Reduction of Limited Angle Artifacts in Medical Tomography via Image Reconstruction

Artifacts are unwanted effects in tomographic images that do not reflect the nature of the object. Their widespread occurrence makes their reduction and if possible removal an important subject in the development of tomographic image reconstruction algorithms. Limited angle artifacts are caused by the limited angular measurements, constraining the available tomographic information. This thesis focuses on reducing these artifacts via image reconstruction in two cases of incomplete measurements from: (1) the gaps left after the removal of high density objects such as dental fillings, screws and implants in computed tomography (CT) and (2) partial ring scanner configurations in positron emission tomography (PET). In order to include knowledge about the measurement and noise, prior terms were used within the reconstruction methods. Careful consideration was given to the trade-off between image blurring and noise reduction upon reconstruction of low-dose measurements.Development of reconstruction methods is an incremental process starting with testing on simple phantoms towards more clinically relevant ones by modeling the respective physical processes involved. In this work, phantoms were constructed to ensure that the proposed reconstruction methods addressed to the limited angle problem. The reconstructed images were assessed qualitatively and quantitatively in terms of noise reduction, edge sharpness and contrast recovery.Maximum a posteriori (MAP) estimation with median root prior (MRP) was selected for the reconstruction of limited angle measurements. MAP with MRP successfully reduced the artifacts caused by limited angle data in various datasets, tested with the reconstruction of both list-mode and projection data. In all cases, its performance was found to be superior to conventional reconstruction methods such as total-variation (TV) prior, maximum likelihood expectation maximization (MLEM) and filtered backprojection (FBP). MAP with MRP was also more robust with respect to parameter selection than MAP with TV prior.This thesis demonstrates the wide-range applicability of MAP with MRP in medical tomography, especially in low-dose imaging. Furthermore, we emphasize the importance of developing and testing reconstruction methods with application-specific phantoms, together with the properties and limitations of the measurements in mind

Interpolación de los datos de Radón calculados a partir de proyecciones tomográficas Cone-Beam en trayectoria circular

La Tomografía Computarizada Cone-Beam es una técnica de adquisición de imágenes volumétricas en la cual una zona del cuerpo es irradiada con un fuente puntual de rayos X. Este tipo de técnica es utilizada en diferentes aplicaciones médicas, como en el tratamiento del cáncer de próstata, en el cual se efectúa una reconstrucción tomográfica previa a la radioterapia con el fin de establecer la posición correcta de la próstata y la estrategia correcta para radiar esa zona. Cuando la trayectoria de captura de las proyecciones es circular, los algoritmos de reconstrucción deben resolver la ausencia de datos asociada a que la fuente de radiación no intersecta todos los planos que cortan el cuerpo. El método de reconstrucción analítica propuesto por Grangeat establece la posibilidad de calcular la derivada de la transformada de Radón del objeto a partir de las proyecciones cone-beam. Para este método de reconstrucción, la imposibilidad de hacer una reconstrucción perfecta se refleja en la ausencia de datos en una zona de sombra (shadow-zone) en el espacio de Radón. Esta zona de sombra es mayor mientras más cerca se encuentre la fuente de radiación del objeto a reconstruir. En particular, para la aplicación de planeación de radioterapia para pacientes con cáncer de próstata, alejar la fuente de radiación implica una menor focalización de la zona afectada, radiando zonas aledañaas. El relleno de este shadow-zone suele tratarse como un problema de interpolación convencional, aplicando métodos simples que no consideran la naturaleza inherente de los datos ainterpolar. Este trabajo propone un método de estimación de los datos faltantes del shadow-zone, agrupando los datos de la derivada de Radón en planos meridionales y regularizando la proyección parallel-beam asociada a estos datos por medio de un filtrado iterativo. Se muestran las ventajas y alcances de este método comparándolo con métodos de interpolación convencionales, y obteniendo una mejora en la relación señal a ruido (PSNR) de hasta 2 dB. / Abstract. Cone-Beam Computed Tomography (CBCT) is a medical imaging technique for acquisitionof volumetric images of the human body, where the region of interest is irradiated with a punctual source of X rays. This technique is currently used in diferent medical applications, such as in the treatment of prostate cancer, where a tomographic reconstruction is obtained before radiotherapy to accurately identify the prostate position and the amount of the radiation dose. When using a circular trajectory for capturing CBCT projections, reconstruction algorithms must take into account the absence of data produced when the radiation source do not intersect the body cutting plane. With the analytic reconstruction method proposed by Grangeat, the Radon transform derivative can be calculated from cone-beam projections. For this method, the missing data is located in a so called shadow-zone in the Radon space, making it impossible to obtain a perfect reconstruction. The shadow-zone size (amount of missing data) increases as the radiation source is closer to the object to be reconstructed. In particular, for radiotherapy planning of prostate cancer patients, moving away the radiation source means to spread the radiation dose, afecting healthy organs near the prostate. Filling up the shadow-zone is commonly treated as a conventional interpolation problem, by applying standard and simple methods that do not take into account the nature of the data to be interpolated. This work proposes an estimation method of missing data in the shadowzone, by grouping the Radon derivative data into meridional planes and then regularizing the parallel-beam projection associated to this data with an iterative _ltering. Performance and advantages of the proposed method are presented by comparing it with conventional interpolation methods, and obtaining an increment in the peak signal-to-noise ratio (PSNR) of about 2 dB.Maestrí

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches