be 11.1% for 22 keV and 15.3% for 30 keV. A lower fraction of charge shared events and an improved energy resolution can be expected by applying a higher bias voltage to the detector.

QC 20121123

4

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.

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During my time as a PhD student at the Physics of Medical Imaging group at KTH, I have taken part in the work of developing a photon counting spectrally resolved silicon detector for clinical computed tomography. This work has largely motivated the direction of my research, and is the main reason for my focus on certain issues. Early in the work, a need to quantify and optimize the performance of a spectrally resolved detector was identified. A large part of my work have thus consisted of reviewing conventional methods used for performance quantification and optimization in computed tomography, and identifying which are best suited for the characterization of a spectrally resolved system. In addition, my work has included comparisons of conventional systems with the detector we are developing. The collected result after a little more than four years of work are four publications and three conference papers. This compilation thesis consists of five introductory chapters and my four publications. The introductory chapters are not self-contained in the sense that the theory and results from all my published work are included. Rather, they are written with the purpose of being a context in which the papers should be read. The first two chapters treat the general purpose of the introductory chapters, and the theory of computed tomography including the distinction between conventional, non-spectral, computed tomography, and different practical implementations of spectral computed tomography. The second chapter consists of a review of the conventional methods developed for quantification and optimization of image quality in terms of detectability and signal-to-noise ratio, part of which are included in my published work. In addition, the theory on which the method of material basis decomposition is based on is presented, together with a condensed version of the results from my work on the comparison of two systems with fundamentally different practical solutions for material quantification. In the fourth chapter, previously unpublished measurements on the photon counting spectrally resolved detector we are developing are presented, and compared to Monte Carlo simulations. In the fifth and final chapter, a summary of the appended publications is included.

QC 20150303

5

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.

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Desenvolvimento de um sistema de tomografia computorizada de contagem de fotão único usando MPGDs
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.
6

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.

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Multibin photon-counting x-ray detectors are promising candidates to be applied in next generation computed tomography (CT), whereby energy information from a broad x-ray spectrum can be extracted and properly used for improving image quality and correspondingly reducing radiation dose. A silicon-strip detector has been developed for spectral CT, which operates in photon-counting mode and allows pulse-height discrimination with 8 adjustable energy bins. Critical characteristics, energy resolution and count-rate performance, of the detector are evaluated. An absolute energy resolution (E) from 1.5 keV to 1.9 keV with increasing x-ray energy from 40 keV to 120 keV is found. Pulse pileup degrades the energy resolution by 0.4 keV when increasing the input count rate from zero to 100 Mcps mm−2, while charge sharing shows negligible effect. A near linear relationship between the input and output count rates is observed up to 90 Mcps mm−2 in a clinical CT environment. In addition, no saturation effect appears for the maximally achieved photon flux of 485 Mphotons s−1 mm−2 with a count rate loss of 30%. The detector is energy calibrated in terms of gain and offset with the aid of monoenergetic x rays. The gain variation among channels is below 4%, whereas the variation of offsets is on the order of 1 keV. In order to do the energy calibration in a routinely available way, a method that makes use of the broad x-ray spectrum instead of using monoenergetic x rays is proposed. It is based on a regression analysis that adjusts a modelled spectrum of deposited energies to a measured pulse-height spectrum. Application of this method shows high potential to be applied in an existing CT scanner with an uncertainty of a calibrated threshold between 0.1 and 0.2 keV. The energy-calibration method is further used in the development of a spectral response model of the detector. This model is used to accurately bin-wise predict the response of each detector channel, which is validated by two application examples. First, the model is used in combination with the inhomogeneity compensation method to eliminate ring artefacts in CT images. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. Additionally, the contrast agent concentrations are reconstructed with more than 94% accuracy.

QC 20160908

7

Niu, Pei. "Multi-energy image reconstruction in spectral photon-counting CT." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI022.

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Le scanner CT spectral à comptage de photons (sCT) est apparu récemment comme une nouvelle technique d'imagerie présentant des avantages fondamentaux par rapport au scanner CT classique et au scanner CT à double énergie. Cependant, en raison du nombre réduit de photons dans chaque bande d'énergie du scanner sCT et des artéfacts divers, la reconstruction des images devient particulièrement difficile. Cette thèse se concentre sur la reconstruction d'images multi-énergie en sCT. Tout d'abord, nous proposons d'étudier la capacité du scanner sCT à réaliser simultanément une imagerie anatomique (aCT) et fonctionnelle (fCT) en une seule acquisition par reconstruction et décomposition des matériaux. La fonction aCT du scanner sCT est étudiée dans la même configuration que celle du scanner CT classique, et la fonction fCT du scanner sCT est étudiée en appliquant des algorithmes de décomposition de matériaux aux mêmes données multi-énergie. Ensuite, comme le bruit est un problème particulièrement aigu en raison du nombre largement réduit de photons dans chaque bande d'énergie du scanner sCT, nous introduisons un mécanisme de débruitage dans la reconstruction de l'image pour effectuer simultanément un débruitage et une reconstruction. Enfin, pour améliorer la reconstruction de l'image, nous proposons de reconstruire l'image à une bande d'énergie donnée en exploitant les informations dans toutes les autres bandes d'énergie. La stratégie clé de cette approche consiste à regrouper les pixels similaires issus de la reconstruction de toutes les bandes d'énergie en une seule classe, à les ajuster dans la même classe, à projeter les résultats de l'ajustement dans chaque bande d'énergie, et à débruiter les informations projetées. Elle est utilisée à la fois comme une opération post-débruitage pour démontrer son efficacité et comme un terme de régularisation ou un terme de régularisations combinées pour la réalisation simultanée du débruitage et de la reconstruction. Toutes les méthodes ci-dessus sont évaluées sur des données de simulation et des données réelles provenant d'un scanner sCT préclinique
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
8

Pivot, Odran. "Scatter correction for spectral computed tomography." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI102.

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Le rayonnement diffusé est une cause majeure de biais, de baisse de contraste et d'artéfacts en tomographie par rayons x. De nombreuses méthode de correction ont été proposées pour la tomographie conventionnelle (utilisant des détecteurs à intégration en énergie) mais le sujet reste ouvert dans le cadre de l'imagerie spectrale, une nouvelle modalité d'imagerie basée sur l'utilisation de détecteurs à comptage des photons résolus en énergie. L'objectif principal de ce travail de thèse a été l'étude de techniques de correction du diffusé adaptées à l'imagerie spectrale. La solution choisie améliore une méthode de correction développée pour la tomographie conventionnelle qui utilise un masque modulateur de primaire semi-transparent. L'atténuation du masque est d'abord compensée avec une matrice de correction qui bénéficie de l'information spectrale. Les autres contributions sont un modèle de diffusé basé sur des B-splines et qui permet de représenter précisément les images de diffusé à l'aide d'un très faible nombre de paramètres, et une fonction de coût qui prend en compte les structures du masque et de l'objet. Les performances de la matrice de correction, du modèle de diffusé, et de l'ensemble de la méthode de correction proposée ont été évaluées sur des données simulées, en considérant des détecteurs à comptage de photons avec différents nombres de canaux d'énergie. De plus, des acquisitions ont été réalisées sur un système tomographique à faisCEAux en éventail parallèles comportant un détecteur commercial résolu en énergie. La méthode a été validée avec succès dans les cas de deux fantômes dédiés à des mesures de qualité d'images médicales, avec une amélioration remarquable
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
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Xie, Bingqing. "Image-domain material decomposition in spectral photon-counting CT for medical applications." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI021.

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La décomposition de matériaux est un problème fondamental et primordial dans la tomographie spectrale (sCT—spectral computed tomography) par rayons X basée sur des détecteurs à comptage de photons (PCD—photon counting detector). La présente thèse porte sur le développement de méthodes de décomposition de matériaux en utilisant des informations spectrale et morphologique encodées dans des images sCT multi-énergie. Dans ce cadre, trois méthodes ont été développées. Pour la première méthode, en utilisant la densité de masse limitée, la parcimonie conjointe locale, et le faible rang structurel (DSR) dans le domaine de l'image, nous obtenons une décomposition très précise de matériaux tels que le gadolinium, l'iode et le fer. Les résultats sur les données numériques et physiques du fantôme ont démontré que la méthode DSR proposée conduit à une décomposition plus précise que la méthode pseudo-inverse habituelle avec décomposition en valeur singulière (SVD—singular value decomposition) et la méthode de régularisation parcimonieuse courante avec contrainte de norme L1 (lasso). La deuxième méthode opère par région. Elle consiste à optimiser les matériaux de base en se basant sur la segmentation spatio-énergétique des régions d'intérêt (ROI—regions-of-interests) dans les images sCT, à réduire le bruit en faisant le moyennage des images spatiales multi-énergie, et à effectuer une décomposition fine des matériaux impliquant une matrice de décomposition optimisée, une régularisation du débruitage et une régularisation parcimonieuse. Les résultats sur des données numériques et physiques ont montré que la méthode proposée de décomposition des matériaux ROI par ROI (ROI-wise—region-of-interests-wise) présente une fiabilité et une précision nettement supérieures à celles des méthodes de décomposition courantes fondées sur la régularisation de la variation totale (TV) ou de la norme L1. Dans la troisième méthode, nous proposons la notion d'imagerie sCT à super-résolution énergétique (SER—super-energy-resolution), qui est réalisée en établissant la relation entre la simulation et les fantômes physiques au moyen d'un apprentissage par dictionnaire couplé, de manière pixel par pixel. L'efficacité de ces méthodes proposées a été validée sur des données de fantômes numériques, de fantômes physiques et in vivo. Les résultats montrent que, pour la même méthode de décomposition de matériaux utilisant la régularisation par lasso, l'imagerie à super-résolution énergétique proposée présente une précision de décomposition et un pouvoir de détection beaucoup plus élevé que ce que peut fournir la machine sCT actuelle
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
10

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.

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The goal in medical x-ray imaging is to obtain the image quality requiredfor a given detection task, while ensuring that the patient dose is kept as lowas reasonably achievable. The two most common strategies for dose reductionare: optimizing incident x-ray beams and utilizing energy informationof transmitted beams with new detector techniques (spectral imaging). Inthis thesis, dose optimization schemes were investigated in two x-ray imagingsystems: digital mammography and computed tomography (CT). In digital mammography, the usefulness of anti-scatter grids was investigatedas a function of breast thickness with varying geometries and experimentalconditions. The general conclusion is that keeping the grid is optimalfor breasts thicker than 5 cm, whereas the dose can be reduced without a gridfor thinner breasts. A photon-counting silicon-strip detector developed for spectral mammographywas characterized using synchrotron radiation. Energy resolution, ΔE/Ein, was measured to vary between 0.11-0.23 in the energy range 15-40 keV, which is better than the energy resolution of 0.12-0.35 measured inthe state-of-the-art photon-counting mammography system. Pulse pileup hasshown little effect on energy resolution. In CT, the performance of a segmented silicon-strip detector developedfor spectral CT was evaluated and a theoretical comparison was made withthe state-of-the-art CT detector for some clinically relevant imaging tasks.The results indicate that the proposed photon-counting silicon CT detector issuperior to the state-of-the-art CT detector, especially for high-contrast andhigh-resolution imaging tasks. The beam quality was optimized for the proposed photon-counting spectralCT detector in two head imaging cases: non-enhanced imaging and Kedgeimaging. For non-enhanced imaging, a 120-kVp spectrum filtered by 2half value layer (HVL) copper (Z = 29) provides the best performance. Wheniodine is used in K-edge imaging, the optimal filter is 2 HVL iodine (Z = 53)and the optimal kVps are 60-75 kVp. In the case of gadolinium imaging, theradiation dose can be minimized at 120 kVp filtered by 2 HVL thulium (Z =69).

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Books on the topic "Spectral Photon Counting Computed Tomography":

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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.

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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.

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Heismann, Björn J. Spectral CT imaging. Bellingham, Wash: SPIE Press, 2012.

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Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral Photon Counting Computed Tomography. Taylor & Francis Group, 2022.

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Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.

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Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.

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Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.

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Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.

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Iniewski, Krzysztof, Katsuyuki Taguchi, and Ira Blevis. Spectral, Photon Counting Computed Tomography: Technology and Applications. Taylor & Francis Group, 2020.

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Hsieh, Scott, and Krzysztof (Kris) Iniewski. Photon Counting Computed Tomography: Clinical Applications, Image Reconstruction and Material Discrimination. Springer International Publishing AG, 2023.

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Book chapters on the topic "Spectral Photon Counting Computed Tomography":

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Conference papers on the topic "Spectral Photon Counting Computed Tomography":

1

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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