CUDA accelerated cone‐beam reconstruction

Cone-Beam Computed Tomography (CBCT) is an imaging method that reconstructs a 3D representation of the object from its 2D X-ray images. It is an important diagnostic tool in the medical field, especially dentistry. However, most 3D reconstruction algorithms are computationally intensive and time consuming; this limitation constrains the use of CBCT. In recent years, high-end graphics cards, such as the ones powered by NVIDIA graphics processing units (GPUs), are able to perform general purpose computation. Due to the highly parallel nature of the 3D reconstruction algorithms, it is possible to implement these algorithms on the GPU to reduce the processing time to the level that is practical. Two of the most popular 3D Cone-Beam reconstruction algorithms are the Feldkamp-Davis-Kress algorithm (FDK) and the Algebraic Reconstruction Technique (ART). FDK is fast to construct 3D images, but the quality of its images is lower than the quality of ART images. However, ART requires significantly more computation. Material ART is a recently developed algorithm that uses beam-hardening correction to eliminate artifacts. In this thesis, these three algorithms were implemented on the NVIDIA\u27s CUDA platform. These CUDA based algorithms were tested on three different graphics cards, using phantom and real data. The test results show significant speedup when compared to the CPU software implementation. The speedup is sufficient to allow a moderate cost personal computer with NVIDIA graphics card to process CBCT images in real-time

Exploiting parallelism in a X-ray tomography reconstruction algorithm on hybrid multi-GPU and multi-core platforms

Proceedings of: 2012 10 th IEEE International Symposium on Parallel and Distributes Processing with Applicatioons (ISPA 2012). Leganés, Madrid, 10-13 July 2012.Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. Currently there are a strong need to speed-up the reconstruction of XRay CT data in order to extend its clinical applications. We present an efficient modular implementation of an FDK-based reconstruction algorithm that takes advantage of the parallel computing capabilities and the efficient bilinear interpolation provided by general purpose graphic processing units (GPGPU). The proposed implementation of the algorithm is evaluated for a high-resolution micro-CT and achieves a speed-up of 46, while preserving the reconstructed image qualiThis work has been partially funded by AMIT Project CDTI CENIT, TEC2007-64731, TEC2008-06715-C02-01, RD07/0014/2009, TRA2009 0175, RECAVA-RETIC, RD09/0077/00087 (Ministerio de Ciencia e Innovacion), ARTEMIS S2009/DPI-1802 (Comunidad de Madrid), and TIN2010-16497 (Ministerio de Ciencia e Innovacion).Publicad

Surfing the optimization space of a multiple-GPU parallel implementation of a X-ray tomography reconstruction algorithm

The increasing popularity of massively parallel architectures based on accelerators have opened up the possibility of significantly improving the performance of X-ray computed tomography (CT) applications towards achieving real-time imaging. However, achieving this goal is a challenging process, as most CT applications have not been designed for exploiting the amount of parallelism existing in these architectures. In this paper we present the massively parallel implementation and optimization of Mangoose(++), a CT application for reconstructing 3D volumes from 20 images collected by scanners based on cone-beam geometry. The main contribution of this paper are the following. First, we develop a modular application design that allows to exploit the functional parallelism inside the application and to facilitate the parallelization of individual application phases. Second, we identify a set of optimizations that can be applied individually and in combination for optimally deploying the application on a massively parallel multi-GPU system. Third, we present a study of surfing the optimization space of the modularized application and demonstrate that a significant benefit can be obtained from employing the adequate combination of application optimizations. (C) 2014 Elsevier Inc. All rights reserved.This work was partially funded by the Spanish Ministry of Science and Technology under the grant TIN2010-16497, the AMIT project (CEN-20101014) from the CDTI-CENIT program, RECAVA-RETIC Network (RD07/0014/2009), projects TEC2010-21619-C04-01, TEC2011-28972-C02-01, and PI11/00616 from the Spanish Ministerio de Ciencia e Innovacion, ARTEMIS program (S2009/DPI-1802), from the Comunidad de Madrid

Novel high performance techniques for high definition computer aided tomography

Mención Internacional en el título de doctorMedical image processing is an interdisciplinary field in which multiple research areas are involved: image acquisition, scanner design, image reconstruction algorithms, visualization, etc. X-Ray Computed Tomography (CT) is a medical imaging modality based on the attenuation suffered by the X-rays as they pass through the body. Intrinsic differences in attenuation properties of bone, air, and soft tissue result in high-contrast images of anatomical structures. The main objective of CT is to obtain tomographic images from radiographs acquired using X-Ray scanners. The process of building a 3D image or volume from the 2D radiographs is known as reconstruction. One of the latest trends in CT is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artefacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative reconstruction algorithms. There are numerous reconstruction algorithms available, from which we can highlight two specific types: traditional algorithms, which are fast but do not enable the obtaining of high quality images in situations of limited data; and iterative algorithms, slower but more reliable when traditional methods do not reach the quality standard requirements. One of the priorities of reconstruction is the obtaining of the final images in near real time, in order to reduce the time spent in diagnosis. To accomplish this objective, new high performance techniques and methods for accelerating these types of algorithms are needed. This thesis addresses the challenges of both traditional and iterative reconstruction algorithms, regarding acceleration and image quality. One common approach for accelerating these algorithms is the usage of shared-memory and heterogeneous architectures. In this thesis, we propose a novel simulation/reconstruction framework, namely FUX-Sim. This framework follows the hypothesis that the development of new flexible X-ray systems can benefit from computer simulations, which may also enable performance to be checked before expensive real systems are implemented. Its modular design abstracts the complexities of programming for accelerated devices to facilitate the development and evaluation of the different configurations and geometries available. In order to obtain near real execution times, low-level optimizations for the main components of the framework are provided for Graphics Processing Unit (GPU) architectures. Other alternative tackled in this thesis is the acceleration of iterative reconstruction algorithms by using distributed memory architectures. We present a novel architecture that unifies the two most important computing paradigms for scientific computing nowadays: High Performance Computing (HPC). The proposed architecture combines Big Data frameworks with the advantages of accelerated computing. The proposed methods presented in this thesis provide more flexible scanner configurations as they offer an accelerated solution. Regarding performance, our approach is as competitive as the solutions found in the literature. Additionally, we demonstrate that our solution scales with the size of the problem, enabling the reconstruction of high resolution images.El procesamiento de imágenes médicas es un campo interdisciplinario en el que participan múltiples áreas de investigación como la adquisición de imágenes, diseño de escáneres, algoritmos de reconstrucción de imágenes, visualización, etc. La tomografía computarizada (TC) de rayos X es una modalidad de imágen médica basada en el cálculo de la atenuación sufrida por los rayos X a medida que pasan por el cuerpo a escanear. Las diferencias intrínsecas en la atenuación de hueso, aire y tejido blando dan como resultado imágenes de alto contraste de estas estructuras anatómicas. El objetivo principal de la TC es obtener imágenes tomográficas a partir estas radiografías obtenidas mediante escáneres de rayos X. El proceso de construir una imagen o volumen en 3D a partir de las radiografías 2D se conoce como reconstrucción. Una de las últimas tendencias en la tomografía computarizada es la reducción de la dosis de radiación administrada a los pacientes a través de la reducción de la cantidad de datos adquiridos. Esta reducción da como resultado artefactos en las imágenes finales si se utilizan métodos de reconstrucción convencionales, por lo que es aconsejable emplear algoritmos de reconstrucción iterativos. Existen numerosos algoritmos de reconstrucción disponibles a partir de los cuales podemos destacar dos categorías: algoritmos tradicionales, rápidos pero no permiten obtener imágenes de alta calidad en situaciones en las que los datos son limitados; y algoritmos iterativos, más lentos pero más estables en situaciones donde los métodos tradicionales no alcanzan los requisitos en cuanto a la calidad de la imagen. Una de las prioridades de la reconstrucción es la obtención de las imágenes finales en tiempo casi real, con el fin de reducir el tiempo de diagnóstico. Para lograr este objetivo, se necesitan nuevas técnicas y métodos de alto rendimiento para acelerar estos algoritmos. Esta tesis aborda los desafíos de los algoritmos de reconstrucción tradicionales e iterativos, con respecto a la aceleración y la calidad de imagen. Un enfoque común para acelerar estos algoritmos es el uso de arquitecturas de memoria compartida y heterogéneas. En esta tesis, proponemos un nuevo sistema de simulación/reconstrucción, llamado FUX-Sim. Este sistema se construye alrededor de la hipótesis de que el desarrollo de nuevos sistemas de rayos X flexibles puede beneficiarse de las simulaciones por computador, en los que también se puede realizar un control del rendimiento de los nuevos sistemas a desarrollar antes de su implementación física. Su diseño modular abstrae las complejidades de la programación para aceleradores con el objetivo de facilitar el desarrollo y la evaluación de las diferentes configuraciones y geometrías disponibles. Para obtener ejecuciones en casi tiempo real, se proporcionan optimizaciones de bajo nivel para los componentes principales del sistema en las arquitecturas GPU. Otra alternativa abordada en esta tesis es la aceleración de los algoritmos de reconstrucción iterativa mediante el uso de arquitecturas de memoria distribuidas. Presentamos una arquitectura novedosa que unifica los dos paradigmas informáticos más importantes en la actualidad: computación de alto rendimiento (HPC) y Big Data. La arquitectura propuesta combina sistemas Big Data con las ventajas de los dispositivos aceleradores. Los métodos propuestos presentados en esta tesis proporcionan configuraciones de escáner más flexibles y ofrecen una solución acelerada. En cuanto al rendimiento, nuestro enfoque es tan competitivo como las soluciones encontradas en la literatura. Además, demostramos que nuestra solución escala con el tamaño del problema, lo que permite la reconstrucción de imágenes de alta resolución.This work has been mainly funded thanks to a FPU fellowship (FPU14/03875) from the Spanish Ministry of Education. It has also been partially supported by other grants: • DPI2016-79075-R. “Nuevos escenarios de tomografía por rayos X”, from the Spanish Ministry of Economy and Competitiveness. • TIN2016-79637-P Towards unification of HPC and Big Data Paradigms from the Spanish Ministry of Economy and Competitiveness. • Short-term scientific missions (STSM) grant from NESUS COST Action IC1305. • TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness. • RTC-2014-3028-1 NECRA Nuevos escenarios clinicos con radiología avanzada from the Spanish Ministry of Economy and Competitiveness.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Daniel García Sánchez.- Secretario: Katzlin Olcoz Herrero.- Vocal: Domenico Tali

Real-time tomographic reconstruction

With tomography it is possible to reconstruct the interior of an object without destroying. It is an important technique for many applications in, e.g., science, industry, and medicine. The runtime of conventional reconstruction algorithms is typically much longer than the time it takes to perform the tomographic experiment, and this prohibits the real-time reconstruction and visualization of the imaged object. The research in this dissertation introduces various techniques such as new parallelization schemes, data partitioning methods, and a quasi-3D reconstruction framework, that significantly reduce the time it takes to run conventional tomographic reconstruction algorithms without affecting image quality. The resulting methods and software implementations put reconstruction times in the same ballpark as the time it takes to do a tomographic scan, so that we can speak of real-time tomographic reconstruction.NWONumber theory, Algebra and Geometr

Accelerated iterative image reconstruction for cone-beam computed tomography through Big Data frameworks

One of the latest trends in Computed Tomography (CT) is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artifacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative algorithms to enhance image quality. Most approaches are built around two main operators, backprojection and projection, which are computationally expensive. In this work, we present an implementation of those operators for iterative reconstruction methods exploiting the Big Data paradigm. We define an architecture based on Apache Spark that supports both Graphical Processing Units (GPU) and CPU-based architectures. The aforementioned are parallelized using a partitioning scheme based on the division of the volume and irregular data structures in order to reduce the cost of communication and computation of the final images. Our solution accelerates the execution of the two most computational expensive components with Apache Spark, improving the programming experience of new iterative reconstruction algorithms and the maintainability of the source code increasing the level of abstraction for non-experienced high performance programmers. Through an experimental evaluation, we show that we can obtain results up to 10 faster for projection and 21 faster for backprojection when using a GPU-based cluster compared to a traditional multi-core version. Although a linear speed up was not reached, the proposed approach can be a good alternative for porting previous medical image reconstruction applications already implemented in C/C++ or even with CUDA or OpenCL programming models. Our solution enables the automatic detection of the GPU devices and execution on CPU and GPU tasks at the same time under the same system, using all the available resources.This work was supported by the NIH, United States under Grant R01-HL-098686 and Grant U01 EB018753, the Spanish Ministerio de Economia y Competitividad (projects TEC2013-47270-R, RTC-2014-3028 and TIN2016-79637-P), the Spanish Ministerio de Educacion (grant FPU14/03875), the Spanish Ministerio de Ciencia, Innovacion y Universidades (Instituto de Salud Carlos III, project DTS17/00122; Agencia Estatal de Investigacion, project DPI2016-79075-R-AEI/FEDER, UE), co-funded by European Regional Development Fund (ERDF), ‘‘A way of making Europe’’. The CNIC is supported by the Ministerio de Ciencia, Spain, Innovacion y Universidades, Spain and the Pro CNIC Foundation, Spain, and is a Severo Ochoa Center of Excellence, Spain (SEV-2015-0505). Finally, this research was partially supported by Madrid regional Government, Spain under the grant ’’Convergencia Big data-Hpc: de los sensores a las Aplicaciones. (CABAHLA-CM)’’. Ref: S2018/TCS-4423

Doctor of Philosophy

dissertationX-ray computed tomography (CT) is a widely popular medical imaging technique that allows for viewing of in vivo anatomy and physiology. In order to produce high-quality images and provide reliable treatment, CT imaging requires the precise knowledge of t

Advanced Image Reconstruction for Limited View Cone-Beam CT

In a standard CT acquisition, a high number of projections is obtained around the sample, generally covering an angular span of 360º. However, complexities may arise in some clinical scenarios such as surgery and emergency rooms or Intensive Care Units (ICUs) when the accessibility to the patient is limited due to the monitoring equipment attached. X-ray systems used in these cases are usually C-arms that only enable the acquisition of planar images within a limited angular range. Obtaining 3D images in these scenarios could be extremely interesting for diagnosis or image guided surgery. This would be based on the acquisition of a small number of projections within a limited angular span. Reconstruction of these limited-view data with conventional algorithms such as FDK result in streak artifacts and shape distortion deteriorating the image quality. In order to reduce these artifacts, advanced reconstruction methods can be used to compensate the lack of data by the incorporation of prior information. This bachelor thesis is framed on one of the lines of research carried out by the Biomedical Imaging and Instrumentation group from the Bioengineering and Aerospace Department of Universidad Carlos III de Madrid working jointly with the Hospital General Universitario Gregorio Marañón through its Instituto de Investigación Sanitaria. This line of research is carried out in collaboration with the company SEDECAL, which enables the direct transfer to the industry. Previous work showed that a new iterative reconstruction method proposed by the group, SCoLD, is able to restore the altered contour of the object, suppress greatly the streak artifacts and recover to some extend the image quality by restricting the space of search with a surface constraint. However, the evaluation was only carried out using a simulated mask that described the shape of the object obtained by thresholding a previous CT image of the sample, which is generally not available in real scenarios. The general objective of this thesis is the designing of a complete workflow to implement SCoLD in real scenarios. For that purpose, the 3D scanner Artec Eva was chosen to acquire the surface information of the sample, which was then transformed to be usable as prior information for SCoLD method. The evaluation done in a rodent study showed high similarity between the mask obtained from real data and the ideal mask obtained from a CT. Distortions in shape and streak artifacts in the limited-view FDK reconstruction were greatly reduced when using the real mask with the SCoLD reconstruction and the image quality was highly improved demonstrating the feasibility of the proposal.Grado en Ingeniería Biomédica (Plan 2010

Advanced capabilities for planar X-ray systems

Mención Internacional en el título de doctorThe past decades have seen a rapid evolution towards the use of digital detectors in radiology and a more flexible robotized movement of the system components, X-ray tube and detector. This evolution opened the possibility for incorporating advanced capabilities in these planar X-ray systems, and for providing new valuable diagnostic information compared to the previous technology. Some of the current challenges for radiography are to obtain more quantitative images and to reduce the inherent superposition of tissues because of the 2D nature of the technique. Dual energy radiography, based on the acquisition of two images at different source voltages, enables a separate characterization of soft tissue and bone structures. Its benefits over conventional radiography have been proven in different applications, since it improves information content without adding significant extra acquisition time or radiation dose. In a different direction, a really disruptive advance would be to obtain 3D imaging with systems designed just for planar images. The incorporation of tomographic capabilities into these systems would have to deal with the acquisition of a limited number of projections, with non-standard geometrical configurations. This thesis presents original contributions in these two directions: dual energy radiography and 3D imaging with X-ray systems designed for planar imaging. The work is framed in a line of research of the Biomedical Imaging and Instrumentation Group from the Bioengineering and Aerospace Department of University Carlos III de Madrid working jointly with the University Hospital Gregorio Marañón, focused on the advance of radiology systems. This research line is carried out in collaboration with the group of Computer Architecture, Communications and Systems (ARCOS), from the same university, the Imaging Research Laboratory (IRL) of the University of Washington and the research center CREATIS, France. The research has a clear focus on technology transfer to the industry through the company Sedecal, a Spanish multinational among the 10 best world companies in the medical imaging field. The first contribution of this thesis is a complete novel protocol to incorporate dual energy capabilities that enable quantitative planar studies. The proposal is based on the use of a preliminary calibration with a very simple and low-cost phantom formed by two parts that represent soft tissue and bone equivalent materials. This calibration is performed automatically with no strict placement requirements. Compared to current Dual-energy X-ray Absorptiometry (DXA) systems, 1) it provides real mass-thickness values directly, enabling quantitative planar studies instead of relative comparisons, and 2) it is based on an automatic preliminary calibration without the need of interaction of an experienced technician. The second contribution is a novel protocol for the incorporation of tomographic capabilities into X-ray systems originally intended for planar imaging. For this purpose, we faced three main challenges. First, the geometrical trajectory of equipment follows non-standard circular orbits, thus posing severe difficulties for reconstruction. To handle this, the proposed protocol comprises a new geometrical calibration procedure that estimates all the system parameters per-projection. Second, the reconstruction of a limited number of projections from a reduced angular span leads to severe artifacts when using conventional reconstruction methods. To deal with these limited-view data, the protocol includes a novel advanced reconstruction method that incorporates the surface information of the sample, which can be extracted with a 3D light surface scanner. These data are introduced as an imposed constraint following the Split Bregman formulation. The restriction of the search space by exploiting the surface-based support becomes crucial for a complete recovery of the external contour of the sample and surroundings when the angular span is extremely reduced. The modular, efficient and flexible design followed for its implementation allows for the reconstruction of limited-view data with non-standard trajectories. Third, the optimization of the acquisition protocols has not yet explored with these systems. This thesis includes a study of the optimum acquisition protocols that allowed us to identify the possibilities and limitations of these planar systems. Using the surface-constrained method, it is possible to reduce the total number of projections up to 33% and the angular span down to 60 degrees. The contributions of this thesis open the way to provide depth and quantitative information very valuable for the improvement of radiological diagnosis. This could impact considerably the clinical practice, where conventional radiology is still the imaging modality most used, accounting for 80-90% of the total medical imaging exams. These advances open the possibility of new clinical applications in scenarios where 1) the reduction of the radiation dose is key, such as lung cancer screening or Pediatrics, according to the ALARA criteria (As Low As Reasonably Achievable), 2) a CT system is not usable due to movement limitations, such as during surgery or in an ICU and 3) where costs issues complicate the availability of CT systems, such as rural areas or underdeveloped countries. The results of this thesis has a clear application in the industry, since it is part of a proof of concept of the new generation of planar X-ray systems that will be commercialized worldwide by the company SEDECAL (Madrid, Spain).Los últimos años están viendo un rápido avance de los sistemas de radiología hacia el uso de detectores digitales y a una mayor flexibilidad de movimientos de los principales componentes del sistema, el tubo de rayos X y el detector. Esta evolución abre la posibilidad de incorporar capacidades avanzadas en sistemas de imagen plana por rayos X proporcionando nueva información valiosa para el diagnóstico. Dos retos en radiografía son obtener imágenes cuantitativas y reducir la superposición de tejidos debida a la naturaleza proyectiva de la técnica. La radiografía de energía dual, basada en la adquisición de dos imágenes a diferente kilovoltaje, permite obtener imágenes de tejido blando y hueso por separado. Los beneficios de esta técnica que aumenta la cantidad de información sin añadir un tiempo de adquisición o de dosis de radiación extra significativos frente al uso de radiografía convencional, han sido demostrados en diferentes aplicaciones. En otra dirección, un avance realmente disruptivo sería la obtención de imagen 3D con sistemas diseñados únicamente para imagen plana. La incorporación de capacidades tomográficas en estos sistemas tendría que lidiar con la adquisición de un número limitado de proyecciones siguiendo trayectorias no estándar. Esta tesis presenta contribuciones originales en esas dos direcciones: radiografía de energía dual e imagen 3D con sistemas de rayos X diseñados para imagen plana. El trabajo se encuadra en una línea de investigación del grupo de Imagen Biomédica e Instrumentación del Departamento de Bioingeniería e Ingeniería Aerospacial de la Universidad Carlos III de Madrid junto con el Hospital Universitario Gregorio Marañon, centrada en el avance de sistemas de radiología. Esta línea de investigación se desarollada en colaboración con el grupo Computer Architecture, Communications and Systems (ARCOS), de la misma universidad, el grupo Imaging Research Laboratory (IRL) de la Universidad de Washington y el centro de investigación CREATIS, de Francia. Se trata de una línea de investigación con un claro enfoque de transferencia tecnológica a la industria a través de la compañía SEDECAL, una multinacional española de entre las 10 líderes del mundo en el campo de la radiología. La primera contribución de esta tesis es un protocolo completo para incorporar capacidades de energía dual que permitan estudios cuantitativos de imagen plana. La propuesta se basa en una calibración previa con un maniquí simple y de bajo coste formado por dos materiales equivalentes de tejido blando y hueso respectivamente. Comparado con los sistemas actuales DXA (Dual-energy X-ray Absorptiometry), 1) proporciona valores reales de tejido atravesado, 2) se basa en una calibración automática que no requiere la interacción de un técnico con gran experiencia. La segunda contribución es un protocolo nuevo para la incorporación de capacidades tomográficas en sistemas de rayos X originariamente diseñados para imagen plana. Para ello, nos enfrentamos a tres principales dificultades. En primer lugar, las trayectorias que pueden seguir la fuente y el detector en estos sistemas no constituyen órbitas circulares estándares, lo que plantea retos importantes en la caracterización geométrica. Para solventarlo, el protocolo propuesto incluye una calibración geométrica que estima todos los parámetros geométricos del sistema para cada proyección. En segundo lugar, la reconstrucción de un número limitado de proyecciones adquiridas en un rango angular reducido da lugar a artefactos graves cuando se reconstruye con algoritmos convencionales. Para lidiar con estos datos de ángulo limitado, el protocolo incluye un nuevo método avanzado de reconstrucción que incorpora la información de superficie de la muestra, que se puede se obtener con un escáner 3D. Esta información se impone como una restricción siguiendo la formulación de Split Bregman, para compensar la falta de datos. La restricción del espacio de búsqueda a través de la explotación del soporte basado en superficie, es crucial para una recuperación completa del contorno externo de la muestra cuando el rango angular es extremadamente pequeño. El diseño modular, eficiente y flexible de la implementación propuesta permite reconstruir datos de ángulo limitado obtenidos con posiciones de fuente y detector no estándar. En tercer lugar, hasta la fecha, no se ha explorado la optimización del protocolo de adquisición con estos sistemas. Esta tesis incluye un estudio de los protocolos óptimos de adquisición que permitió identificar las posibilidades y limitaciones de estos sistemas de imagen plana. Gracias al método de reconstrucción basado en superficie, es posible reducir el número total de proyecciones hasta el 33% y el rango angular hasta 60 grados. Las contribuciones de esta tesis abren la posibilidad de proporcionar información de profundidad y cuantitativa muy valiosa para la mejora del diagnóstico radiológico. Esto podría impactar considerablemente en la práctica clínica, donde la radiología convencional es todavía la modalidad de imagen más utilizada, abarcando el 80- 90% del total de los exámenes de imagen médica. Estos avances abren la posibilidad de nuevas aplicaciones clínicas en escenarios donde 1) la reducción de la dosis de radiación es clave, como en screening de cáncer de pulmón, de acuerdo con el criterio ALARA (As Low As Reasonably Achievable), 2) no se puede usar un sistema TAC por limitaciones de movimiento como en cirugía o UCI, o 3) el coste limita la disponibilidad de sistemas TAC, como en zonas rurales o en países subdesarrollados. Los resultados de esta tesis presentan una clara aplicación industrial, ya que son parte de un prototipo de la nueva generación de sistemas planos de rayos X que serán distribuidos mundialmente por la compañía SEDECAL.This thesis has been developed as part of several research projects with public funding: - DPI2016-79075-R. ”Nuevos escenarios de tomografía por rayos X”, IP: Mónica Abella García, Ministerio de Economía y Competitividad, 01/01/2017-31/12/2019, 147.620 e. - ”Nuevos escenarios de tomografía por rayos X (NEXT) DPI2016-79075-R. Ministerio de Economía”, Industria y Competitividad. (Universidad Carlos III de Madrid). 30/12/2016-29/12/2019. 147.620 e. (…) - FP7-IMI-2012 (GA-115337), ”PreDict-TB: Model-based preclinical development of anti-tuberculosis drug combinations”. FP7-IMI - Seventh Framework Programme (EC-EFPIA). Unión Europea. (Universidad Carlos III de Madrid). 01/05/2012-31/10/2017. (…) - TEC2013-47270-R, ”Avances en Imagen Radiológica (AIR)”, Ministerio de Economía y Competitividad”, 01/01/2014-31/12/2016. IP: Mónica Abella Garcia and Manuel Desco Menéndez. 160.204 e (…) - RTC-2014-3028-1, ”Nuevos Escenarios Clínicos con Radiología Avanzada (NECRA)”, Ministerio de Economía y Competitividad, 01/06/2014-31/12/2016 IP: Mónica Abella García. 2014-2016. 219.458,96 e - IDI-20130301, ”Nuevo sistema integral de radiografía (INNPROVE: INNovative image PROcessing in medicine and VEterinary)”, IP: Mónica Abella García and Manuel Desco Menéndez. Ministerio de Economía y Competitividad. Subcontratación CDTI, 14/01/2013-31/03/2015. Total: 1.860.629e (UC3M: 325.000e). (Art. 83) - IPT-2012-0401-300000 INNPACTO 2012, ”Tecnologías para Procedimientos Intraoperatorios Seguros y Precisos. XIORT. MINECO. (Universidad Carlos III de Madrid). 01/01/2013-31/12/2015.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Doménec Ros Puig.- Secretario: Cyril Riddell.- Vocal: Yannick Boursie