1,720,962 research outputs found
HandCT: hands-on computational dataset for X-Ray Computed Tomography
No full textHandCT is a computational dataset to train machine-learning models for X-Ray Computed Tomography (CT). It consists of a meshed hand model, of which pose and anatomical properties are computed at run-time from a script. As such, it is an accurate modeling of anatomical phantoms of only 1.35 mB, and reproducibility is ensured using random seeds. It allows the user to have full control over the imaging chain, from projection to reconstruction, and over the X-Ray interaction with the different parts of the model by a simple variable editing. This open-source solution relies on the freeware Blender for the modelling and Python for the computations. The first deals with modelling, rigging and deformations, whilst the later ensures transformations such as scaling, translation, or else forward projection. This dataset can be used to train and evaluate regularisation procedures for low-energy, dual-energy and scarce-view CT.</span
Integrating shape priors to X-ray computed tomography using machine-learning
No full textThis thesis focuses on using Computer-Assisted Design (CAD) data priors to infer missing X-Ray measurements instead of sampling them. X-Ray Computed Tomography (XCT) is a non-destructive imaging technique that produces cross-sectional, volumetric images of bodies sensitive to X-Ray. It relies on the repeated sampling of the body from different points of view and on numerical methods to obtain an image from the measurements. Due to the models on which the computations rely, that necessarily approximate the actual object and sampling process, reconstructing the causal factor that produced the sequence of measurements, the sinogram, is a non-trivial task. Indeed, it involves solving an ill-posed inverse problem, which becomes under-determined under certain circumstances that depends on the object's material or else the sampling constraints. Several approaches to this problem have been proposed, but they primarily focus on reconstruction algorithms and overlook certain contingencies of the sampling process. Specifically, reconstruction schemes need to account for the opacity of specific object's components to X-Ray, objects of significant size that cannot fit fully in CT-scanner's gantries and scanning time in non-destructive testing. Reconstruction schemes designed for data acquired in such scenarios are yet to be proposed and implemented at scale, and crucially, no pre-processing of the raw data from the detector has been thoroughly investigated. In this thesis, we investigate various techniques to provide an initial estimate of missing and impaired measurements, given prior knowledge about the sampled object. This goal stems from the availability of CAD models in engineering, as well as the recent developments of machine-learning tools allowing to learn cross-modality dependencies between CAD priors and actual measurements
Data-driven interpolation for super-scarce X-ray computed tomography
No full textWe address the problem of reconstructing X-Ray tomographic images from scarce measurements by interpolating missing acquisitions using a self-supervised approach. To do so, we train shallow neural networks to combine two neighbouring acquisitions into an estimated measurement at an intermediate angle. This procedure yields an enhanced sequence of measurements that can be reconstructed using standard methods, or further enhanced using regularisation approaches. Unlike methods that improve the sequence of acquisitions using an initial deterministic interpolation followed by machine-learning enhancement, we focus on inferring one measurement at once. This allows the method to scale to 3D, the computation to be faster and crucially, the interpolation to be significantly better than the current methods, when they exist. We also establish that a sequence of measurements must be processed as such, rather than as an image or a volume. We do so by comparing interpolation and up-sampling methods, and find that the latter significantly under-perform. We compare the performance of the proposed method against deterministic interpolation and up-sampling procedures and find that it outperforms them, even when used jointly with a state-of-the-art projection-data enhancement approach using machine-learning. These results are obtained for 2D and 3D imaging, on large biomedical datasets, in both projection space and image space
Sinogram enhancement with generative adversarial networks using shape priors
No full textCompensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems. We tackle Limited Angle Tomography by completing the set of acquisitions using a generative model and prior-knowledge about the scanned object. Using a Generative Adversarial Network as model and Computer-Assisted Design data as shape prior, we demonstrate a quantitative and qualitative advantage of our technique over other state-of-the-art methods. Inferring a substantial number of consecutive missing measurements, we offer an alternative to other image inpainting techniques that fall short of providing a satisfying answer to our research question: can X-Ray exposition be reduced by using generative models to infer lacking measurements
Sinogram inpainting with Generative Adversarial Networks and shape priors
No full textX-Ray computed tomography is a widely used, non-destructive imaging technique that computes cross-sectional images of an object from a set of X-Ray absorption profiles (the so-called sinogram). The computation of the image from the sinogram is an ill-posed inverse problem, which becomes under-determined when we are only able to collect insufficiently many X-Ray measurements. We are here interested in solving X-ray tomography image reconstruction problems where we are unable to scan the object from all directions, but where we have prior information about the object's shape. We thus propose a method that reduces image artefacts due to limited tomographic measurements by inferring missing measurements using shape priors. Our method uses a Generative Adversarial Network that combines limited acquisition data and shape information. While most existing methods focus on evenly spaced missing scanning angles, we propose an approach that infers a substantial number of consecutive missing acquisitions. We show that our method consistently improves image quality compared to images reconstructed using the previous state-of-the-art sinogram-inpainting techniques. In particular, we demonstrate a 7dB Peak Signal-to-Noise Ratio improvement compared to other methods
Generative Adversarial Networks for X-Ray Computed Tomography
Get PDFX-Ray computed tomography is a widely used, non-destructive imaging technique that produces cross-sectional images of bodies sensitive to X-Ray. Inter alia, it relies on exhaustive sampling of the attenuation properties of the scanned material and advanced reconstruction processes. However, acquisition can be toxic for humans or limiting for exotic geometries, as intense X-Ray exposure can lead to cancers during in-vivo diagnosis and experiments chambers have a fixed size that might limit the information gathering process for certain objects. Since sparse data from incomplete scans is yet to be compensated by adequate aftertreatment, we have decided to use deep-learning techniques to extract information on additional modalities to generate missing data in the acquisition.In many routine diagnoses, prior knowledge about the scanned object is often known. Whether it is computer-assisted design drawings or anatomical models, the availability of information regarding the shape of the test sample has led us to look for an acquisition process that minimises object sampling and maximises data harnessing on a known modality. After an introductory period of looking for the suitable architecture and publishing negative results, our exploration of deep generative models has led us to a unique design, one that combines unsupervised feature extraction with graphical models, use of these features for image generation with likelihood-free networks and a constrained optimisation problem to generate high-resolution acquisitions. This model translates our optimal understanding of the problem and an initial analysis suggests the feasibility of our process. Should the concept be promising, many challenges are yet to be addressed: accurate database constitution, efficient training items generation, thorough hyperparameters optimisation and delicate experimentations. As such, these are the next milestones in this investigation. Over the course of the next year, we are determined to deliver a method that is not only novel, but useful to many research fields.DSTL/DGA joint schem
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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