Relevant bibliographies by topics / Imaging Medicale

Academic literature on the topic 'Imaging Medicale'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Imaging Medicale"

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Chaumoitre, K., P. Petit, and M. Panuel. "Douleurs d’origine « Medicale »." Journal de Radiologie 85, no. 9 (September 2004): 1322. http://dx.doi.org/10.1016/s0221-0363(04)77051-1.

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Aich, B., and Siemens. "Le workflow en imagerie medicale." Journal de Radiologie 85, no. 9 (September 2004): 1155. http://dx.doi.org/10.1016/s0221-0363(04)76509-9.

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Kremer, S., N. Holl, and T. Moser. "Imagerie de la moelle medicale." Journal de Radiologie 90, no. 10 (October 2009): 1238. http://dx.doi.org/10.1016/s0221-0363(09)74953-4.

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Frija, G., P. Halimi, A. Hernigou, N. Siauve, C. Mutschler, and F. Taillieu. "Validation medicale des demandes d’examens scanographiques." Journal de Radiologie 89, no. 10 (October 2008): 1218. http://dx.doi.org/10.1016/s0221-0363(08)75602-6.

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Picard, L. "La responsabilite medicale en radiologie vasculaire interventionnelle." Journal de Radiologie 85, no. 9 (September 2004): 1316. http://dx.doi.org/10.1016/s0221-0363(04)77030-4.

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Kandelman, M., and A. Khelifa. "4236 L’audit d’un service hospitalier d’imagerie medicale." Journal de Radiologie 86, no. 10 (October 2005): 1214–15. http://dx.doi.org/10.1016/s0221-0363(05)75048-4.

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Kandelman, M., and A. Khelifa. "5236 L’audit d’un service hospitalier d’imagerie medicale." Journal de Radiologie 86, no. 10 (October 2005): 1226. http://dx.doi.org/10.1016/s0221-0363(05)75108-8.

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Boudghene, F. "Bases de la communication en imagerie medicale." Journal de Radiologie 89, no. 10 (October 2008): 1517. http://dx.doi.org/10.1016/s0221-0363(08)76665-4.

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Thiesse, P., C. Vincent, and J. Carretier. "Les « sor savoir patients » en imagerie medicale." Journal de Radiologie 89, no. 10 (October 2008): 1518. http://dx.doi.org/10.1016/s0221-0363(08)76668-x.

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Thiesse, P., C. Vincent, and J. Carretier. "Les « sor savoir patients » en imagerie medicale." Journal de Radiologie 90, no. 10 (October 2009): 1286. http://dx.doi.org/10.1016/s0221-0363(09)75121-2.

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Dissertations / Theses on the topic "Imaging Medicale"

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Laudereau, Jean-Baptiste. "Acousto-optic imaging : challenges of in vivo imaging." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066414/document.

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Les tissus biologiques sont des milieux fortement diffusant pour la lumière. En conséquence, les techniques d'imagerie actuelles ne permettent pas d'obtenir un contraste optique en profondeur à moins d'user d'approches invasives. L'imagerie acousto-optique (AO) est une approche couplant lumière et ultrasons (US) qui utilise les US afin de localiser l'information optique en profondeur avec une résolution millimétrique. Couplée à un échographe commercial, cette technique pourrait apporter une information complémentaire permettant d'augmenter la spécificité des US. Grâce à une détection basée sur l'holographie photoréfractive, une plateforme multi-modale AO/US a pu être développée. Dans ce manuscrit, les premiers tests de faisabilité ex vivo sont détaillés en tant que premier jalon de l'imagerie clinique. Des métastases de mélanomes dans le foie ont par exemple été détectées alors que le contraste acoustique n'était pas significatif. En revanche, ces premiers résultats ont souligné deux obstacles majeurs à la mise en place d'applications cliniques.Le premier concerne la cadence d'imagerie de l'imagerie AO très limitée à cause des séquences US prenant jusqu'à plusieurs dizaines de secondes. Le second concerne le speckle qui se décorrèle en milieu vivant sur des temps inférieurs à 1 ms, trop rapide pour les cristaux photorefractif actuellement en palce. Dans ce manuscrit, je propose une nouvelle séquence US permettant d'augmenter la cadence d'imagerie d'un ordre de grandeur au moins ainsi qu'une détection alternative basée sur le creusement de trous spectraux dans des cristaux dopés avec des terres rares qui permet de s'affranchir de la décorrélation du speckle
Biological tissues are very strong light-scattering media. As a consequence, current medical imaging devices do not allow deep optical imaging unless invasive techniques are used. Acousto-optic (AO) imaging is a light-ultrasound coupling technique that takes advantage of the ballistic propagation of ultrasound in biological tissues to access optical contrast with a millimeter resolution. Coupled to commercial ultrasound (US) scanners, it could add useful information to increase US specificity. Thanks to photorefractive crystals, a bimodal AO/US imaging setup based on wave-front adaptive holography was developed and recently showed promising ex vivo results. In this thesis, the very first ones of them are described such as melanoma metastases in liver samples that were detected through AO imaging despite acoustical contrast was not significant. These results highlighted two major difficulties regarding in vivo imaging that have to be addressed before any clinical applications can be thought of.The first one concerns current AO sequences that take several tens of seconds to form an image, far too slow for clinical imaging. The second issue concerns in vivo speckle decorrelation that occurs over less than 1 ms, too fast for photorefractive crystals. In this thesis, I present a new US sequence that allows increasing the framerate of at least one order of magnitude and an alternative light detection scheme based on spectral holeburning in rare-earth doped crystals that allows overcoming speckle decorrelation as first steps toward in vivo imaging
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RUNDO, LEONARDO. "Computer-Assisted Analysis of Biomedical Images." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241343.

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Oggigiorno, la mole di dati biomedicali eterogenei è in continua crescita grazie alle nuove tecniche di sensing e alle tecnologie ad high-throughput. Relativamente all'analisi di immagini biomedicali, i progressi relativi alle modalità di acquisizione di immagini agli esperimenti di imaging ad high-throughput stanno creando nuove sfide. Questo ingente complesso di informazioni può spesso sopraffare le capacità analitiche sia dei medici nei loro processi decisionali sia dei biologi nell'investigazione di sistemi biochimici complessi. In particolare, i metodi di imaging quantitativo forniscono informazioni scientificamente rilevanti per la predizione, la prognosi o la valutazione della risposta al trattamento, prendendo in considerazione anche approcci di radiomica. Pertanto, l'analisi computazionale di immagini medicali e biologiche svolge un ruolo chiave in applicazioni di radiologia e di laboratorio. A tal proposito, framework basati su tecniche avanzate di Machine Learning e Computational Intelligence permettono di migliorare significativamente i tradizionali approcci tradizionali di Image Processing e Pattern Recognition. Tuttavia, le tecniche convenzionali di Intelligenza Artificiale devono essere propriamente adattate alle sfide uniche imposte dai dati di imaging biomedicale. La presente tesi mira a proporre innovativi metodi assistiti da calcolatore per l'analisi di immagini biomedicali, da utilizzare anche come strumento per lo sviluppo di Sistemi di Supporto alle Decisioni Cliniche, tenendo sempre in considerazione la fattibilità delle soluzioni sviluppate. In primo luogo, sono descritti gli algoritmi classici di Image Processing realizzati, focalizzandosi sugli approcci basati su regioni e sulla morfologia matematica. Dopodiché, si introducono le tecniche di Pattern Recognition, applicando il clustering fuzzy non supervisionato e i modelli basati su grafi (i.e., Random Walker e Automi Cellulari) per l'elaborazione di dati multispettrali e multimodali di imaging medicale. In riferimento ai metodi di Computational Intelligence, viene presentato un innovativo framework evolutivo basato sugli Algoritmi Genetici per il miglioramento e la segmentazione di immagini medicali. Inoltre, è discussa la co-registrazione di immagini multimodali utilizzando Particle Swarm Optimization. Infine, si investigano le Deep Neural Network: (i) le capacità di generalizzazione delle Convolutional Neural Network nell'ambito della segmentazione di immagini medicali provenienti da studi multi-istituzionali vengono affrontate mediante la progettazione di un'architettura che integra blocchi di ricalibrazione delle feature, e (ii) la generazione di immagini medicali realistiche basata sulle Generative Adversarial Network è applicata per scopi di data augmentation. In conclusione, il fine ultimo di tali studi è quello di ottenere conoscenza clinicamente e biologicamente utile che possa guidare le diagnosi e le terapie differenziali, conducendo verso l'integrazione di dati biomedicali per la medicina personalizzata. Difatti, i metodi assistiti da calcolatore per l'analisi delle immagini biomedicali sono vantaggiosi sia per la definizione di biomarcatori basati sull'imaging sia per la medicina e biologia quantitativa.
Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical image analysis, the advances in image acquisition modalities and high-throughput imaging experiments are creating new challenges. This huge information ensemble could overwhelm the analytic capabilities needed by physicians in their daily decision-making tasks as well as by biologists investigating complex biochemical systems. In particular, quantitative imaging methods convey scientifically and clinically relevant information in prediction, prognosis or treatment response assessment, by also considering radiomics approaches. Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications. In this regard, frameworks based on advanced Machine Learning and Computational Intelligence can significantly improve traditional Image Processing and Pattern Recognition approaches. However, conventional Artificial Intelligence techniques must be tailored to address the unique challenges concerning biomedical imaging data. This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis, also as an instrument in the development of Clinical Decision Support Systems, by always keeping in mind the clinical feasibility of the developed solutions. The devised classical Image Processing algorithms, with particular interest to region-based and morphological approaches in biomedical image segmentation, are first described. Afterwards, Pattern Recognition techniques are introduced, applying unsupervised fuzzy clustering and graph-based models (i.e., Random Walker and Cellular Automata) to multispectral and multimodal medical imaging data processing. Taking into account Computational Intelligence, an evolutionary framework based on Genetic Algorithms for medical image enhancement and segmentation is presented. Moreover, multimodal image co-registration using Particle Swarm Optimization is discussed. Finally, Deep Neural Networks are investigated: (i) the generalization abilities of Convolutional Neural Networks in medical image segmentation for multi-institutional datasets are addressed by conceiving an architecture that integrates adaptive feature recalibration blocks, and (ii) the generation of realistic medical images based on Generative Adversarial Networks is applied to data augmentation purposes. In conclusion, the ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies, leading towards biomedical data integration for personalized medicine. As a matter of fact, the proposed computer-assisted bioimage analysis methods can be beneficial for the definition of imaging biomarkers, as well as for quantitative medicine and biology.
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3

Carlak, Hamza Feza. "Medical Electro-thermal Imaging." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614168/index.pdf.

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Breast cancer is the most crucial cancer type among all other cancer types. There are many imaging techniques used to screen breast carcinoma. These are mammography, ultrasound, computed tomography, magnetic resonance imaging, infrared imaging, positron emission tomography and electrical impedance tomography. However, there is no gold standard in breast carcinoma diagnosis. The object of this study is to create a hybrid system that uses thermal and electrical imaging methods together for breast cancer diagnosis. Body tissues have different electrical conductivity values depending on their state of health and types. Consequently, one can get information about the anatomy of the human body and tissue&rsquo
s health by imaging tissue conductivity distribution. Due to metabolic heat generation values and thermal characteristics that differ from tissue to tissue, thermal imaging has started to play an important role in medical diagnosis. To increase the temperature contrast in thermal images, the characteristics of the two imaging modalities can be combined. This is achieved by implementing thermal imaging applying electrical currents from the body surface within safety limits (i.e., thermal imaging in active mode). Electrical conductivity of tissues changes with frequency, so it is possible to obtain more than one thermal image for the same body. Combining these images, more detailed information about the tumor tissue can be acquired. This may increase the accuracy in diagnosis while tumor can be detected at deeper locations. Feasibility of the proposed technique is investigated with analytical and numerical simulations and experimental studies. 2-D and 3-D numerical models of the female breast are developed and feasibility work is implemented in the frequency range of 10 kHz and 800 MHz. Temporal and spatial temperature distributions are obtained at desired depths. Thermal body-phantoms are developed to simulate the healthy breast and tumor tissues in experimental studies. Thermograms of these phantoms are obtained using two different infrared cameras (microbolometer uncooled and cooled Quantum Well Infrared Photodetectors). Single and dual tumor tissues are determined using the ratio of uniform (healthy) and inhomogeneous (tumor) images. Single tumor (1 cm away from boundary) causes 55 °
mC temperature increase and dual tumor (2 cm away from boundary) leads to 50 °
mC temperature contrast. With multi-frequency current application (in the range of 10 kHz-800 MHz), the temperature contrast generated by 3.4 mm3 tumor at 9 mm depth can be detected with the state-of-the-art thermal imagers.
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Belle, Valérie. "Le contraste en imagerie d'activation cérébrale chez l'homme par résonance magnétique nucléaire : aspects physiques et biophysiques." Université Joseph Fourier (Grenoble ; 1971-2015), 1995. http://www.theses.fr/1995GRE10085.

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Ce travail est consacre aux aspects physiques et biophysiques de l'imagerie fonctionnelle cerebrale chez l'homme par resonance magnetique nucleaire. Dans le premier chapitre, le principe des techniques d'imagerie rapide, dont l'imagerie fonctionnelle est une des applications, est expose. Cette partie insiste notamment sur la sensibilite des differentes sequences aux parametres susceptibles de modifier le signal rmn lors d'une activation cerebrale. Les deux chapitres suivants presentent un ensemble de resultats experimentaux concernant l'imagerie des fonctions cerebrales motrices etudiees a 1,5 tesla avec une technique d'echo de gradient. Nous avons en premier lieu evalue les differents phenomenes physiologiques responsables des variations de signal detectees: variation du debit sanguin cerebral (effet de flux) et variation du taux d'oxygenation sanguine (effet de susceptibilite magnetique). Par la suite, nous avons demontre que les variations de signal observees provenaient de maniere predominante de certaines veines de drainage situees a la surface des zones corticales d'interet. Ce resultat nous a permis d'introduire le concept d'angiographie fonctionnelle. Le dernier chapitre est independant du sujet principal. Il concerne le developpement d'une technique d'acquisition rapide de profils d'excitation d'impulsions selectives
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Alomari, Zainab Rami Saleh. "Plane wave imaging beamforming techniques for medical ultrasound imaging." Thesis, University of Leeds, 2017. http://etheses.whiterose.ac.uk/18127/.

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In ultrasound array imaging, the beamforming operation is performed by aligning and processing the received echo signals from each individual array element to form a complete image. This operation can be performed in many different ways, where adaptive and non-adaptive beamformers are considered as the main categories. Adaptive beamformers exploit the statistical correlation between the received data to find a weighting value at the focal point, instead of using a fixed weighting window in non-adaptive beamforming. This results in a significant improvement in the image quality in terms of resolution and sidelobes reduction. This improvement is necessary for ultrafast imaging because of the lack of focusing in Plane Wave Imaging (PWI) that results in lowering the SNR, and thus the produced imaging quality is reduced. This thesis analyses different adaptive beamforming techniques for ultrafast imaging. For accurate medical diagnosis, the frame rate, the imaging resolution, contrast and speckle homogeneity are all considered as important parameters that contribute to the final imaging result. To be able to evaluate each technique by minimizing the effect of external parameters, two different analysis were performed. First an empirical expression for PWI lateral resolution is produced after studying the effect of the imaging parameters on this imaging method. Then a method for selecting the suitable steering angles in Compound Plane- Wave Imaging (CPWI) is introduced, with a detailed explanation for the effect of the compound angles on resolution and sidelobes level. In order to add the contrast improvement to the properties of adaptive beamformers, some techniques like the coherence-based factors and Eigenspace-Based Minimum Variance (ESBMV) are produced in the literature. After demonstrating the principle of Minimum Variance adaptive beamformer, a detailed comparison for the types of coherence-based factors is given. In addition, a new technique of Partial-ESBMV is introduced to modify reference ESBMV so that no Black Box Region artefacts nor dark spots appear when using this method in medical imaging. After explaining its background and properties using cystic and wire phantoms, the proposed method is applied to the real RF data of carotid artery, as an application to clarify the efficiency of this method in medical ultrasound imaging.
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Smith, Rhodri. "Motion correction in medical imaging." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/841883/.

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It is estimated that over half of current adults within Great Britain under the age of 65 will be diagnosed with cancer at some point in their lifetime. Medical Imaging forms an essential part of cancer clinical protocols and is able to furnish morphological, metabolic and functional information. The imaging of molecular interactions of biological processes in vivo with Positron Emission Tomography (PET) is informative not only for disease detection but also therapeutic response. The qualitative and quantitative accuracy of imaging is thus vital in the extraction of meaningful and reproducible information from the images, allowing increased sensitivity and specificity in the diagnosis and precision of image guided treatment. Furthermore the utilization of complementary information obtained via Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) in integrated PET-CT and PET-MR devices offers the potential for the synergistic effects of hybrid imaging to provide increased detection and precision of diagnosis with reduced radiation dose in a fully comprehensive single imaging examination. With the increasing sophistication in imaging technology respiratory organ motion during imaging has demonstrated itself to be a major degrading factor of PET image resolution. A modest estimate of respiratory motion amplitude of 5mm, results in PET system resolution degrading from ≈ 5mm to ≈8.5mm. This evidently has an impact on cancer lesion detectability. Therefore accurate and robust methods for respiratory motion correction are required for both clinical effectiveness and economic justification for purchasing state of the art hybrid PET scanners with high resolution capabilities. In addition the judicious use of imaging resources from hybrid imaging devices coupled with advanced image processing / acquisition protocols will allow optimization of data used for improving quantitative accuracy of PET images and those used for clinical interpretation. In essence it would prove impractical to use the MR scanner purely for monitoring respiratory motion. Numerous methods exist to attempt to correct PET imaging for respiratory motion. As presented in this thesis many methods demonstrate themselves to be ineffective in the clinical setting where the patients breathing patterns appear irregular in comparison to the idealized situation of regular periodic motion. Advanced respiratory motion correction techniques utilize hybrid PET/CT, PET/MR scanners coupled with an external source of information which serves as a surrogate to build a static correspondence to the estimated internal respiratory motion. Static models however are unable to adapt to their external environment and do not consider time dependent changes in the state of a system. A further confounding factor in the development and assessment of motion correction schemes for medical imaging data is the inability to acquire volumetric data with high contrast and high spatial and temporal resolution which serves as a ground truth for quantifying model accuracy and confidence. This thesis addresses both problems by analysing respiratory motion correspondence modelling under a manifold learning and alignment paradigm which may be used to consolidate many of the respiratory motion estimation models that exist today. A Bayesian approach is adopted in this work to incorporate a-priori information into the model building stage for a more robust, flexible adaptive respiratory motion estimation / correction framework. This thesis constructs and tests the first proposed adaptive motion model to correlate a surrogate signal with internal motion. This adaptive approach allows the relationship between external surrogate signal and internal motion to change dependent upon breathing pattern and system noise. The adaptive model was compared to a state-of the-art static model and allows more accurate motion estimates to be made when the patient is breathing with an irregular pattern. Testing performed on MRI data from 9 volunteers demonstrated the adaptive model was statistically more significant (p < 0.001) in the presence of irregular motion in comparison to a static model. The adaptive Kalman model on average reduced the error in motion by 30% in comparison to the static model. Utilizing the adaptive model during a typical PET study would theoretically result in ≈ 10% increase in PET resolution in comparison to relying on a static model alone for motion correction. The adaptive Kalman model has the capability to increase the performance of PET system resolution from ≈ 8.5mm to ≈ 5.8mm, ≈ 30%. A simulated PET study also demonstrated ≈ 30% increase in tumour uptake when using motion correction. Also demonstrated in the thesis is the first method to acquire volumetric imaging data from sparse MR samples during free breathing to allow the realization of high contrast, high resolution 4D models of respiratory motion using limited acquired data. The developed framework facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities as well as informing the development of generalizable models of human respiration. It is shown that the developed approach can provide equivalent motion vector fields in comparison to fully sampled 4D dynamic data. The incorporation of the manifold alignment step into the sparse motion model reduces the error in motion estimates by ≈ 16%. Example images of propagated motion are also presented as supplementary information. The thesis concludes by generalizing the concepts in this work and looking to utilize the developed methods to other problems in the medical imaging arena.
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Ye, Luming. "Perception Metrics in Medical Imaging." Thesis, KTH, Medicinsk teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102186.

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Fonseca, Francisco Xavier dos Santos. "GPU power for medical imaging." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7853.

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Mestrado em Engenharia de Computadores e Telemática
A aplicação CapView utiliza um algoritmo de classificação baseado em SVM (Support Vector Machines) para automatizar a segmentação topográfica de vídeos do trato intestinal obtidos por cápsula endoscópica. Este trabalho explora a aplicação de processadores gráficos (GPU) para execução paralela desse algoritmo. Após uma etapa de otimização da versão sequencial, comparou-se o desempenho obtido por duas abordagens: (1) desenvolvimento apenas do código do lado do host, com suporte em bibliotecas especializadas para a GPU, e (2) desenvolvimento de todo o código, incluindo o que é executado no GPU. Ambas permitiram ganhos (speedups) significativos, entre 1,4 e 7 em testes efetuados com GPUs individuais de vários modelos. Usando um cluster de 4 GPU do modelo de maior capacidade, conseguiu-se, em todos os casos testados, ganhos entre 26,2 e 27,2 em relação à versão sequencial otimizada. Os métodos desenvolvidos foram integrados na aplicação CapView, utilizada em rotina em ambientes hospitalares.
The CapView application uses a classification algorithm based on SVMs (Support Vector Machines) for automatic topographic segmentation of gastrointestinal tract videos obtained through capsule endoscopy. This work explores the use graphic processors (GPUs) to parallelize the segmentation algorithm. After an optimization phase of the sequential version, two new approaches were analyzed: (1) development of the host code only, with support of specialized libraries for the GPU, and (2) development of the host and the device’s code. The two approaches caused substantial gains, with speedups between 1.4 and 7 times in tests made with several different individual GPUs. In a cluster of 4 GPUs of the most capable model, speedups between 26.2 and 27.2 times were achieved, compared to the optimized sequential version. The methods developed were integrated in the CapView application, used in routine in medical environments.
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Zhang, Hongbin. "Signal detection in medical imaging." Diss., The University of Arizona, 2001. http://hdl.handle.net/10150/290512.

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The goal of this research is to develop computational methods for predicting how a given medical imaging system and reconstruction algorithm will perform when mathematical observers for tumor detection use the resulting images. Here the mathematical observer is the ideal observer, which sets an upper limit to the performance as measured by the Bayesian risk or receiver operating characteristic analysis. This dissertation concentrates on constructing the ideal observer in complex detection problems and estimating its performance. Thus the methods reported in this dissertation can be used to approximate the ideal observer in real medical images. We define our detection problem as a two-hypothesis detection task where a known signal is superimposed on a random background with complicated distributions and embedded in independent Poisson noise. The first challenge of this detection problem is that the distribution of the random background is usually unknown and difficult to estimate. The second challenge is that the calculation of the ideal observer is computationally intensive for non stylized problems. In order to solve these two problems, our work relies on multiresolution analysis of images. The multiresolution analysis is achieved by decomposing an image into a set of spatial frequency bandpass images so each bandpass image represents information about a particular fitness of detail or scale. Connected with this method, we will use three types of image representation by invertible linear transforms. They are the orthogonal wavelet transform, pyramid transform and independent component analysis. Based on the findings from human and mammalian vision, we can model textures by using marginal densities of a set of spatial frequency bandpass images. In order to estimate the distribution of an ensemble of images given the empirical marginal distributions of filter responses, we can use the maximum entropy principle and get a unique solution. We find that the ideal observer calculates a posterior mean of the ratio of conditional density functions, or the posterior mean of the ratio of two prior density functions, both of which are high dimensional integrals and have no analytic solution usually. But there are two ways to approximate the ideal observer. The first one is a classic decision process; that is, we construct a classifier following feature extraction steps. We use the integrand of the posterior mean as features, which are calculated at the estimated background close to the posterior mode. The classifier combines these features to approximate the integral (or the ideal observer). Finally, if we know both the conditional density function and the prior density function then we can also approximate the high dimensional integral by Monte Carlo integration methods. Since the calculation of the posterior mean is usually a very high dimensional integration problem, we must construct a Markov chain, which can explore the posterior distribution efficiently. We will give two proposal functions. The first proposal function is the likelihood function of random backgrounds. The second method makes use of the multiresolution representation of the image by decomposing the image into a set of spatial frequency bands. Sampling one pixel in each band equivalently updates a cluster of pixels in the neighborhood of the pixel location in the original image.
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Fisher, Joshua. "In Vitro Binding Kinetics of ChemoFilter with Cisplatin." Thesis, University of California, San Francisco, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10165379.

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Introduction: Endovascular chemotherapy treatment allows localized delivery adjacent to the target tumor; allowing an increased dosage and decreased leakage to other areas. It also allows for the opportunity to filter chemotherapy escaping the target tumor and entering the bloodstream. The ChemoFilter - a temporarily deployable, endovascular device will do just that; reducing systemic toxicity thus reducing adverse side effects from chemotherapy treatment. This will allow further increased dosage, increased tumor suppression, and increased tolerance to treatment. ChemoFilter has successfully filtered the chemotherapeutic Doxorubicin, but had yet to be tested in other chemotherapeutics. This study evaluates binding with new chemotherapeutics: Cisplatin, Carboplatin, and a cocktail comprised of Cisplatin and Doxorubicin.

Materials and Methods: ChemoFilter prototypes based on: 1.) Genomic DNA and 2.) Dowex (ion-exchange) resin, were evaluated for their ability to bind chemotherapy in vitro in phosphate-buffered saline (PBS). ChemoFilter was tested free in solution and encapsulated in nylon or polyester mesh packets of various dimensions. Concentrations were quantified using inductively coupled plasma mass spectrometry (IPC-MS), ultraviolet-visible spectrophotometry (UV-Vis), or fluorospectrometry. 11C, 13C, and/or 14C radiolabeling Carboplatin began for in vitro and in vivo ChemoFilter quantification. In vitro quantification can include scintillation and/or gamma counting. In vivo may include Positron Emission Tomography (PET) imaging, Hyperpolarized 13C Magnetic Resonance Imaging (MRI), and/or Magnetic Resonance Spectroscopy (MRS) for real-time visualization. Reactions were verified using High Performance Liquid Chromatography (HPLC) for chemical species identification.

Results and Discussion: Results indicate significant and nearly complete, ~99% (p<0.01) clearance of Cisplatin using the DNA ChemoFilter sequestered in Nylon mesh, quantified with gold standard ICP-MS (evidenced at 214 and 265 nm). The Ion-exchange ChemoFilter has significant clearance, within seconds, of both Doxorubicin and Cisplatin mixed in a cocktail solution. However, it appears some Cisplatin is binding to the Nylon Mesh itself. Size, shape, and material of the mesh have been optimized. A potential mechanism for 11C, 13C, or 14C radiolabeling of Carboplatin has been developed and early results have been successful. ChemoFilter works much more efficiently when sequestered in nylon packets of specific geometries. Significant improvements have been made to ChemoFilter, moving the device closer to clinical trials.

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Books on the topic "Imaging Medicale"

1

Introduction to diagnostic imaging. Philadelphia: Saunders, 1992.

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Principles of radiographic imaging: An art and a science. [Place of publication not identified]: Cengage Learning, 2006.

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R, Carlton Richard, Adler Arlene McKenna, and Burns Barry RT(R), eds. Principles of radiographic imaging: An art and a science. 4th ed. Clifton Park, NY: Thomson Delmar Learning, 2006.

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María-Ester, Brandan, Herrera Corral G, and Ortega Martínez R, eds. Medical physics: Second Mexican symposium, Coyoacán, México, February 1998. Woodbury, N.Y: American Institute of Physics, 1998.

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Tuchin, V. Tissue optics: Light scattering methods and instruments for medical diagnosis. Bellingham, Wash: SPIE Optical Engineering Press, 2000.

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1953-, Ritenour E. Russell, and Hendee William R, eds. Medical imaging physics. 3rd ed. St. Louis: Mosby Year Book, 1992.

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Wolbarst, Anthony B., Patrizio Capasso, and Andrew R. Wyant. Medical Imaging. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118480267.

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Iniewski, Krzysztof, ed. Medical Imaging. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009. http://dx.doi.org/10.1002/9780470451816.

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1949-, LeVine Harry, ed. Medical imaging. Santa Barbara, Calif: ABC-CLIO, 2010.

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Erondu, Okechukwu Felix. Medical imaging. Rijeka: InTech, 2011.

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Book chapters on the topic "Imaging Medicale"

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van Ooijen, Peter M. A., and Wiard Jorritsma. "Medical Imaging Informatics in Nuclear Medicine." In Quality in Nuclear Medicine, 241–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33531-5_16.

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Krupinski, Elizabeth A. "Medical Imaging." In Handbook of Visual Display Technology, 545–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14346-0_186.

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Krupinski, Elizabeth A. "Medical Imaging." In Handbook of Visual Display Technology, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35947-7_186-1.

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Dallas, William J. "Medical Imaging." In ASST ’87 6. Aachener Symposium für Signaltheorie, 302–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-73015-3_57.

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Hoskins, Peter R., Stephen F. Keevil, and Saeed Mirsadraee. "Medical Imaging." In Cardiovascular Biomechanics, 163–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46407-7_9.

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Majumdar, Angshul. "Medical Imaging." In Compressed Sensing for Engineers, 151–99. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis, [2019] | Series: Devices, circuits, and systems: CRC Press, 2018. http://dx.doi.org/10.1201/9781351261364-10.

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Olson, Tim. "Medical Imaging." In Applied Fourier Analysis, 255–77. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7393-4_9.

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Jin, Miao, Xianfeng Gu, Ying He, and Yalin Wang. "Medical Imaging." In Conformal Geometry, 175–251. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75332-4_9.

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Sargsyan, Ashot E. "Medical Imaging." In Principles of Clinical Medicine for Space Flight, 181–207. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68164-1_9.

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Gupta, Tapan K. "Medical Imaging." In Radiation, Ionization, and Detection in Nuclear Medicine, 187–250. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34076-5_4.

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Conference papers on the topic "Imaging Medicale"

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Sokol, Yevgen, Oleg Avrunin, Kostyantyn Kolisnyk, and Petro Zamiatin. "Using Medical Imaging in Disaster Medicine." In 2020 IEEE 4th International Conference on Intelligent Energy and Power Systems (IEPS). IEEE, 2020. http://dx.doi.org/10.1109/ieps51250.2020.9263175.

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Taylor, Russell H. "Medical robotics and computer-integrated interventional medicine." In SPIE Medical Imaging, edited by David R. Holmes III and Kenneth H. Wong. SPIE, 2012. http://dx.doi.org/10.1117/12.916500.

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Goeringer, Fred. "Medical diagnostic imaging support systems for military medicine." In Medical Imaging '91, San Jose, CA, edited by Yongmin Kim. SPIE, 1991. http://dx.doi.org/10.1117/12.45185.

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WIEBE, LEONARD I. "MEDICAL IMAGING APPLICATIONS IN PRE-CLINICAL AND CLINICAL MEDICINE." In Proceedings of the 3rd International Conference on Isotopes. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812793867_0046.

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Journeau, P. "Imaging medical imaging." In SPIE Medical Imaging, edited by Tessa S. Cook and Jianguo Zhang. SPIE, 2015. http://dx.doi.org/10.1117/12.2084490.

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Bashkansky, M., and J. Reintjes. "Nonlinear optical coherent gating for medical imaging with lasers." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mz6.

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Abstract:
It is often desirable to form an image of the object hidden in or behind a scattering medium. For example, the whole new field of transillumination1,2 was established in medicine. Several techniques, relying on the fact that the highly scattered light is delayed in time with respect to the less scattered light, have been used. Various time gating2 techniques with very short laser pulses (<10 psec) have been employed. Recently a chrono-coherent imaging3 technique was developed with long broadband laser pulses. The interference between imaging and reference beams is recorded on the film as a holographic image. One of the disadvantages of this method is the holographic image degradation due to the scattered light. It is also not a real-time imaging technique. We developed a new nonlinear optical cross-correlation technique for hidden object imaging which can be used with long broadband laser pulses. Using nonlinear optical techniques the following advantages are attained: good discrimination against scattered radiation, real-time imaging, image amplification resulting in extremely high sensitivity, up or down conversion of image to a more convenient laser wavelength. This is a general method that can be used with many nonlinear optical processes. We discuss some of the relevant nonlinear optical effects, their advantages, and disadvantages.
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Debebe, S. A., R. Bhatt, and A. J. McGoron. "Web Based Interactive Medical Imaging Applications for Teaching Nuclear Medicine." In 2013 29th Southern Biomedical Engineering Conference (SBEC 2013). IEEE, 2013. http://dx.doi.org/10.1109/sbec.2013.75.

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Prinz, Michael, Manfred Gengler, and Ernst Schuster. "Medical imaging." In Sixth International Workshop on Digital Image Processing and Computer Graphics, edited by Emanuel Wenger and Leonid I. Dimitrov. SPIE, 1998. http://dx.doi.org/10.1117/12.301390.

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Donjon, J., T. Tsujiuchi, and L. Guyot. "Medical Imaging." In International Topical Meeting on Image Detection and Quality, edited by Lucien F. Guyot. SPIE, 1987. http://dx.doi.org/10.1117/12.966739.

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"Medical Imaging." In 2006 IEEE International Workshop on Medical Measurement and Applications. IEEE, 2006. http://dx.doi.org/10.1109/memea.2006.1644459.

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Reports on the topic "Imaging Medicale"

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Chapman, Leroy. Application of Diffraction Enhanced Imaging to Medical Imaging. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada395133.

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Keto, E., and S. Libby. Medical imaging with coded apertures. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/100008.

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Barrett, Harrison H. Information Processing in Medical Imaging Meeting (IPMI). Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada278488.

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Heese, V., N. Gmuer, and W. Thomlinson. A survey of medical diagnostic imaging technologies. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/5819036.

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Heese, V., N. Gmuer, and W. Thomlinson. A survey of medical diagnostic imaging technologies. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/10121224.

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Diakides, Nicholas A. Exploitation of Infrared Imaging in Medicine. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada391763.

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Chaple, Ivis. Production and Purification of Radiometals for Medical Imaging. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1843150.

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Jin, Zheming. Improving the performance of medical imaging applications using SYCL. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1630290.

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Lee, Hyoung-Koo. Application of a-Si:H radiation detectors in medical imaging. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/100242.

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Jin, Zheming. Improving the Performance of Medical Imaging Applications using SYCL. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1577129.

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