Méthodes statistiques de reconstruction tomographique spectrale pour des systèmes à détection spectrométrique de rayons X

X-ray spectral tomography is a 3D visualization technique. It is based on the transmission of X-rays through object matter. It is a non-destructive technology but which irradiates the studied object/patient. X-ray tomography is mainly used in three areas: medical diagnosis, non-destructive testing (detection of defects in industry devices) and airport security (luggage screening). New technological breakthroughs in X-ray photon-counting detectors provide new perspective for improving this technique in each application field. We have developed a new reconstruction method named MLTR-ONE-STEP which enables the obtention of energetic variability of the scanned object linear attenuation coefficient. This approach belongs to the “One-Step” class because it directly reconstructs the final images from raw photon-counting detector data.Physical effects inside the detector are causing spectral distortion of the energetic spectrum. This distortion is taken into account in our reconstruction through a Detector Response Matrix. The developed reconstruction method maximizes the poissonian likelihood of the measurements with a spatial regularization Tukey term. The objectives of spectral tomography are the improvement of the image quality compared to standard tomography and the quantification of materials inside the object. We have studied the influence of regularization parameters on the final result. In practice, photon-counting detector measurements are in practice sorted in 64 energy bins. Bins are then merged in a smaller number (from 2 to 25). The influence of this binning was studied on simulated data. The MLTR-ONE-STEP was then tested on real experimental data in order to prove the feasibility of such a “One-Step” reconstruction method.La tomographie à rayons X est une technologie d’imagerie en trois dimensions. Elle se base sur la transmission de rayons X à travers l’objet d’étude. Elle est non destructive mais néanmoins irradiante. Cette technique de visualisation est utilisée principalement dans trois domaines : le diagnostic médical, le contrôle non destructif (détection de défauts dans des pièces industrielles de haute performance) et la sécurité (contrôles aéroportuaires des bagages). Les récentes avancées technologiques dans le domaine des détecteurs spectrométriques de rayons X ouvrent des perspectives d’amélioration de cette technique d’imagerie dans ses divers domaines d’application. Nous avons développé une nouvelle méthode reconstruction statistique appelée MLTR-ONE-STEP qui permet de reconstruire la variabilité énergétique du coefficient linéaire d’atténuation de l’objet étudié. Cette approche est dite « one-step » car elle reconstruit directement le volume final à partir des mesures brutes issues de détecteurs spectrométriques.Les phénomènes physiques au sein du détecteur provoquent une distorsion énergétique du spectre d’atténuation qui a été prise en compte lors de la reconstruction. La méthode utilisée s’inscrit dans le cadre bayésien et maximise la log-vraisemblance du modèle tout en prenant en compte de l’a priori spatial sur le volume reconstruit. L’objectif de la méthode est l’amélioration de la qualité de l’image finale (réduction des artefacts et niveau de bruit) et la quantification des matériaux présents. Nous avons étudié dans le cadre de données simulées l’influence des paramètres de régularisation sur la reconstruction. En pratique, le détecteur de rayon X étudié classe les photons incidents en 64 canaux. Ils sont ensuite regroupés en un nombre de canaux plus faible (2 à 25) et l’influence de ce regroupement a été étudiée. La reconstruction MLTR-ONE-STEP a ensuite été testée sur des données expérimentales regroupées en 12 canaux

Spectral CT statistical reconstruction methods for X-ray photon-counting detectors system

La tomographie à rayons X est une technologie d’imagerie en trois dimensions. Elle se base sur la transmission de rayons X à travers l’objet d’étude. Elle est non destructive mais néanmoins irradiante. Cette technique de visualisation est utilisée principalement dans trois domaines : le diagnostic médical, le contrôle non destructif (détection de défauts dans des pièces industrielles de haute performance) et la sécurité (contrôles aéroportuaires des bagages). Les récentes avancées technologiques dans le domaine des détecteurs spectrométriques de rayons X ouvrent des perspectives d’amélioration de cette technique d’imagerie dans ses divers domaines d’application. Nous avons développé une nouvelle méthode reconstruction statistique appelée MLTR-ONE-STEP qui permet de reconstruire la variabilité énergétique du coefficient linéaire d’atténuation de l’objet étudié. Cette approche est dite « one-step » car elle reconstruit directement le volume final à partir des mesures brutes issues de détecteurs spectrométriques.Les phénomènes physiques au sein du détecteur provoquent une distorsion énergétique du spectre d’atténuation qui a été prise en compte lors de la reconstruction. La méthode utilisée s’inscrit dans le cadre bayésien et maximise la log-vraisemblance du modèle tout en prenant en compte de l’a priori spatial sur le volume reconstruit. L’objectif de la méthode est l’amélioration de la qualité de l’image finale (réduction des artefacts et niveau de bruit) et la quantification des matériaux présents. Nous avons étudié dans le cadre de données simulées l’influence des paramètres de régularisation sur la reconstruction. En pratique, le détecteur de rayon X étudié classe les photons incidents en 64 canaux. Ils sont ensuite regroupés en un nombre de canaux plus faible (2 à 25) et l’influence de ce regroupement a été étudiée. La reconstruction MLTR-ONE-STEP a ensuite été testée sur des données expérimentales regroupées en 12 canaux.X-ray spectral tomography is a 3D visualization technique. It is based on the transmission of X-rays through object matter. It is a non-destructive technology but which irradiates the studied object/patient. X-ray tomography is mainly used in three areas: medical diagnosis, non-destructive testing (detection of defects in industry devices) and airport security (luggage screening). New technological breakthroughs in X-ray photon-counting detectors provide new perspective for improving this technique in each application field. We have developed a new reconstruction method named MLTR-ONE-STEP which enables the obtention of energetic variability of the scanned object linear attenuation coefficient. This approach belongs to the “One-Step” class because it directly reconstructs the final images from raw photon-counting detector data.Physical effects inside the detector are causing spectral distortion of the energetic spectrum. This distortion is taken into account in our reconstruction through a Detector Response Matrix. The developed reconstruction method maximizes the poissonian likelihood of the measurements with a spatial regularization Tukey term. The objectives of spectral tomography are the improvement of the image quality compared to standard tomography and the quantification of materials inside the object. We have studied the influence of regularization parameters on the final result. In practice, photon-counting detector measurements are in practice sorted in 64 energy bins. Bins are then merged in a smaller number (from 2 to 25). The influence of this binning was studied on simulated data. The MLTR-ONE-STEP was then tested on real experimental data in order to prove the feasibility of such a “One-Step” reconstruction method

Spatially varying regularization weights for one-step spectral CT with SQS

International audiencePhoton-counting detectors provide spectral information for x-ray acquisitions. Taking advantage of this information currently requires iterative algorithms to reconstruct basis material CT images. One-step reconstruction is the simultaneous inversion of the spectral distortion occurring in the detector and the geometrical projection. Separable quadratic surrogate (SQS) algorithms have been applied to this one-step problem with satisfactory convergence and material separation. However, this class of method leads to numerical instabilities stemming from voxels out of the field-of-view (FOV) which need to be included in the forward model for reconstructing the FOV. We aim at improving one-step spectral CT reconstruction by investigating two possible corrections of this effect: replacing the exponential in the forward model by a linear function for negative attenuations and spatially varying regularization depending on the geometrical conditioning. We demonstrate the efficiency of the second method using experimental data acquired on a clinical prototype CT scanner with a photon-counting detector

Spectral CT reconstruction with an explicit photon-counting detector model: a " one-step " approach

International audienceRecent developments in energy-discriminating Photon-Counting Detector (PCD) enable new horizons for spectral CT. With PCDs, new reconstruction methods take advantage of the spectral information measured through energy measurement bins. However PCDs have serious spectral distortion issues due to charge-sharing, fluorescence escape, pileup effect… Spectral CT with PCDs can be decomposed into two problems: a noisy geometric inversion problem (as in standard CT) and an additional PCD spectral degradation problem. The aim of the present study is to introduce a reconstruction method which solves both problems simultaneously: a " one-step " approach. An explicit linear detector model is used and characterized by a Detector Response Matrix (DRM). The algorithm reconstructs two basis material maps from energy-window transmission data. The results prove that the simultaneous inversion of both problems is well performed for simulation data. For comparison, we also perform a standard " two-step " approach: an advanced polynomial decomposition of measured sinograms combined with a filtered-back projection reconstruction. The results demonstrate the potential uses of this method for medical imaging or for non-destructive control in industry

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

International audienceAbstract Objective . X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach . MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results . The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance . Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images

Spectral Photon-Counting CT Technology in Chest Imaging

International audienceThe X-ray imaging field is currently undergoing a period of rapid technological innovation in diagnostic imaging equipment. An important recent development is the advent of new X-ray detectors, i.e., photon-counting detectors (PCD), which have been introduced in recent clinical prototype systems, called PCD computed tomography (PCD-CT) or photon-counting CT (PCCT) or spectral photon-counting CT (SPCCT) systems. PCD allows a pixel up to 200 microns pixels at iso-center, which is much smaller than that can be obtained with conventional energy integrating detectors (EID). PCDs have also a higher dose efficiency than EID mainly because of electronic noise suppression. In addition, the energy-resolving capabilities of these detectors allow generating spectral basis imaging, such as the mono-energetic images or the water/iodine material images as well as the K-edge imaging of a contrast agent based on atoms of high atomic number. In recent years, studies have therefore been conducted to determine the potential of PCD-CT as an alternative to conventional CT for chest imaging

Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning

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Feasibility of lung imaging with a large field-of-view spectral photon-counting CT system

International audienceAbstract Aims In low-gradient aortic stenosis (LGAS), the high valvulo-arterial impedance observed despite low valvular gradient suggests a high vascular load. Thoracic aortic calcifications (TACs) and valvular aortic calcifications (VACs) are, respectively, surrogates of aortic load and aortic valvular gradient. The aim of this study was to compare the respective contributions of TAC and VAC on 3-year cardiovascular (CV) mortality following TAVI in LGAS vs. high-gradient aortic stenosis (HGAS) patients. Methods and results A total of 1396 consecutive patients were included. TAC and VAC were measured on the pre-TAVI CT-scan. About 435 (31.2%) patients had LGAS and 961 (68.8%) HGAS. LGAS patients were more prone to have diabetes, coronary artery disease (CAD), atrial fibrillation (AF), and lower left ventricular ejection fraction (LVEF), P<0.05 for all. During the 3 years after TAVI, 245(17.8%) patients experienced CV mortality, 92(21.6%) in LGAS and 153(16.2%) in HGAS patients, P=0.018. Multivariate analysis adjusted for age, gender, diabetes, AF, CAD, LVEF, renal function, vascular access, and aortic regurgitation showed that TAC but not VAC was associated with CV mortality in LGAS, hazard ratio (HR) 1.085 confidence interval (CI) (1.019–1.156), P=0.011, and HR 0.713 CI (0.439–1.8), P=0.235; the opposite was observed in HGAS patients with VAC but not TAC being associated with CV mortality, HR 1.342 CI (1.034–1.742), P=0.027, and HR 1.015 CI (0.955–1.079), P=0.626. Conclusion TAC plays a major prognostic role in LGAS while VAC remains the key in HGAS patients. This confirms that LGAS is a complex vascular and valvular disease

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

First In-Human Results of Computed Tomography Angiography for Coronary Stent Assessment With a Spectral Photon Counting Computed Tomography

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