Relevant bibliographies by topics / CT quantification

Contents

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

Academic literature on the topic 'CT quantification'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "CT quantification"

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Ferrando, Ornella, Alessandro Chimenz, Franca Foppiano, and Andrea Ciarmiello. "SPECT/CT activity quantification in 99mTc-MAA acquisitions." Journal of Diagnostic Imaging in Therapy 5, no. 1 (June 24, 2018): 32–36. http://dx.doi.org/10.17229/jdit.2018-0624-034.

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Ferrando, Ornella, Franca Foppiano, Tindaro Scolaro, Chiara Gaeta, and Andrea Ciarmiello. "PET/CT images quantification for diagnostics and radiotherapy applications." Journal of Diagnostic Imaging in Therapy 2, no. 1 (February 16, 2015): 18–29. http://dx.doi.org/10.17229/jdit.2015-0216-013.

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Do, Synho, Kristen Salvaggio, Supriya Gupta, Mannudeep Kalra, Nabeel U. Ali, and Homer Pien. "Automated Quantification of Pneumothorax in CT." Computational and Mathematical Methods in Medicine 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/736320.

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An automated, computer-aided diagnosis (CAD) algorithm for the quantification of pneumothoraces from Multidetector Computed Tomography (MDCT) images has been developed. Algorithm performance was evaluated through comparison to manual segmentation by expert radiologists. A combination of two-dimensional and three-dimensional processing techniques was incorporated to reduce required processing time by two-thirds (as compared to similar techniques). Volumetric measurements on relative pneumothorax size were obtained and the overall performance of the automated method shows an average error of just below 1%.
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Morsbach, Fabian, Lotus Desbiolles, André Plass, Sebastian Leschka, Bernhard Schmidt, Volkmar Falk, Hatem Alkadhi, and Paul Stolzmann. "Stenosis Quantification in Coronary CT Angiography." Investigative Radiology 48, no. 1 (January 2013): 32–40. http://dx.doi.org/10.1097/rli.0b013e318274cf82.

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Erlandsson, K., D. Visvikis, W. A. Waddington, I. D. Cullum, and G. Davies. "39. Absolute quantification with hybrid PET/CT and SPET/CT systems." Nuclear Medicine Communications 24, no. 4 (April 2003): 456–57. http://dx.doi.org/10.1097/00006231-200304000-00058.

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Bovenschulte, H., B. Krug, T. Schneider, H. Schwabe, C. Kabbasch, C. Bangard, M. Hellmich, G. Michels, D. Maintz, and K. Lackner. "CT coronary angiography: Coronary CT-flow quantification supplements morphological stenosis analysis." European Journal of Radiology 82, no. 4 (April 2013): 608–16. http://dx.doi.org/10.1016/j.ejrad.2012.08.004.

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Im, Won Hyeong, Gong Yong Jin, Young Min Han, and Eun Young Kim. "CT Quantification of Central Airway in Tracheobronchomalacia." Journal of the Korean Society of Radiology 74, no. 5 (2016): 299. http://dx.doi.org/10.3348/jksr.2016.74.5.299.

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Williams, Michelle C., and David E. Newby. "CT myocardial perfusion: a step towards quantification." Heart 98, no. 7 (March 15, 2012): 521–22. http://dx.doi.org/10.1136/heartjnl-2012-301677.

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Mawlawi, Osama, S. Kappadath, Tinsu Pan, Eric Rohren, and Homer Macapinlac. "Factors Affecting Quantification in PET/CT Imaging." Current Medical Imaging Reviews 4, no. 1 (February 1, 2008): 34–45. http://dx.doi.org/10.2174/157340508783502778.

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Antunovic, Lidija, Marcello Rodari, Pietro Rossi, and Arturo Chiti. "Standardization and Quantification in PET/CT Imaging." PET Clinics 9, no. 3 (July 2014): 259–66. http://dx.doi.org/10.1016/j.cpet.2014.03.002.

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Dissertations / Theses on the topic "CT quantification"

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Robertson, Galen Charles. "Quantification of Skeletal Phenotype Using Micro-CT and Mechanical Testing." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4874.

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With the vast array of genetically altered (knockout) mice becoming available there is a need for quantitative, repeatable, and efficient methodologies to characterize the phenotypic consequences of knocking out specific genes. Since knockout animals often have the ability to compensate for a single missing gene, it is important to examine the structural, material and morphological properties to obtain a thorough understanding of the changes occurring. For this project, femurs of knockout mice were first scanned using microcomputed tomography (micro-CT) to obtain high-resolution images of the trabecular bone in the distal femur, as well as cortical bone in the mid-diaphysis. After scanning, the femurs were tested to destruction in four-point bending at the mid-diaphysis about the medial lateral axis of the femur. These methodologies allowed quantification of (1) morphologic properties such as bone volume fraction, trabecular properties and 2nd moment of the area (2) structural properties such as stiffness, maximum load at failure, and post yield deformation and (3) material properties such as bone mineral density, elastic modulus and yield strength. As part of two independent studies, two different knockout mice, cyclooxygenase-2 (COX-2 -/-) and Apolipoprotein E (APOE -/-), were examined for structure-function relationships using these methodologies. COX-2 knockout mice were found to have decreased mineral content in their femurs, and increased post yield deformation. APOE knockout mice at 10 weeks of age had decreased bone mass and structural properties. However, by 40 weeks of age APOE deficient mice caught up to and exceeded the structural properties and bone mass of their wild type counterparts.
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Chakraborty, Chandrani. "Quantification of respiratory motion in PET/CT and its significance in radiation therapy." Oklahoma City : [s.n.], 2008.

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Uribe, Muñoz Carlos Felipe. "SPECT/CT quantification of ¹⁷⁷Lu for dosimetry in radionuclide therapy treatments of neuroendocrine tumors." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/56822.

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Peptide receptor radionuclide therapy (PRRT) shows promising results in the treatment of neuroendocrine tumors. These tumors over-express somatostatin receptors that allow us to label somatostatin analogues with ¹⁷⁷Lu to deliver dose to the tumor. However, currently every patient receives the same amount of radioactivity of approximately 7400 MBq per treatment cycle. With this “one dose fits all” approach, differences between patients are not taken into account resulting in some being under-treated while others over-treated. The aim of this thesis was to develop a simple protocol for ¹⁷⁷Lu activity quantification for patients undergoing PRRT with the purpose of performing personalized dose assessments. Physics phenomena that influence image quantification were investigated. As electrons emitted in the decay of ¹⁷⁷Lu result in creation of Bremsstrahlung, Monte-Carlo simulations were performed to investigate this effect on image quantification of ¹⁷⁷Lu. Phantom experiments with different attenuation and scatter conditions were performed to test quantification accuracy and evaluate performance of several segmentation methods. Images were reconstructed using the OSEM algorithm and two scatter correction methods were compared. An experiment to measure the camera deadtime was performed by adding activity into a bottle placed in a cylindrical phantom. Plots for observed count rate vs. true count rate were made, and the deadtime was calculated based on the paralyzable model. The protocol was applied to four patient data, and OLINDA and voxelized dosimetry calculations were used to create dose volume histograms for the kidneys. Lastly, a graphical user interface to allow for the quantitative reconstruction of the data obtained using any of the manufacturers was developed. Our results suggest that Bremsstrahlung contributions to the detected energy spectrum in imaging studies of ¹⁷⁷Lu have no degrading effects in image quantification. Our protocol recovers the activity in kidneys to within 10%. The deadtime correction based on paralyzable model was accurate for the count rates measured. The deadtime corrections should be performed based on scatter corrected photopeak window instead of the full spectrum. Lastly, the dose delivered to the kidneys in patient data was lower than the suggested dose per session in order to reach current toxicity limits.
Science, Faculty of
Physics and Astronomy, Department of
Graduate
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Jawaid, M. M. "Detection, localization and quantification of non-calcified coronary plaques in contrast enhanced CT angiography." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/19157/.

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State-of-the-art imaging equipment has increased clinician's ability to make non-invasive diagnoses of coronary heart disease (CHD); however, high volumes of imaging data make manual abnormality detection cumbersome in practice. In addition, the interpretation of CTA heavily relies upon the previous knowledge of the clinician. These limitations have driven an intense research in the context of automated solutions for fast, reliable and accurate diagnosis. Accordingly, in this thesis, we present an automated framework for detection, localization and quantification of the non-calcified coronary plaques in cardiac computed tomography angiography (CTA). The first contribution of the thesis is a coronary segmentation algorithm that is adaptive to the contrast agent and employs a hybrid energy incorporating local and global image statistics in a segmentation framework using partial differential equations (PDEs). Accordingly, we illustrated with the help of experimental evidence that a volume-specific intensity threshold leads to an improved segmentation in CTA. In the subsequent step, we employed a hybrid region-based energy for improved segmentation in CTA imagery. The hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The proposed method is less sensitive to the local optima problem and helps in reducing false positives, as well as it allows a certain degree of freedom for the initialization. Moreover, we employed an auto-correction feature for improved segmentation, as an auto-corrected mask captures the emerging peripheries of the coronary tree during the curve evolution. The effectiveness of the proposed model is demonstrated with the help of both qualitative and quantitative results, with a mean accuracy of 80% across the CTA dataset. The capability to address the variations in initial mask and localization radii simultaneously, makes our algorithm a feasible choice for coronary segmentation. The second contribution of the thesis is an automatic approach to analyse the segmented coronary tree for the presence of non-calcified plaques. The specific focus of this work is detection of non-calcified plaques in CTA, as intensity overlap between blood, fat and non-calcified plaques make the detection challenging. Non-calcified plaques are identified based on mean radial profiles that average the image intensities in concentric rings around the vessel centreline. Subsequently, an SVM classifier is applied to differentiate the abnormal coronary segments from normal ones. A total of 32 CTA volumes have been analysed and a detection accuracy of 88.4% with respect to the manual expert has been achieved. For plaque-affected segments, we further proposed a derivative-based method to localize the position and length of the plaque inside the segment. The plaque localization accuracy has been around 83.2%. Moreover, the proposed model has been tested on three different CTA datasets and has produced consistent results, demonstrating its reproducibility for generic CTA data. The final contribution of the thesis is a method to segment and quantify the non-calcified plaque. After evaluating the vessel wall thickness, posterior probability based voxel classification has been performed to quantify the lumen and plaque, respectively. Both qualitative and quantitative results demonstrate that the proposed model shows a good agreement with three independent experts. To optimize the processing time, we employed sparse field method in a level-set based active contour evolution.
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Melki, Imen. "Towards an automated framework for coronary lesions detection and quantification in cardiac CT angiography." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1022/document.

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Les maladies coronariennes constituent l'ensemble des troubles affectant les artères coronaires. Elles sont la première cause mondiale de mortalité. Par conséquent, la détection précoce de ces maladies en utilisant des techniques peu invasives fournit un meilleur résultat thérapeutique, et permet de réduire les coûts et les risques liés à une approche interventionniste. Des études récentes ont montré que la tomodensitométrie peut être utilisée comme une alternative non invasive et fiable pour localiser et quantifier ces lésions. Cependant, l'analyse de ces examens, basée sur l'inspection des sections du vaisseau, reste une tâche longue et fastidieuse. Une haute précision est nécessaire, et donc seulement les cliniciens hautement expérimentés sont en mesure d'analyser et d'interpréter de telles données pour établir un diagnostic. Les outils informatiques sont essentiels pour réduire les temps de traitement et assurer la qualité du diagnostic. L'objectif de cette thèse est de fournir des outils automatisés de traitement d'angiographie CT, pour la visualisation et l'analyse des artères coronaires d'une manière non invasive. Ces outils permettent aux pathologistes de diagnostiquer et évaluer efficacement les risques associés aux maladies cardio-vasculaires tout en améliorant la qualité de l'évaluation d'un niveau purement qualitatif à un niveau quantitatif. Le premier objectif de ce travail est de concevoir, analyser et valider un ensemble d'algorithmes automatisés utiles pour la détection et la quantification de sténoses des artères coronaires. Nous proposons un nombre de techniques couvrant les différentes étapes de la chaîne de traitement vers une analyse entièrement automatisée des artères coronaires. Premièrement, nous présentons un algorithme dédié à l'extraction du cœur. L'approche extrait le cœur comme un seul objet, qui peut être utilisé comme un masque d'entrée pour l'extraction automatisée des coronaires. Ce travail élimine l'étape longue et fastidieuse de la segmentation manuelle du cœur et offre rapidement une vue claire des coronaires. Cette approche utilise un modèle géométrique du cœur ajusté aux données de l'image. La validation de l'approche sur un ensemble de 133 examens montre l'efficacité et la précision de cette approche. Deuxièmement, nous nous sommes intéressés au problème de la segmentation des coronaires. Dans ce contexte, nous avons conçu une nouvelle approche pour l'extraction de ces vaisseaux, qui combine ouvertures par chemin robustes et filtrage sur l'arbre des composantes connexes. L'approche a montré des résultats prometteurs sur un ensemble de 11 examens CT. Pour une détection et quantification robuste de la sténose, une segmentation précise de la lumière du vaisseau est cruciale. Par conséquent, nous avons consacré une partie de notre travail à l'amélioration de l'étape de segmentation de la lumière, basée sur des statistiques propres au vaisseau. La validation avec l'outil d'évaluation en ligne du challenge de Rotterdam sur la segmentation des coronaires, a montré que cette approche présente les mêmes performances que les techniques de l'état de l'art. Enfin, le cœur de cette thèse est consacré à la problématique de la détection et la quantification des sténoses. Deux approches sont conçues et évaluées en utilisant l'outil d'évaluation en ligne de l'équipe de Rotterdam. La première approche se base sur l'utilisation de la segmentation de la lumière avec des caractéristiques géométriques et d'intensité pour extraire les sténoses coronaires. La seconde utilise une approche basée sur l'apprentissage. Durant cette thèse, un prototype pour l'analyse automatisée des artères coronaires et la détection et quantification des sténoses a été développé. L'évaluation qualitative et quantitative sur différents bases d'examens cardiaques montre qu'il atteint le niveau de performances requis pour une utilisation clinique
Coronary heart diseases are the group of disorders that affect the coronary artery vessels. They are the world's leading cause of mortality. Therefore, early detection of these diseases using less invasive techniques provides better therapeutic outcome, as well as reduces costs and risks, compared to an interventionist approach. Recent studies showed that X-ray computed tomography (CT) may be used as an alternative to accurately locate and grade heart lesions in a non invasive way. However, analysis of cardiac CT exam for coronaries lesions inspection remains a tedious and time consuming task, as it is based on the manual analysis of the vessel cross sections. High accuracy is required, and thus only highly experienced clinicians are able to analyze and interpret the data for diagnosis. Computerized tools are critical to reduce processing time and ensure quality of diagnostics. The goal of this thesis is to provide automated coronaries analysis tools to help in non-invasive CT angiography examination. Such tools allow pathologists to efficiently diagnose and evaluate risks associated with CVDs, and to raise the quality of the assessment from a purely qualitative level to a quantitative level. The first objective of our work is to design, analyze and validate a set of automated algorithms for coronary arteries analysis with the final purpose of automated stenoses detection and quantification. We propose different algorithms covering different processing steps towards a fully automated analysis of the coronary arteries. Our contribution covers the three major blocks of the whole processing chain and deals with different image processing fields. First, we present an algorithm dedicated to heart volume extraction. The approach extracts the heart as one single object that can be used as an input masque for automated coronary arteries segmentation. This work eliminates the tedious and time consuming step of manual removing obscuring structures around the heart (lungs, ribs, sternum, liver...) and quickly provides a clear and well defined view of the coronaries. This approach uses a geometric model of the heart that is fitted and adapted to the image data. Quantitative and qualitative analysis of results obtained on a 114 exam database shows the efficiency and the accuracy of this approach. Second, we were interested to the problem of coronary arteries enhancement and segmentation. In this context, we first designed a novel approach for coronaries enhancement that combines robust path openings and component tree filtering. The approach showed promising results on a set of 11 CT exam compared to a Hessian based approach. For a robust stenoses detection and quantification, a precise and accurate lumen segmentation is crucial. Therefore, we have dedicated a part of our work to the improvement of lumen segmentation step based on vessel statistics. Validation on the Rotterdam Coronary Challenge showed that this approach provides state of the art performances. Finally, the major core of this thesis is dedicated to the issue of stenosis detection and quantification. Two different approaches are designed and evaluated using the Rotterdam online evaluation framework. The first approach get uses of the lumen segmentation with some geometric and intensity features to extract the coronary stenosis. The second is using a learning based approach for stenosis detection and stenosis. The second approach outperforms some of the state of the art works with reference to some metrics. This thesis results in a prototype for automated coronary arteries analysis and stenosis detection and quantification that meets the level of required performances for a clinical use. The prototype was qualitatively and quantitatively validated on different sets of cardiac CT exams
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Ainslie-McLaren, Gillian. "Assessment of somatostatin image quantification with SPET and SPET-CT to aid characterisation of disease." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3179/.

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Work was undertaken in this thesis to assess the use of somatostatin image quantification with SPET and SPET-CT to aid the characterisation of disease within the body. Two radionuclide somatostatin analogues were used for this assessment, the first was NeoSPECT and the second, OctreoScan. The primary aim of work in this thesis was to assess the role of NeoSPECT imaging in the characterisation of disease within the lungs, that is, to differentiate benign from malignant disease. Two forms of image quantification were used in the NeoSPECT assessment, a tumour to background ratio (T:B) and a value of tumour percentage uptake (% uptake). Values of T:B and % uptake were calculated from SPET images acquired 2 hours post injection. T:B results from the benign group (n = 8) demonstrated a median T:B of 2.21, whilst the malignant group (n = 28) demonstrated a median T:B of 2.01. The differences between the groups were tested statistically via a Mann-Whitney test, which showed there to be no statistical difference between the groups (p=0.90, 95.4% CI of (-0.5598, 0.5498)). To undertake the calculation of % uptake a non-patient acquisition (a standard acquisition) was also required, unfortunately not all of the patient cohort used for the T:B assessment had this additional acquisition. As a result of this numbers were low for the % uptake assessment in patients with a benign histology (n = 2), therefore statistical analysis could not be performed. However, review of the range of values for each histology within the malignant group proved useful as no differences were demonstrated between the ranges of values which could help to differentiate between the histologies. Quantification of dual time point imaging was also assessed to determine if there were any variations in values calculated that could also help differentiate the different histologies. For this assessment patients were images at 2 and 4 hours post injection. Results from the Wilcoxon Signed Rank test of the T:B assessment found there to be no statistically significant difference between values of T:B calculated at 2 and 4 hours that was characteristic of tumour type (p=1.0 and p=0.14). The difference in % uptake between 2 and 4 hours was also assessed via a Wilcoxon Signed Rank test, this test also concluded there to be no significant difference value of % uptake between the two acquisitions of the malignant group (p = 0.73). An attempt was also made to quantify ‘other’ uptake within the mediastinum, however, a lack of anatomical information made correlation with histology impossible and as a result no firm conclusions relating image quantification to histology could be drawn from this work. Work from this thesis concluded no quantitative difference between tissue histology could be demonstrated using NeoSPECT, either from single or dual time point imaging. As a result of the NeoSPECT work a number of factors which limited the accuracy and reproducibility of SPET image quantification were identified. Towards the end of the NeoSPECT work hybrid imaging (SPET-CT) became available within the department at Glasgow Royal Infirmary. It was believed that hybrid imaging could resolve some of the limitations and subsequently improve the accuracy of SPET image quantification. However, NeoSPECT was removed from the market for a short period of time and therefore a similar somatostatin analogue, OctreoScan, was used to investigate if the accuracy of somatostatin image quantification could be improved as a result of SPET-CT and its associated reconstruction algorithms including a CT based attenuation correction. Firstly, a qualitative assessment of image quality using the new hybrid reconstructions techniques was undertaken via an observer study. Images were reconstructed with the existing reconstruction techniques, as used for the NeoSPECT work, and with the new hybrid imaging techniques. Four experienced observers blinded to reconstruction technique were asked to score images in terms of their overall image quality. A Friedman test was performed on the scores for each observer, three of the four observers demonstrated a statistically significant difference in their scores between the existing and new hybrid technique (p = 0.00, p = 0.003, p= 0.00), with the new hybrid technique being assigned the highest scores in terms of image quality. Images were also assessed semi-quantitatively via profile analysis which also demonstrated a clear differentiation between the existing and new hybrid techniques with increased image quality being demonstrated in the hybrid data set. The quantitative accuracy of hybrid imaging was also assessed using phantom data. For 111In the difference of the value of absolute activity calculated and that measured varied by 35% but this improved to 21 % when scatter and CT-attenuation based corrections were applied. For 99mTc a much more notable difference between the existing techniques used in chapter 2 and those available from the use of hybrid imaging was demonstrated, the difference in the value of absolute activity calculated and that measured improved from 67% to 0.04%, respectively. Work in this thesis clearly demonstrated an improvement in image quality and accuracy in SPET quantification as a result of hybrid imaging techniques.
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Wang, Yin. "Blood vessel segmentation and shape analysis for quantification of coronary artery stenosis in CT angiography." Thesis, City University London, 2011. http://openaccess.city.ac.uk/1186/.

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This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm.
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GALLIVANONE, FRANCESCA. "Quantification methods for PET/CT oncological studies and correlation approacches with proteomic and hystological data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19696.

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Aim of the PhD work was the development and validation of methods for the extraction of quantitative biomarkers from in vivo molecular imaging (from PET/CT oncological studies) to be correlated with ex vivo molecular imaging indexes (from hystological and proteomic data).
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Vallot, Delphine. "Reconstruction adaptative optimisée pour la quantification en tomographie de positons couplée à un tomodensitomètre." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30188.

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L'étude a été initiée à l'occasion de la disponibilité d'un algorithme itératif de reconstruction en tomographie par émission de positons, qui présente l'avantage d'atteindre la convergence grâce à une méthode de régularisation. Il a donc fallu évaluer ses performances et son éventuel apport en comparaison aux algorithmes de référence, puis étudier l'influence du seul paramètre accessible aux utilisateurs pour une optimisation en clinique. Pour cela, plusieurs tests ont été réalisés, sur fantômes d'abord, puis sur patients car les résultats obtenus n'étaient pas directement transposables en clinique. Cet algorithme a de nombreux avantages par rapport au standard actuel OSEM-MLEM (moins de bruit, meilleur contraste, meilleure détectabilité des lésions) mais pourrait encore être amélioré pour diminuer les artéfacts et la surestimation de certaines métriques, grâce à l'utilisation de fantômes plus anthropomorphiques et l'accès à plus de paramètres de reconstruction. Des travaux sont encore en cours avec l'éditeur
This study was initiated to evaluate an iterative reconstruction algorithm in positron emission tomography based on a regularization method to obtain convergence. Our aim was to assess its performance, in comparison with other currently available algorithms and to study the impact of the only parameter available to users for eventual optimization, both using anthropomorphic phantoms and clinical data. We confirm that this algorithm shows several advantages compared to the traditional OSEM-MLEM concerning noise, contrast and detectability. By using anthropomorphic phantoms and with access to more reconstruction parameters, the performance could be further improved to decrease the artefacts and the overestimation of certain metrics. Work in progress
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Morales, Pinzon Alfredo. "Lung segmentation and airway tree matching : application to aeration quantification in CT images of subjects with ARDS." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1019/document.

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Le syndrome de détresse respiratoire aiguë (SDRA) présente un taux de mortalité élevé, près de 40%, dans des unités de soins intensifs. Il est défini comme un ensemble de manifestations cliniques, radiologiques et physiologiques qui traduisent une intense inflammation pulmonaire et une hyperperméabilité pulmonaire, correspondant aux différentes agressions aiguës du poumon. Le prise en charge des patients atteints du SDRA nécessite une ventilation assistée qui, en cas de mauvaise adaptation des paramètres de ventilation, notamment, pression et volume, peut aggraver l'état du patient. Le réglage de ces paramètres est basé sur l'analyse de l'aération pulmonaire en réponse à la ventilation assistée. Cette analyse peut être faite sur des images tomodensitométriques (CT en anglais) après y avoir segmenté le parenchyme pulmonaire. Néanmoins, cette segmentation est entravée par l'augmentation de la densité du parenchyme, qui réduit le contraste entre le poumon et les structures voisines. Cette thèse cherche à fournir des outils de traitement d'images qui permettent aux experts l'analyse de l'aération pulmonaire dans des images CT acquises dans le cadre d'un projet sur le SDRA utilisant un modèle animal
Acute Respiratory Distress Syndrome (ARDS) is a life threatening disease presenting a high mortality of about 40% in intensive care units. It is the consequence of different pulmonary aggressions generating hypoxemia and pulmonary edema, which are radiologically expressed as infiltrations observable as opaque regions in the lung. The treatment of ARDS requires mechanical ventilation, which may deteriorate the state of the patient if the ventilation parameters, namely volume and pressure, are not correctly adjusted. To adjust the parameter settings to each individual case, lung aeration - in response to ventilation - needs to be assessed. This assessment can be done using computed tomography (CT) images. However, it requires the segmentation of the lung-parenchymal tissue, which is a challenging task in ARDS images due the opacities that hinder the image contrast. In this thesis we aim to provide the required tools for the experts to analyze the aeration in the images acquired within an ARDS project using an animal model
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Books on the topic "CT quantification"

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Morrison, Alan R., Joseph C. Wu, and Mehran M. Sadeghi. Cardiovascular Molecular Imaging. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0029.

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Cardiovascular molecular imaging is a relatively young but rapidly expanding discipline that consists of a biologically-targeted approach to the assessment of physiologic and pathologic processes in vivo. This novel approach to imaging involves the integration of multiple disciplines such as cell and molecular biology, chemistry, and imaging sciences. The ultimate goal is quantitative assessment of cardiovascular processes at the cellular and molecular level, moving beyond traditional diagnostic information, in order to guide individually tailored therapy. In fact, it is likely that specific approaches to molecular imaging will be developed in tandem with the development of novel therapeutic strategies. Recent advances in probe development and imaging systems have contributed to evolution of molecular imaging toward clinical translational. These include technological progress in traditional imaging platforms; along with the emergence of newer imaging modalities such as photoacoustic imaging. In addition, hybrid imaging (e.g. nuclear imaging with CT or MRI) has the potential for improved spatial localization, and more accurate quantification by coupling anatomic and biological information. In addition to potential clinical applications that address existing diagnostic gaps in cardiovascular medicine, molecular imaging allows for unique approaches to studying pathophysiology. This chapter is intended to provide an overview of the state of the art in cardiovascular molecular imaging, highlighting how it may improve the management of major cardiovascular diseases.
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Book chapters on the topic "CT quantification"

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Samei, Ehsan, and Jocelyn Hoye. "CT-Based Quantification." In Computed Tomography, 289–304. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26957-9_15.

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Ulzheimer, Stefan, Kaiss Shanneik, and Willi A. Kalender. "Noninvasive Quantification of Coronary Calcium." In CT of the Heart, 129–41. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-818-8:129.

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Schmermund, Axel, Stefan Möhlenkamp, and Raimund Erbel. "Detection and Quantification of Coronary Calcium With Electron Beam CT." In CT of the Heart, 83–89. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-818-8:083.

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Shemesh, Joseph. "Detection and Quantification of Coronary Calcium With Dual-Slice CT." In CT of the Heart, 91–99. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-818-8:091.

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Carr, J. Jeffrey. "Detection and Quantification of Calcified Coronary Plaque With Multidetector-Row CT." In CT of the Heart, 101–10. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-818-8:101.

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Goo, Jin Mo. "Computer-Aided Diagnosis and Quantification in Chest CT." In Multidetector-Row CT of the Thorax, 431–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30355-0_22.

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Rojulpote, Chaitanya S., and Paco E. Bravo. "Myocardial Blood Flow Quantification with PET/CT: Applications." In Hybrid Cardiac Imaging, 133–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83167-7_8.

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Gevenois, P. A., P. De Vuyst, M. Littani, J. Zanen, P. de Franquen, J. C. Yernault, and J. Struyven. "CT Quantification of Pulmonary Emphysema — Correlation with Pulmonary Function Tests: Preliminary Results on 15 Patients." In Advances in CT II, 3–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77463-8_1.

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Weinheimer, Oliver, Tobias Achenbach, Christian Buschsiewke, Claus Peter Heussel, Thomas Uthmann, and Hans-Ulrich Kauczor. "Quantification and Characterization of Pulmonary Emphysema in Multislice-CT." In Medical Data Analysis, 75–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39619-2_10.

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Sørensen, Lauge, Marco Loog, Pechin Lo, Haseem Ashraf, Asger Dirksen, Robert P. W. Duin, and Marleen de Bruijne. "Image Dissimilarity-Based Quantification of Lung Disease from CT." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 37–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15705-9_5.

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Conference papers on the topic "CT quantification"

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Figl, Michael, Romana Fragner, Patrick Heimel, Christian Loewe, and Wolfgang Birkfellner. "Streak artefact quantification for abdominal CT." In SPIE Medical Imaging, edited by David J. Manning and Craig K. Abbey. SPIE, 2011. http://dx.doi.org/10.1117/12.878577.

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Bandekar, Alok N., Morteza Naghavi, and Ioannis A. Kakadiaris. "Automated Pericardial Fat Quantification in CT Data." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259259.

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Bandekar, Alok N., Morteza Naghavi, and Ioannis A. Kakadiaris. "Automated Pericardial Fat Quantification in CT Data." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4397556.

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Parui, Sweta, Nilotpal Das, and Monisha Chakraborty. "CT Based Histogram Assisted Emphysema Severity Quantification." In 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021. http://dx.doi.org/10.1109/i2ct51068.2021.9418103.

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Persson, Mats, Bettina Meyer, Hans Bornefalk, and Mats Danielsson. "Quantification of ring artifact visibility in CT." In SPIE Medical Imaging, edited by Norbert J. Pelc, Robert M. Nishikawa, and Bruce R. Whiting. SPIE, 2012. http://dx.doi.org/10.1117/12.910537.

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Shechter, Gilad, Axel Thran, and Tsvi Katchalski. "Accurate material quantification in dual energy CT." In SPIE Medical Imaging, edited by Norbert J. Pelc, Robert M. Nishikawa, and Bruce R. Whiting. SPIE, 2012. http://dx.doi.org/10.1117/12.911291.

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Mallya, Yogish, A. K. Narayanan, and Lyubomir Zagorchev. "Automatic quantification of neo-vasculature from micro-CT." In SPIE Medical Imaging, edited by Josien P. W. Pluim and Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811162.

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Straumit, Ilya, Stepan V. Lomov, and Martine Wevers. "Quantification of micro-CT images of textile reinforcements." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF GLOBAL NETWORK FOR INNOVATIVE TECHNOLOGY AND AWAM INTERNATIONAL CONFERENCE IN CIVIL ENGINEERING (IGNITE-AICCE’17): Sustainable Technology And Practice For Infrastructure and Community Resilience. Author(s), 2017. http://dx.doi.org/10.1063/1.5008000.

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Gonzalez, Andres, Mehdi Teymouri, Zoya Heidari, and Olivier Lopez. "ANISOTROPY QUANTIFICATION USING HIGH-RESOLUTION WHOLE-CORE CT-SCAN IMAGES." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0087.

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Spatial anisotropy and heterogeneity in petrophysical properties can significantly affect formation evaluation of hydrocarbon bearing formations. A common example is permeability anisotropy, which is a consequence of the depositional mechanisms of sediments. Additionally, the variation in spatial distribution of rock components and the effect of post-depositional processes on the physical and chemical structure of the rock constituents can strongly impact the directional dependency of petrophysical, electrical, and elastic properties. Therefore, image-based quantification of spatial distribution of rock constituents can be used for anisotropy evaluation. Assessment of anisotropy has been previously accomplished through use of pore-scale images. However, the discrete nature of this images gives a narrow picture of anisotropy in larger scales. Whole-core computed tomography (CT) scan images, despite revealing the distribution of rock components at a coarser scale, provide a continuous medium for anisotropy estimation. Assessment of anisotropy using three-dimensional (3D) CT-scan data and incorporation of that information in well-log-based formation evaluation is, however, not widely studied or practiced in the petroleum industry. The objectives of this paper are (a) to develop a method to quantify anisotropy utilizing whole-core 3D CT-scan image stacks, (b) to provide a semi-continuous measure of rock anisotropy, and (c) to show the value of the proposed method by means of estimation of directional-dependent elastic properties. First, we pre-process the raw whole-core CT-scan images to remove undesired image artifacts and to generate an image containing pixels representing only the recovered core material. Then, we segment each whole-core CT-scan image stack into distinctive phases. Then, we conduct numerical simulations of electric potential distribution in conjunction with streamline tracing techniques to quantify the electrical tortuosity of the continuous phase in each cartesian direction. We employed the tortuosity distribution values in each direction as a measure of rock anisotropy. Finally, we use a simulation model to estimate direction-dependent elastic properties. We applied the introduced method to dual energy whole-core CT-scan image stacks acquired in a siliciclastic depth interval. Estimates of rock anisotropy obtained using the proposed method agreed with the observed visual distribution of the segmented phase and the observed heterogeneity in available slabbed whole-core photos and 2D CT-scan images. Additionally, estimation of directional-dependent elastic properties demonstrated the value of the proposed method. Anisotropy results coincided with directional-dependent estimation of elastic properties. We observed measurable anisotropy in the 3D CT-scan image stacks, which is important to be quantitatively taken into account in petrophysical/ mechanical evaluation of this formation. A unique contribution of the proposed workflow is the use of core-scale image data for anisotropy estimation and the continuous nature of the anisotropy estimates when compared with workflows employing only pore-scale image data. It should also be noted that the proposed method can potentially be employed to identify the optimum locations to acquire core plugs for further assessment of rock anisotropy.
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Liu, Shuang, Emily B. Sonnenblick, Lea Azour, David F. Yankelevitz, Claudia I. Henschke, and Anthony P. Reeves. "Fully automated gynecomastia quantification from low-dose chest CT." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293852.

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Reports on the topic "CT quantification"

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Sanchez, Maria Ambert. Quantification of Soil Physical Properties by Using X-Ray Computerized Tomography (CT) and Standard Laboratory (STD) Methods. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/822059.

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