Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Deep learning based methods for decomposition in spectral computed tomography : Application to knee osteoarthritis

La tomographie spectrale est une nouvelle modalité d'imagerie à rayons X qui permet d'acquérir des données avec une dimension énergétique. Cela est possible grâce aux détecteurs à comptage de photons qui classent les photons en fonction de leur énergie. En exploitant cette dimension énergétique nous pouvons estimer les matériaux constituant l'objet ou reconstruire des images mono-énergétiques. Dans le domaine médical, la décomposition de matériaux est souvent opérée sur des bases de matériaux anatomiques tels que les tissus mous et l'os, combinés à des produits de contraste. D'un point de vue mathématique, la décomposition en matériaux est un problème inverse non linéaire et mal posé. De nombreuses méthodes de décomposition de matériaux ont été développées. Certaines sont basées sur l'inversion d'un modèle physique prenant en compte la source, la réponse du détecteur et l'atténuation théorique des matériaux constituant l'objet. Toutefois, les algorithmes d'apprentissage profond peuvent également être utilisés pour résoudre des problèmes inverses. Des travaux récents en tomographie ont montré que ceux-ci peuvent améliorer les qualités des reconstructions et sont plus rapides que les algorithmes basés sur le modèle. Le but de la thèse est de développer des algorithmes d'apprentissage profond pour la décomposition de matériaux et la reconstruction d'images mono-énergétiques en tomographie spectrale et de les évaluer par rapport à des méthodes basées sur des techniques d'optimisation. Nous nous sommes particulièrement focalisés sur l'application de la tomographie spectrale à l'arthrose du genou sans produit de contraste. Des genoux humains excisés ont été scannés à l'aide d'un prototype de scanner spectral mais aussi par tomographie synchrotron mono-énergétique afin d'avoir une image de référence. Des fantômes de genoux ont été générés à partir de ces volumes synchrotron en segmentant les matériaux tels que les tissus mous, l'os et potentiellement le cartilage. Puis, les projections spectrales sont simulées en utilisant des modèles de scanners spectraux. Nous avons ensuite développé un algorithme d'apprentissage profond basé sur U-net afin d'effectuer la décomposition de matériaux (tissus mous et os) dans le domaine des projections. Les résultats sont comparés à un algorithme de décomposition itératif basé sur le modèle, Gauss-Newton régularisé. Nous reconstruisons également les images mono-énergétiques soit grâce à la décomposition de matériaux, soit directement grâce à une adaptation de la méthode d'apprentissage. Les méthodes sont comparées en calculant l'erreur quadratique moyenne et l'indice de similarité. Finalement, les méthodes basées sur le modèle et d'apprentissage sont appliquées sur des données réelles issues du prototype clinique de scanner spectral.Spectral computed tomography is an emerging modality of X ray imaging that allows to acquire energy resolved data thanks to photon counting detectors. They are able to sort photons according to their energy. This extra energy dimension allows to decompose the objet into its material constituents or to reconstruct virtual mono-energetic images. In the medical field, material decomposition is often based on anatomic materials like soft tissue and bone, combined with contast agents. From a mathematical point of view, material decomposition is an ill posed non linear inverse problem. Several material decomposition methods have been developped. Ones are based on the inversion of a physical model taking into account the source spectrum, detector reponse function and theoritical material constituents' attenuation. However, a lot of deep learning algorithms can be used to solve inverse problems. Recent studies in tomography show that they can improve reconstructions accuracy and are faster than the model-based one. The goal of this thesis is developping deep learning algorithms for material decomposition and mono-energetic images reconstruction and evaluating them with respect to methods based on optimization. In particular, we focus on the application of spectral computed tomography to knee osteoarthritis without contrast agent. Excised human knees are scanned with a spectral computed tomography scanner but also with synchrotron mono-energetic computed tomography to have a gold standard. Phantoms are generated from this synchrotron computed tomography volumes, segmenting materials as soft tissue, bone and potentially cartilage. Then, spectral projections are simulated using spectral computed tomography scanners models. After, we developped a learning algorithm based on U-net to decompose materials (soft tissue and bone) in the projection domain. Results are compared with a model-based iterative decomposition algorithm, regularized Gauss-Newton. We also reconstruct mono-energetic images thanks to decomposed material maps, or directly with an adaptation of the learning method. We compare the methods computing the mean squared error and the structural similarity matrix. Finally, model-based and learning-based methods are applied to real data from the spectral computed tomography clinic prototype

Deep learning methods for virtual monoenergetic imaging from spectral CT

International audienceSpectral photon counting computed tomography (CT) is a X-ray imaging modality that acquires energy-resolved data thanks to photon counting detectors that sort photons depending on their energy. This allows to decompose the object into its material constituents or to reconstruct virtual monoenergetic images. In this paper, we address for the first time the reconstruction of virtual monoenergetic images from spectral CT measurements, which is a non linear inverse problem, focusing on the application to knee osteoarthrisis. While traditional methods are based on the inversion of a physical model, deep learning methods have recently demonstrated their ability to solve inverse problems. In this work, we propose several physics-informed deep learning strategies for virtual monoenergetic image reconstruction. We consider four different reconstruction algorithms for the recovery of virtual monoenergetic images in the projection and in the image domain. All of our algorithms include a variant of the U-net convolutional neural network. The proposed algorithms were trained and evaluated on the spectral CT data simulated from realistic knee phantoms generated from synchrotron radiation CT. They were also compared to a Gauss-Newton algorithm that minimized a cost function with a hand-crafted regularization term. Finally, our algorithms were applied to an experimental knee data set acquired on a clinical spectral CT scanner. We found that the proposed approaches provide virtual monoenergetic images with improved mean squared errors and structural similarities, compared to the Gauss-Newton method. Moreover, the image-domain network improved the mean squared error by a factor of two, compared to the projection-domain network. In both simulated and experimental data of osteoarthritis knees, we found that the cartilage was visible with naked eye on the virtual monoenergetic images reconstructed by our methods. The proposed deep learning networks outperformed the Gauss-Newton algorithm in the projection domain. Among deep reconstruction strategies, we found that the image-domain direct virtual monoenergetic reconstruction performs the best. They also allow for the direct visualization of the cartilage, which is essential for the assessment of cartilage integrity

Convolutional neural network for material decomposition in spectral CT scans

Abstract Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain. In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10⁵ photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Radionuclide Transport Simulations Supporting Proposed Borehole Waste Disposal in Israel

A scientific collaboration between the U.S. and Israel is underway to assess the suitability of a potential site for subsurface radioactive waste disposal in the Negev Desert, Israel. The Negev Desert has several favorable attributes for geologic disposal, including an arid climate, a deep vadose zone, interlayered low-permeability lithologies, and carbonate rocks with high uranium-sorption potential. These features may provide a robust natural barrier to radionuclide migration. Geologic and laboratory characterization data from the Negev Desert are incorporated into multiphase flow and transport models, solved using PFLOTRAN, to aid in site characterization and risk analysis that will support decision-making for waste disposal in an intermediate-depth borehole design. The lithology with the greatest uranium sorption potential at the site is phosphorite. We use modeling to evaluate the ability of this layer to impact uranium transport around a proposed disposal borehole. The current objective of the simulations is focused on characterizing hypothetical leakage from waste canisters and subsequent uranium migration under three infiltration scenarios. Here, we describe a hydrogeologic model based on data from a local exploratory borehole and present results for uranium flow and transport simulations under varying infiltration scenarios. We find that under the current climate conditions, it is likely that uranium will remain in the near-field of the borehole for thousands of years. However, under a hypothesized extreme climate scenario representing an increase in infiltration by a factor of 300x above present-day values, uranium may break through the phosphorite layer and exit the base of the model domain (~200 m above the water table) within 1000 years. Simulation results have direct implications for the planning of nuclear waste disposal in the Negev Desert, and specifically in intermediate-depth boreholes

Material decomposition in spectral CT using deep learning:a Sim2Real transfer approach

Abstract The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomography is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specific materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. We compared projection- and image-based decomposition approaches where the network is trained to decompose the materials either in the projection or in the image domain. The proposed Sim2Real transfer strategies are compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data