Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography

Accurate estimation of the proton stopping power ratio (SPR) is important for treatment planning and dose prediction for proton beam therapy. The state-of-the-art clinical practice for estimating patient-specific SPR distributions is the stoichiometric calibration method using single-energy computed tomography (SECT) images, which in principle may introduce large intrinsic uncertainties into estimation results. One major factor that limits the performance of SECT-based methods is the Hounsfield unit (HU) degeneracy in the presence of tissue composition variations. Dual-energy computed tomography (DECT) has shown the potential of reducing uncertainties in proton SPR prediction via scanning the patient with two different source energy spectra. Numerous methods have been studied to estimate the SPR by dual-energy CT DECT techniques using either image-domain or sinogram-domain decomposition approaches. In this work, we implement and evaluate a novel DECT approach for proton SPR mapping, which integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). This method reconstructs two images of material parameters simultaneously from the DECT measurement data and then uses them to predict the electron densities and the mean excitation energies, which are required by the Bethe equation for computing proton SPR. The proposed JSIR-BVM method is first compared with image-domain and sinogram-domain decomposition approaches based on three available SPR models including the BVM in a well controlled simulation framework that is representative of major uncertainty sources existing in practice. The intrinsic SPR modeling accuracy of the three DECT-SPR models is validated via theoretical computed radiological quantities for various reference human tissues. The achievable performances of the investigated methods in the presence of image formation uncertainties are evaluated using synthetic DECT transmission sinograms of virtual cylindrical phantoms and virtual patients, which consist of reference human tissues with known densities and compositions. The JSIR-BVM method is then experimentally commissioned using the DECT measurement data acquired on a Philips Brilliance Big Bore CT scanner at 90 kVp and 140 kVp for two phantoms of different sizes, each of which contains 12 different soft and bony tissue surrogates. An image-domain decomposition method that utilizes the two HU images reconstructed via the scanner\u27s software is implemented for comparison The JSIR-BVM method outperforms the other investigated methods in both the simulation and experimental settings. Although all investigated DECT-SPR models support low intrinsic modeling errors (i.e., less than 0.2% RMS errors for reference human tissues), the achievable accuracy of the image- and sinogram-domain methods is limited by the image formation uncertainties introduced by the reconstruction and decomposition processes. In contrast, by taking advantage of an accurate polychromatic CT data model and a joint DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves much better accuracy and precision on proton SPR estimation compared to the image- and sinogram-domain methods for various materials and object sizes, with an overall RMS-of-mean error of 0.4% and a maximum absolute-mean error of 0.7% for test samples in the experimental setting. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image- and sinogram-domain methods while exhibiting relatively higher spatial resolution. The results suggest that the JSIR-BVM method has the potential for better SPR prediction in clinical settings

Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used

Advances in dual-energy computed tomography imaging of radiological properties

Dual-energy computed tomography (DECT) has shown great potential in the reduction of uncertainties of proton ranges and low energy photon cross section estimation used in radiation therapy planning. The work presented herein investigated three contributions for advancing DECT applications. 1) A linear and separable two-parameter DECT, the basis vector model (BVM) was used to estimate proton stopping power. Compared to other nonlinear two-parameter models in the literature, the BVM model shows a comparable accuracy achieved for typical human tissues. This model outperforms other nonlinear models in estimations of linear attenuation coefficients. This is the first study to clearly illustrate the advantages of linear model not only in accurately mapping radiological quantities for radiation therapy, but also in providing a unique model for accurate linear forward projection modelling, which is needed by the statistical iterative reconstruction (SIR) and other advanced DECT reconstruction algorithms. 2) Accurate DECT requires knowledge of x-ray beam properties. Using the Birch-Marshall1 model and beam hardening correction coefficients encoded in a CT scanner’s sinogram header files, an efficient and accurate way to estimate the x-ray spectrum is proposed. The merits of the proposed technique lie in requiring no physical transmission measurement after a one-time calibration against an independently measured spectrum. This technique can also be used in monitoring the aging of x-ray CT tubes. 3) An iterative filtered back projection with anatomical constraint (iFBP-AC) algorithm was also implemented on a digital phantom to evaluate its ability in mitigating beam hardening effects and supporting accurate material decomposition for in vivo imaging of photon cross section and proton stopping power. Compared to iFBP without constraints, both algorithms demonstrate high efficiency of convergence. For an idealized digital phantom, similar accuracy was observed under a noiseless situation. With clinically achievable noise level added to the sinograms, iFBP-AC greatly outperforms iFBP in prediction of photon linear attenuation at low energy, i.e., 28 keV. The estimated mean errors of iFBP and iFBP-AC for cortical bone are 1% and 0.7%, respectively; the standard deviations are 0.6% and 5%, respectively. The achieved accuracy of iFBP-AC shows robustness versus contrast level. Similar mean errors are maintained for muscle tissue. The standard deviation achieved by iFBP-AC is 1.2%. In contrast, the standard deviation yielded by iFBP is about 20.2%. The algorithm of iFBP-AC shows potential application of quantitative measurement of DECT. The contributions in this thesis aim to improve the clinical performance of DECT

Development, Optimization and Clinical Evaluation Of Algorithms For Ultrasound Data Analysis Used In Selected Medical Applications.

The assessment of soft and hard tissues is critical when selecting appropriate protocols for restorative and regenerative therapy in the field of dental surgery. The chosen treatment methodology will have significant ramifications on healing time, success rate and overall long-time oral health. Currently used diagnostic methods are limited to visual and invasive assessments; they are often user-dependent, inaccurate and result in misinterpretation. As such, the clinical need has been identified for objective tissue characterization, and the proposed novel ultrasound-based approach was designed to address the identified need. The device prototype consists of a miniaturized probe with a specifically designed ultrasonic transducer, electronics responsible for signal generation and acquisition, as well as an optimized signal processing algorithm required for data analysis. An algorithm where signals are being processed and features extracted in real-time has been implemented and studied. An in-depth algorithm performance study has been presented on synthetic signals. Further, in-vitro laboratory experiments were performed using the developed device with the algorithm implemented in software on animal-based samples. Results validated the capabilities of the new system to reproduce gingival assessment rapidly and effectively. The developed device has met clinical usability requirements for effectiveness and performance

Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues

Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation by tissue optical properties, an effect that causes spectral corruption. Predictions of the spectral variations of light fluence in tissue are challenging since the spatial distribution of optical properties in tissue cannot be resolved in high resolution or with high accuracy by current methods. Spectral corruption has fundamentally limited the quantification accuracy of optical and optoacoustic methods and impeded the long sought-after goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical but still unattainable target for the assessment of oxygenation in physiological processes and disease. We discover a new principle underlying light fluence in tissues, which describes the wavelength dependence of light fluence as an affine function of a few reference base spectra, independently of the specific distribution of tissue optical properties. This finding enables the introduction of a previously undocumented concept termed eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively account for wavelength dependent light attenuation without explicit knowledge of the tissue optical properties. We validate eMSOT in more than 2000 simulations and with phantom and animal measurements. We find that eMSOT can quantitatively image tissue sO2 reaching in many occasions a better than 10-fold improved accuracy over conventional spectral optoacoustic methods. Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor; revealing so far unattainable tissue physiology patterns. Last, we related eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2 images and tissue perfusion and hypoxia maps obtained by correlative histological analysis

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

MR-based pseudo-CT generation using water-fat decomposition and Gaussian mixture regression

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017O uso de tomografia computorizada (CT) é considerado como a prática clínica adequada para aplicações clínicas onde a simulação da atenuação de radiação pelos tecidos corporais é necessária, tais como a correcção de atenuação dos fotões em Tomografia de Emissão de Positrões (PET) e no cálculo da dosagem a ser administrada durante o planeamento de radioterapia (RTP). Imagens de ressonância magnética (MRI) têm vindo a substituir o uso de TC em algumas aplicações, sobretudo devido ao seu superior contraste entre tecidos moles e ao facto de não usar radiação ionizante. Desta forma, técnicas como PET-MRI e o planeamento de radioterapia apenas com recurso a imagens de ressonância magnética são alvo de uma crescente atenção. No entanto, estas técnicas estão limitadas pelo facto de imagens de ressonância magnética não fornecerem informação acerca da atenuação e absorção de radiação pelos tecidos. Normalmente, de forma a solucionar este problema, uma imagem de tomografia computorizada é adquirida de forma a realizar a correcção da atenuação dos fotões, assim como a dose a ser entregue em radioterapia. No entanto, esta prática introduz erros aquando do alinhamento entre as imagens de MRI e CT, que serão propagados durante todo o procedimento. Por outro lado, o uso de radiação ionizante e os custos adicionais e tempo de aquisição associado à obtenção de múltiplas modalidades de imagem limitam a aplicação clínica destas práticas. Assim, o seguimento natural prende-se com a completa substituição do uso de CT por MRI. Desta forma, o desenvolvimento de um método para a obtenção de uma imagem equivalente a CT usando MRI é necessário, sendo a imagem resultante designada de pseudo-CT. Vários métodos foram desenvolvidos de forma a construir pseudo-CT, usando métodos baseados na anatomia do paciente ou em métodos de regressão entre CT e MRI. No entanto, no primeiro caso, erros significativos são frequentes devido ao difícil alinhamento entre as imagens em casos em que a geometria do paciente é muito diferente da presente no atlas. No segundo caso, a ausência de sinal no osso cortical em MRI, torna-o indistinguível do ar. Sequências que usam um tempo de eco muito curto são normalmente utilizadas para distinguir osso cortical de ar. No entanto, para áreas com maior dimensão, como a área pélvica, dificuldades relacionadas com o equipamento e com o ruído limitam a sua aplicação nestas áreas. Por outro lado, estes métodos utilizam frequentemente diferentes imagens de MRI de forma a obter diferentes contrastes, aumentando assim o tempo de aquisição das imagens. Nesta dissertação, é proposto um método para a obtenção de um pseudo-CT baseado na combinação de um algoritmo de decomposição de água e gordura e um modelo de regressão de mistura gaussiana para a região pélvica através da aquisição de sequências de MRI convencionais. Desta forma, a aquisição de diferentes contrastes é obtida por pós-processamento das imagens originais. Desta forma, uma imagem ponderada em T1 foi adquirida com 3 tempos de eco. Um algoritmo de decomposição do sinal de ressonância magnética em sinal proveniente de água e gordura foi utilizado, permitindo a obtenção de duas imagens, cada uma representando apenas o sinal da água e gordura, respectivamente. Usando estas duas imagens, uma imagem da fracção de gordura em cada voxel foi também calculada. Por outro lado, usando o primeiro e o terceiro eco foi possível calcular o decaimento de sinal devido a efeitos relacionados com o decaimento T2*. O método para gerar o pseudo-CT baseia- se num modelo de regressão duplo entre as variáveis relacionadas com MRI e CT. Assim, o primeiro modelo aplica-se aos tecidos moles, enquanto que o segundo modelo se aplica aos tecidos ósseos. A segmentação entre estes tecidos foi realizada através da delineação manual dos tecidos ósseos. No caso do modelo de regressão para os tecidos moles, o modelo consiste numa regressão polinomial entre as imagens da fracção de gordura e os valores de CT. A ordem do polinómio usada foi obtida pela minimização do erro absoluto médio. No caso do modelo de regressão para os tecidos ósseos, um modelo de regressão de mistura gaussiana foi aplicado usando as imagens de gordura, água, de fracção de gordura e de R2*. Estas variáveis foram selecionadas, uma vez que estudos prévios correlacionam esta com a densidade mineral óssea, que por sua vez está relacionada com as intensidades em CT. A influência de incluir no modelo de regressão informação acerca da vizinhança foi estudada através da inclusão de imagens do desvio padrão nos 27 voxéis na vizinhança das variáveis previamente incluídas no modelo. O número de componentes a usar no modelo de regressão de mistura gaussiana foi obtido através da minimização do critério de Akaike. O pseudo-CT final foi obtido pela sobreposição das imagens obtidas através do duplo modelo de regressão, seguido da aplicação de um filtro gaussiano com desvio padrão de 0.5 de forma a mitigar os erros na segmentação dos tecidos ósseos. Este método foi validado usando imagens da zona pélvica de 6 pacientes usando um procedimento leave-one-out-cross-validation (LOOCV). Durante este procedimento, o modelo foi estimado através das variáveis de 5 pacientes (imagens de treino) e aplicado às variáveis relacionadas com MRI do paciente restante (imagem de validação), de forma a gerar o pseudo-CT. Este procedimento foi repetido para todas as seis combinações de imagens de treino e de validação e os pseudo-CT obtidos foram comparados com a imagem TC correspondente. No caso do modelo para os tecidos moles, verificou-se que a utilização de um polinómio de segundo grau permitia a obtenção de melhores resultados. Da mesma forma, verificou-se que a inclusão de informação acerca da vizinhança permitia uma melhor estimativa dos valores de pseudo-CT no caso dos tecidos ósseos. A segmentação dos tecidos ósseos foi considerada adequada uma vez que o valor médio do coeficiente de Dice entre estes tecidos e o osso em CT foi de 0.91 ±0.02. O valor médio do erro absoluto entre o pseudo-CT e a correspondente CT para todos os pacientes foi de 37.76±3.11 HU, enquanto que no caso dos tecidos ósseos o valor foi de 96.61±10.49 HU. Um erro médio de -2.68 ± 6.32 HU foi obtido, denotando a presença de bias no processo. Por outro lado, valores médios de peak-to-signal-noise-ratio (PSNR) e strucutre similarity índex (SSIM) de 23.92±1.62 dB e 0.91±0.01 foram obtidos, respectivamente. Os maiores erros foram encontrados no recto, uma vez que o ar não foi considerado neste método, nas interfaces entre diferentes tecidos, devido a erros no alinhamento das imagens, e nos tecidos ósseos. Desta forma, o método de obtenção de um pseudo-CT proposto nesta dissertação demonstrou ter potencial para permitir uma correcta estimativa da intensidade em CT. Os resultados obtidos demonstram uma melhoria significativa quando comparados com outros métodos encontrados na literatura que se baseiam num método relacionado com a intensidade, enquanto que se encontram na mesma ordem de magnitude de métodos baseados na anatomia do paciente. Para além disso, quando comparados com os primeiros, este método tem a vantagem de apenas uma sequência MRI ser utilizada, levando a uma redução no tempo de aquisição e nos custos associados. Por outro lado, a principal limitação deste método prende-se com a segmentação manual dos tecidos ósseos, o que dificulta a sua implementação clínica. Desta forma, o desenvolvimento de técnicas de segmentação automáticas dos tecidos ósseos torna-se necessária, sendo exemplos destas técnicas a criação de um shape model ou através da segmentação baseada num atlas. A combinação destes métodos com o método descrito nesta dissertação pode permitir a obtenção de uma alternativa às imagens de CT para o cálculo das doses em radioterapia e correcção de atenuação em PET-MRI.Purpose: Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET-MRI applications, as well as for dose planning in MRI based radiation therapy workflows, due to the lack of correlation between the MR signal and the electron density of different tissues. This dissertation presents a method to generate a pseudo-CT from MR images acquired with a conventional MR pulse sequence. Methods: A T1-weighted Fast Field Echo sequence with 3 echo times was used. A 3-point water-fat decomposition algorithm was applied to the original MR images to obtain water and fat-only images as well as a quantitative fat fraction image. A R2* image was calculated using a mono-exponential fit between the first and third echo of the original MR images. The method for generating the pseudo-CT includes a dual-model regression between the MR features and a matched CT image. The first model was applied to soft tissues, while the second-model was applied to the bone anatomy that were previously segmented. The soft-tissue regression model consists of a second-order polynomial regression between the fat fraction values in soft tissue and the HU values in the CT image, while the bone regression model consists of a Gaussian mixture regression including the water, fat, fat fraction and R2* values in bone tissues. Neighbourhood information was also included in the bone regression model by calculating an image of the standard deviation of 27-neighbourhood of each voxel in each MR related feature. The final pseudo-CT was generated by combining the pseudo-CTs from both models followed by the application of a Gaussian filter for additional smoothing. This method was validated using datasets covering the pelvic area of six patients and applying a leave-one-out-cross-validation (LOOCV) procedure. During LOOCV, the model was estimated from the MR related features and the CT data of 5 patients (training set) and applied to the MR features of the remaining patient (validation set) to generate a pseudo-CT image. This procedure was repeated for the all six training and validation data combinations and the pseudo-CTs were compared to the corresponding CT image. Results: The average mean absolute error for the HU values in the body for all patients was 37.76±3.11 HU, while the average mean absolute error in the bone anatomy was 96.61±10.49 HU. No large differences in method accuracy were noted for the different patients, except for the air in the rectum which was classified as soft tissue. The largest errors were found in the rectum and in the interfaces between different tissue types. Conclusions: The pseudo-CT generation method here proposed has the potential to provide an accurate estimation of HU values. The results here reported are substantially better than other voxel-based methods proposed. However, they are in the same range as the results presented in anatomy-based methods. Further investigation in automatic MRI bone segmentation methods is necessary to allow the automatic application of this method into clinical practice. The combination of these automatic bone segmentation methods with the model here reported is expected to provide an alternative to CT images for dose planning in radiotherapy and attenuation correction in PET-MRI