Дисертації з теми "Spectral Photon Counting Computed Tomography"
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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
di, trapani vittorio. "State-of-the-art setups for K-edge imaging." Doctoral thesis, Università di Siena, 2021. http://hdl.handle.net/11365/1144523.
Dong, Xu. "Material-Specific Computed Tomography for Molecular X-Imaging in Biomedical Research." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88869.
Doctor of Philosophy
X-ray Computed Tomography (CT) has played a central role in clinical imaging since it was invented in 1972. It has distinguishing characteristics of being able to generate three dimensional images with comprehensive inner structural information in fast speed (less than one second). However, traditional CT imaging lacks of material-specific capability due to the mechanism of image formation, which makes it cannot be used for molecular imaging. Molecular imaging plays a central role in present and future biomedical research and clinical diagnosis and treatment. For example, imaging of biological processes and molecular markers can provide unprecedented rich information, which has huge potentials for individualized therapies, novel drug design, earlier diagnosis, and personalized medicine. Therefore there exists a pressing need to enable the traditional CT imaging technique with material-specific capability for molecular imaging purpose. This dissertation conducted comprehensive study to separately investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging utilizes fluorescence signal to achieve molecular imaging in CT; Material identification can be achieved based on the rich image data from PCCT; The deep learning based correction method is an efficient approach for PCCT data distortion correction, and furthermore can boost its performance on material identification. With those techniques, the material-specific capability of CT can be greatly enhanced and the molecular imaging can be approached in biological bodies.
Raviol, Jolan. "Vers l'évaluation du risque de rupture des anévrismes intracrâniens : caractérisation mécanique in vivo de la paroi artérielle." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0011.
Intracranial aneurysms are a critical public health condition linked to the degradation of the cerebral artery wall. There is currently no method for estimating the risk of aneurysm rupture that takes into account the in vivo mechanical properties of the aneurysm wall, which are believed to be essential in the rupture phenomenon. This doctoral work is part of a large-scale project aimed at improving the intervention criteria currently available to practitioners by developing a non-invasive decision-support tool based on the mechanical state of the tissue to assess the probability of rupture. This tool will be based on the definition of a relationship between the shape of the aneurysm observed by clinical imaging and a database containing a set of clinical images from previous studies, associated with the in vivo mechanical properties of the wall and a characterisation of the rupture. To produce this database, an aneurysm wall deformation device was developed as part of the overall project. This doctoral work focuses on (1) the calibration, the optimisation and in vitro testing of this device on phantom arteries and (2) the in vivo application of the device on an animal model of intracranial aneurysm. To do this, a numerical model of the in vitro experiment was implemented and validated against the experimental results by developing an original validation method. This finite element model of fluid-structure interaction was used to understand the uncertainties involved in using the device within the aneurysm and to help for dimensioning the phantom arteries. The best compromise in terms of phantom artery wall thickness and flexibility was identified, taking into account the limitations of the fabrication techniques. In addition, an inverse analysis procedure was developed to estimate the mechanical characteristics of the aneurysm wall in vivo. Its use is based on quantifying the deformation generated by the device and visualised by spectral photon-counting computed tomography, an emerging medical imaging technique whose spatio-temporal resolutions allow controlled stressing of the tissue without increasing the risk of rupture. The mechanical properties identified were consistent with those derived from ex vivo characterisations of similar aneurysms available in the literature. Finally, a first patient-specific criterion for rupture of the aneurysm wall, taking into account the state of stress in vivo in the tissue, was proposed
Loberg, Johannes, and Miranda Gisudden. "Estimation of Noise and Contrast for CTA of the Brain." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239916.
Su, Ting. "Quantitative material decomposition methods for X-ray spectral CT." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI056/document.
X-ray computed tomography (X-ray CT) plays an important part in non-invasive imaging since its introduction. During the past few years, numerous technological advances in X-ray CT have been observed, including spectral CT, which uses photon counting detectors (PCDs) to discriminate transmitted photons corresponding to selected energy bins in order to obtain spectral information with one single acquisition. Spectral CT enables us to overcome many limitations of the conventional CT techniques and opens up many new application possibilities, among which quantitative material decomposition is the hottest topic. A number of material decomposition methods have been reported and different experimental systems are under development for spectral CT. According to the type of data on which the decomposition step operates, we have projection domain method (decomposition before reconstruction) and image domain method (decomposition after reconstruction). The commonly used decomposition is based on least square criterion, named proj-LS and ima-LS method. However, the inverse problem of material decomposition is usually ill-posed and the X-ray spectral CT measurements suffer from Poisson photon counting noise. The standard LS criterion can lead to overfitting to the noisy measurement data. In the present work, we have proposed a least log-squares criterion for projection domain method to minimize the errors on linear attenuation coefficient: proj-LLS method. Furthermore, to reduce the effect of noise and enforce smoothness, we have proposed to add a patchwise regularization term to penalize the sum of the square variations within each patch for both projection domain and image domain decomposition, named proj-PR-LLS and ima-PR-LS method. The performances of the different methods were evaluated by spectral CT simulation studies with specific phantoms for different applications: (1) Medical application: iodine and calcium identification. The decomposition results of the proposed methods show that calcium and iodine can be well separated and quantified from soft tissues. (2) Industrial application: ABS-flame retardants (FR) plastic sorting. Results show that 3 kinds of ABS materials with different flame retardants can be separated when the sample thickness is favorable. Meanwhile, we simulated spectral CT imaging with a PMMA phantom filled with Fe, Ca and K solutions. Different acquisition parameters, i.e. exposure factor and number of energy bins were simulated to investigate their influence on the performance of the proposed methods for iron determination
Dupont, Mathieu. "Tomographie spectrale à comptage de photons : développement du prototype PIXSCAN et preuve de concept." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4011/document.
In the field of preclinical X-ray tomography, spectral tomography is actively explorated. The aims of spectral tomography are the caracterisation of tissues and contrast agentstogether with the quantification of the latter and the enhancement of contrast between soft tissues. This is achived by the exploitation of spectral information (i.e. energy) and not only the detected quantities of photons X. The interest in spectral tomography is enforced by the arrival of hybrid pixel cameras like XPAD, because of their ability to select photons according to their energy. The XPAD3 camera, third version of XPAD, is built to be used in the micro-CT demonstrator PIXCAN fully developped at CPPM.In this context, this thesis has two goals : a contribution to the developement of the PIXSCAN and a realisation of a proof of concept of spectral tomography in PIXSCAN. The first goal is done by developing the data acquisition system of PIXSCAN. To accomplish the second one, we perform spectral tomography by implementing component separation in order to isolate photoelectric, compton and contrast agents contribution. This work begins by the caracterisation of this method and ends by a proof of concept on real data acquired by PIXSCAN
Bergström, Eva, and Ida Johansson. "Improved Spatial Resolution in Segmented Silicon Strip Detectors." Thesis, KTH, Medicinteknik och hälsosystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257953.
Halvledardetektorer är av stigande intresse inom forskning för användning i fotonräknande datortomografi med spektral upplösning. För att erhålla en hög spatiell upplösning är det av intresse att hitta fotonens ursprungliga interaktionsposition. I detta arbete undersöks om maskininlärning kan användas för att erhålla en spatiell upplösning på subpixelnivå i en fotonräknande kiselstrippdetektor med 10 µm pixlar. Laddningsfördelningen från simulerade interaktioner i en, tre, och sju positioner inom var och en av tre pixlar undersöktes med hjälp av applikationen Classification Learner i MATLAB® för att bestämma den korrekta interaktionspositionen. Olika maskininlärningsmodeller tränades och testades för att maximera prestandan. När pulser från en och sju positioner inom pixeln användes, kunde modellen hitta den korrekta pixeln med en noggrannhet på 100% respektive 88.9%. Vidare kunde den korrekta positionen inom en pixel bestämmas med en noggrannhet på 54.0% och 29.4% när tre respektive sju positioner inom varje pixel användes. Resultaten visar att det skulle vara möjligt att förbättra den spatiella upplösningen med hjälp av maskininlärning.
Simard, Mikaël. "Étude de la tomodensitométrie spectrale quantitative et ses applications en radiothérapie." Thesis, 2021. http://hdl.handle.net/1866/25252.
X-ray computed tomography (CT) is an imaging modality that produces a tridimensional map of the attenuation of X-rays by the scanned object. In radiation therapy, CT provides anatomical and quantitative information on the patient that is required for treatment planning. However, CT has some issues, notably (1) a limited accuracy in the estimation of quantitative physical parameters of the patient, and (2) a sensitivity to biases caused by beam hardening artifacts. Finally, (3) in the case where contrast-enhanced CT is performed to help treatment planning, a second scan with no contrast agent is required for dose calculation purposes, which increases the overall dose to the patient. Those 3 problems limit the efficiency of CT for some treatment modalities more sensitive to uncertainties, such as proton therapy. Spectral CT regroups a set of methods that allows the production of multiple X-ray attenuation maps evaluated over various energy windows. The additional energy-weighted information that is obtained allows better material characterization. The potential of one spectral CT modality, dual-energy CT (DECT), is already well demonstrated for radiation therapy, while an upcoming method, spectral photon counting CT (SPCCT), promises more spectral information with the help of energy discriminating detectors. Unfortunately, SPCCT suffers from increased noise and poor conditioning. This thesis thus investigates the following question: is there a benefit to using more, but lower quality energy-resolved information for radiotherapy? The question is studied in the context of the three problems discussed earlier. First, a maximum a posteriori (MAP) estimator is introduced for post-reconstruction tissue characterization for denoising purposes in spectral CT. The estimator is validated experimentally using a commercial DECT. The noise level on the proton stopping power is reduced, on average, by a factor of 3.2 with the MAP estimator. The estimator also generally con- serves the quantitative accuracy of estimated physical parameters. For instance, the stopping power varies on average by 0.9% with respect to the conventional approach. Then, the MAP estimation framework is adapted to the context of contrast-enhanced imaging. Numerical results show clear benefits when using SPCCT for virtual non-contrast imaging compared to DECT, with a reduction of the RMS error on the proton stopping power from 2.7 to 1.4%. Third, the developed tools are validated experimentally on a micro-SPCCT from MARS Bioimaging, which uses the Medipix 3 chip as a photon counting detector. Small benefits in the accuracy of physical parameters of tissue substitutes materials are obtained. Finally, a new parametrization of the attenuation coefficient for pre-reconstruction imaging is pro- posed, whose ultimate aim is to correct beam hardening artifacts. In a simulation study, the proposed parametrization eliminates all biases in the estimated physical parameters of human tissues, which is an improvement upon existing parametrizations. However, no ad- vantage has been obtained with SPCCT compared to DECT, which suggests the need to incorporate MAP estimation in the pre-reconstruction framework using an iterative reconstruction approach.
Dunning, Chelsea Amanda Saffron. "Contrast agent imaging using an optimized table-top x-ray fluorescence and photon-counting computed tomography imaging system." Thesis, 2020. http://hdl.handle.net/1828/12308.
Graduate
Lalonde, Arthur. "Étude Monte Carlo de l’impact de la tomodensitométrie multiénergie sur la précision du calcul de dose en protonthérapie." Thèse, 2019. http://hdl.handle.net/1866/22673.