Cardiac C-arm computed tomography

Un C-arm est un appareil d’imagerie médicale par rayons X utilisé en radiologie interventionnelle. La plupart des C-arms modernes sont capables de tourner autour du patient tout en acquérant des images radiographiques, à partir desquelles une reconstruction 3D peut être effectuée. Cette technique est appelée angiographie rotationnelle et est déjà utilisée dans certains centres hospitaliers pour l’imagerie des organes statiques. Cependant son extension à l’imagerie du cœur ou du thorax en respiration libre demeure un défi pour la recherche. Cette thèse a pour objet l’angiographie rotationnelle pour l’analyse du myocarde chez l’homme. Plusieurs méthodes nouvelles y sont proposées et comparées à l’état de l’art, sur des données synthétiques et des données réelles. La première de ces méthodes, la déconvolution par FDK itérative synchronisée à l’ECG, consiste à effacer les artéfacts de stries dans une reconstruction FDK synchronisée à l’ECG par déconvolution. Elle permet d’obtenir de meilleurs résultats que les méthodes existantes basées sur la déconvolution, mais reste insuffisante pour l’angiographie rotationnelle cardiaque chez l’homme. Deux méthodes de reconstruction 3D basées sur l’échantillonnage compressé sont proposées : la reconstruction 3D régularisée par variation totale, et la reconstruction 3D régularisée par ondelettes. Elles sont comparées à la méthode qui constitue l’état de l’art actuel, appelé « Prior Image Constrained Compressed Sensing » (PICCS). Elles permettent d’obtenir des résultats similaires à ceux de PICCS. Enfin, deux méthodes de reconstruction 3D+temps sont présentées. Leurs formulations mathématiques sont légèrement différentes l’une de l’autre, mais elles s’appuient sur les mêmes principes : utiliser un masque pour restreindre le mouvement à la région contenant le cœur et l’aorte, et imposer une solution régulière dans l’espace et dans le temps. L’une de ces méthodes génère des résultats meilleurs, c’est-à-dire à la fois plus nets et plus cohérents dans le temps, que ceux de PICCSA C-arm is an X-ray imaging device used for minimally invasive interventional radiology procedures. Most modern C-arm systems are capable of rotating around the patient while acquiring radiographic images, from which a 3D reconstruction can be performed. This technique is called C-arm computed tomography (C-arm CT) and is used in clinical routine to image static organs. However, its extension to imaging of the beating heart or the free-breathing thorax is still a challenging research problem. This thesis is focused on human cardiac C-arm CT. It proposes several new reconstruction methods and compares them to the current state or the art, both on a digital phantom and on real data acquired on several patients. The first method, ECG-gated Iterative FDK deconvolution, consists in filtering out the streak artifacts from an ECG-gated FDK reconstruction in an iterative deconvolution scheme. It performs better than existing deconvolution-based methods, but it is still insufficient for human cardiac C-arm CT. Two 3D reconstruction methods based on compressed sensing are proposed: total variation-regularized 3D reconstruction and wavelets-regularized 3D reconstruction. They are compared to the current state-of-the-art method, called prior image constrained compressed sensing (PICCS). They exhibit results that are similar to those of PICCS. Finally, two 3D+time reconstruction methods are presented. They have slightly different mathematical formulations but are based on the same principles: using a motion mask to restrict the movement to the area containing the heart and the aorta, and enforcing smoothness of the solution in both space and time. One of these methods outperforms PICCS by producing results that are sharper and more consistent throughout the cardiac cycl

Cardiac C-arm computed tomography

Un C-arm est un appareil d’imagerie médicale par rayons X utilisé en radiologie interventionnelle. La plupart des C-arms modernes sont capables de tourner autour du patient tout en acquérant des images radiographiques, à partir desquelles une reconstruction 3D peut être effectuée. Cette technique est appelée angiographie rotationnelle et est déjà utilisée dans certains centres hospitaliers pour l’imagerie des organes statiques. Cependant son extension à l’imagerie du cœur ou du thorax en respiration libre demeure un défi pour la recherche. Cette thèse a pour objet l’angiographie rotationnelle pour l’analyse du myocarde chez l’homme. Plusieurs méthodes nouvelles y sont proposées et comparées à l’état de l’art, sur des données synthétiques et des données réelles. La première de ces méthodes, la déconvolution par FDK itérative synchronisée à l’ECG, consiste à effacer les artéfacts de stries dans une reconstruction FDK synchronisée à l’ECG par déconvolution. Elle permet d’obtenir de meilleurs résultats que les méthodes existantes basées sur la déconvolution, mais reste insuffisante pour l’angiographie rotationnelle cardiaque chez l’homme. Deux méthodes de reconstruction 3D basées sur l’échantillonnage compressé sont proposées : la reconstruction 3D régularisée par variation totale, et la reconstruction 3D régularisée par ondelettes. Elles sont comparées à la méthode qui constitue l’état de l’art actuel, appelé « Prior Image Constrained Compressed Sensing » (PICCS). Elles permettent d’obtenir des résultats similaires à ceux de PICCS. Enfin, deux méthodes de reconstruction 3D+temps sont présentées. Leurs formulations mathématiques sont légèrement différentes l’une de l’autre, mais elles s’appuient sur les mêmes principes : utiliser un masque pour restreindre le mouvement à la région contenant le cœur et l’aorte, et imposer une solution régulière dans l’espace et dans le temps. L’une de ces méthodes génère des résultats meilleurs, c’est-à-dire à la fois plus nets et plus cohérents dans le temps, que ceux de PICCSA C-arm is an X-ray imaging device used for minimally invasive interventional radiology procedures. Most modern C-arm systems are capable of rotating around the patient while acquiring radiographic images, from which a 3D reconstruction can be performed. This technique is called C-arm computed tomography (C-arm CT) and is used in clinical routine to image static organs. However, its extension to imaging of the beating heart or the free-breathing thorax is still a challenging research problem. This thesis is focused on human cardiac C-arm CT. It proposes several new reconstruction methods and compares them to the current state or the art, both on a digital phantom and on real data acquired on several patients. The first method, ECG-gated Iterative FDK deconvolution, consists in filtering out the streak artifacts from an ECG-gated FDK reconstruction in an iterative deconvolution scheme. It performs better than existing deconvolution-based methods, but it is still insufficient for human cardiac C-arm CT. Two 3D reconstruction methods based on compressed sensing are proposed: total variation-regularized 3D reconstruction and wavelets-regularized 3D reconstruction. They are compared to the current state-of-the-art method, called prior image constrained compressed sensing (PICCS). They exhibit results that are similar to those of PICCS. Finally, two 3D+time reconstruction methods are presented. They have slightly different mathematical formulations but are based on the same principles: using a motion mask to restrict the movement to the area containing the heart and the aorta, and enforcing smoothness of the solution in both space and time. One of these methods outperforms PICCS by producing results that are sharper and more consistent throughout the cardiac cycl

Tomographie cardiaque en angiographie rotationnelle

A C-arm is an X-ray imaging device used for minimally invasive interventional radiology procedures. Most modern C-arm systems are capable of rotating around the patient while acquiring radiographic images, from which a 3D reconstruction can be performed. This technique is called C-arm computed tomography (C-arm CT) and is used in clinical routine to image static organs. However, its extension to imaging of the beating heart or the free-breathing thorax is still a challenging research problem. This thesis is focused on human cardiac C-arm CT. It proposes several new reconstruction methods and compares them to the current state or the art, both on a digital phantom and on real data acquired on several patients. The first method, ECG-gated Iterative FDK deconvolution, consists in filtering out the streak artifacts from an ECG-gated FDK reconstruction in an iterative deconvolution scheme. It performs better than existing deconvolution-based methods, but it is still insufficient for human cardiac C-arm CT. Two 3D reconstruction methods based on compressed sensing are proposed: total variation-regularized 3D reconstruction and wavelets-regularized 3D reconstruction. They are compared to the current state-of-the-art method, called prior image constrained compressed sensing (PICCS). They exhibit results that are similar to those of PICCS. Finally, two 3D+time reconstruction methods are presented. They have slightly different mathematical formulations but are based on the same principles: using a motion mask to restrict the movement to the area containing the heart and the aorta, and enforcing smoothness of the solution in both space and time. One of these methods outperforms PICCS by producing results that are sharper and more consistent throughout the cardiac cycleUn C-arm est un appareil d’imagerie médicale par rayons X utilisé en radiologie interventionnelle. La plupart des C-arms modernes sont capables de tourner autour du patient tout en acquérant des images radiographiques, à partir desquelles une reconstruction 3D peut être effectuée. Cette technique est appelée angiographie rotationnelle et est déjà utilisée dans certains centres hospitaliers pour l’imagerie des organes statiques. Cependant son extension à l’imagerie du cœur ou du thorax en respiration libre demeure un défi pour la recherche. Cette thèse a pour objet l’angiographie rotationnelle pour l’analyse du myocarde chez l’homme. Plusieurs méthodes nouvelles y sont proposées et comparées à l’état de l’art, sur des données synthétiques et des données réelles. La première de ces méthodes, la déconvolution par FDK itérative synchronisée à l’ECG, consiste à effacer les artéfacts de stries dans une reconstruction FDK synchronisée à l’ECG par déconvolution. Elle permet d’obtenir de meilleurs résultats que les méthodes existantes basées sur la déconvolution, mais reste insuffisante pour l’angiographie rotationnelle cardiaque chez l’homme. Deux méthodes de reconstruction 3D basées sur l’échantillonnage compressé sont proposées : la reconstruction 3D régularisée par variation totale, et la reconstruction 3D régularisée par ondelettes. Elles sont comparées à la méthode qui constitue l’état de l’art actuel, appelé « Prior Image Constrained Compressed Sensing » (PICCS). Elles permettent d’obtenir des résultats similaires à ceux de PICCS. Enfin, deux méthodes de reconstruction 3D+temps sont présentées. Leurs formulations mathématiques sont légèrement différentes l’une de l’autre, mais elles s’appuient sur les mêmes principes : utiliser un masque pour restreindre le mouvement à la région contenant le cœur et l’aorte, et imposer une solution régulière dans l’espace et dans le temps. L’une de ces méthodes génère des résultats meilleurs, c’est-à-dire à la fois plus nets et plus cohérents dans le temps, que ceux de PICC

Kullback-Leibler residual and regularization for inverse problems with noisy data and noisy operator

International audienceWe study the properties of a regularization method for inverse problems with joint Kullback-Leibler data term and regularization when the data and the operator are corrupted by some noise. We show the convergence of the method and we obtain convergence rates for the approximate solution of the inverse problem and for the operator when it is characterized by some kernel, under the assumption that some source conditions are satisfied. Numerical results showing the effect of the noise levels on the reconstructed solution are provided for Spectral Computerized Tomography

Improving iterative 4D CBCT through the use of motion information

International audienceIn Image-Guided RadioTherapy (IGRT) of lung tumors, patients undergo a 4D CT, on the basis of which their treatment is planned. It is implicitely assumed that their breathing motion will not change much throughout the treatment, and remain close to what it was during the 4D CT acquisition. During the treatment, several cone beam CT acquisitions are performed, and used to re-position the patient. Obtaining a 4D reconstruction from this cone beam data would allow the therapists to check whether the breathing motion of the day still matches that of the planning CT, and if not, take appropriate corrective actions. Unfortunately, most tomography methods currently available are inadequate for such a task: static 3D reconstructions are pointless for motion assessment, respirationcorrelated reconstructions are affected by streak artifacts, and regularization techniques only bring limited improvement. Recently, regularized 4D methods have been proposed, in which the whole respiratory cycle is reconstructed at once. As these methods allow to explicitely enforce similarity between consecutive frames, they considerably improve image quality. In the case of IGRT, the motion information extracted from the 4D planning CT can be used to further improve the 4D reconstruction results. We describe a recent 4D reconstruction method (ROOSTER), propose its motion-compensated counterpart (MC-ROOSTER), and compare their results

4D cone-beam computed tomography using motion-aware regularization

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).National audienceIn Image-Guided RadioTherapy (IGRT) of lung tumors, patients undergo a 4D CT, on the basis of which their treatment is planned. It is implicitely assumed that their breathing motion will not change much throughout the treatment. At the beginning of a treatment fraction, a Cone Beam CT (CBCT) acquisition is performed, and used to re-position the patient. Obtaining a 4D reconstruction from this cone beam data would allow the therapists to check whether the breathing motion of the day still matches that of the planning CT, and if not, take appropriate corrective actions. But 4D tomog-raphy from a single cone beam CT acquisition implies a severe lack of projection data, and efficient methods have only started to appear during the last few years. Some perform regularization along time to explicitely enforce similarity between consecutive frames, which considerably improves image quality. In IGRT, breathing motion can be estimated on the 4D planning CT, and used to refine the 4D CBCT reconstruction results. We describe a 4D CBCT reconstruction method that combines regular-ization techniques with the use of motion information

4D cone-beam computed tomography using motion-aware regularization

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).National audienceIn Image-Guided RadioTherapy (IGRT) of lung tumors, patients undergo a 4D CT, on the basis of which their treatment is planned. It is implicitely assumed that their breathing motion will not change much throughout the treatment. At the beginning of a treatment fraction, a Cone Beam CT (CBCT) acquisition is performed, and used to re-position the patient. Obtaining a 4D reconstruction from this cone beam data would allow the therapists to check whether the breathing motion of the day still matches that of the planning CT, and if not, take appropriate corrective actions. But 4D tomog-raphy from a single cone beam CT acquisition implies a severe lack of projection data, and efficient methods have only started to appear during the last few years. Some perform regularization along time to explicitely enforce similarity between consecutive frames, which considerably improves image quality. In IGRT, breathing motion can be estimated on the 4D planning CT, and used to refine the 4D CBCT reconstruction results. We describe a 4D CBCT reconstruction method that combines regular-ization techniques with the use of motion information

Image Formation in Spectral Computed Tomography

International audienc

Motion-aware temporal regularization for improved 4D cone-beam computed tomography

Four-dimensional cone-beam computed tomography (4D-CBCT) of the free-breathing thorax is a valuable tool in image-guided radiation therapy of the thorax and the upper abdomen. It allows the determination of the position of a tumor throughout the breathing cycle, while only its mean position can be extracted from three-dimensional CBCT. The classical approaches are not fully satisfactory: respiration-correlated methods allow one to accurately locate high-contrast structures in any frame, but contain strong streak artifacts unless the acquisition is significantly slowed down. Motion-compensated methods can yield streak-free, but static, reconstructions. This work proposes a 4D-CBCT method that can be seen as a trade-off between respiration-correlated and motion-compensated reconstruction. It builds upon the existing reconstruction using spatial and temporal regularization (ROOSTER) and is called motion-aware ROOSTER (MA-ROOSTER). It performs temporal regularization along curved trajectories, following the motion estimated on a prior 4D CT scan. MA-ROOSTER does not involve motion-compensated forward and back projections: the input motion is used only during temporal regularization. MA-ROOSTER is compared to ROOSTER, motion-compensated Feldkamp-Davis-Kress (MC-FDK), and two respiration-correlated methods, on CBCT acquisitions of one physical phantom and two patients. It yields streak-free reconstructions, visually similar to MC-FDK, and robust information on tumor location throughout the breathing cycle. MA-ROOSTER also allows a variation of the lung tissue density during the breathing cycle, similar to that of planning CT, which is required for quantitative post-processing

Comparison of five one-step reconstruction algorithms for spectral CT

International audienceOver the last decade, dual-energy CT scanners have gone from prototypes to clinically available machines, and spectral photon counting CT scanners are following. They require a specific reconstruction process, consisting of two steps: material decomposition and tomographic reconstruction. The two steps can be done 5 separately in either order, but in both cases, some information is lost along the way. As an alternative, "one-step inversion" methods have been proposed, which perform decomposition and reconstruction simultaneously. For most CT applications, reconstruction time is critical for practical usability, and one-step methods are typically 10 slower than their two-step counterparts. The goal of this paper is to provide an independent comparison of five one-step inversion algorithms, focused mainly on convergence speed, but also on memory footprint, stability, and ease of use. We adapted and implemented a Bayesian method which uses non-linear conjugate 15 gradient for minimization [1], three methods based on quadratic surrogates [2, 3, 4], and a primal-dual method based on MOCCA, a modified Chambolle-Pock algorithm [5]. Experiments were performed on both simulated and real data. Some of these methods can be accelerated by using µ-preconditioning, i.e. by performing 20 all internal computations not with the actual materials the object is made of, but with carefully chosen linear combinations of those. In this paper, we also evaluate the impact of three different µ-preconditioners on convergence speed. Our results show that the method of Mechlem et al. [4] is much faster than the others, while 25 being only slightly less stable and more complex: it requires less than 100 iterations, versus several thousands for other methods. It seems to be the only viable candidate for implementation into a real multi-energy scanner