Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015. / Submitted by Fernanda Percia França (fernandafranca@bce.unb.br) on 2015-11-20T17:20:35Z No. of bitstreams: 1 2015_ThalesHenriqueDantas.pdf: 3537939 bytes, checksum: 20f1179054479db12fcfe14d4e20b397 (MD5) / Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2016-05-12T20:20:38Z (GMT) No. of bitstreams: 1 2015_ThalesHenriqueDantas.pdf: 3537939 bytes, checksum: 20f1179054479db12fcfe14d4e20b397 (MD5) / Made available in DSpace on 2016-05-12T20:20:38Z (GMT). No. of bitstreams: 1 2015_ThalesHenriqueDantas.pdf: 3537939 bytes, checksum: 20f1179054479db12fcfe14d4e20b397 (MD5) / Fourier velocity encoding (FVE) é útil no diagnóstico de doenças valvulares, visto que pode eliminar os efeitos de volume parcial que podem causar perda de informação de diagnóstico no imageamento de fluxo cardiovascular por contraste de fase. FVE também foi proposto como método para a medição da taxa de cisalhamento da superfície das artérias carótidas. Apesar de o tempo de aquisição para FVE no espaço de Fourier bi-dimensional(2DFT) ser proibitivamente longo, o uso de FVE espiral se mostra bastante promissor, uma vez que este é substancialmente mais rápido. Contudo, a reconstrução dos dados de FVE em espiral é longo, devido à sua multi-dimensionalidade e ao uso de amostragem não Cartesiana. Isso é particularmente importante para aquisições de múltiplos cortes, volumes e(ou) de múltiplos canais. Os conjuntos de dados de FVE em espiral consistem em pilhas de espirais resolvidas no espaço kx-ky-kv. A distribuição de velocidade espaço-temporal, m(x, y, v, t), é tipicamente obtida a partir dos dados no espaço-k, M(kx, ky, kv, t), aplicando uma transformada inversa de Fourier não uniforme ao longo de kx-ky, seguida de uma transformada Cartesiana ao longo de kv. Com esta abordagem, toda a matriz m(x, y, v, t) é calculada. Entretanto estamos tipicamente interessados nas distribuições de velocidade associadas com uma pequena região de interesse dentro do plano x-y. Nós propomos o uso da reconstrução de um único voxel usando a transformada direta de Fourier (DrFT) para reconstruir os dados da FVE espiral. Ao passo que o tempo de reconstrução por DrFT de toda a imagem é ordens de magnitude maior que a reconstrução por gridding ou Non Uniform Fast Fourier Transform(NUFFT), a equação da DrFT permite a reconstrução de voxels individuais com uma quantidade consideravelmente reduzida de esforço computacional. Adicionalmente, propomos o uso de placas de processamento gráfico de uso geral (GPGPUs) para acelerar ainda mais a reconstrução e alcançar reconstruções de FVE espirais de maneira aparentemente instantânea. É apresentada também uma proposta para, potencialmente, acelerar também a reconstrução por gridding ou NUFFT utilizando o algoritmo de Goertzel para reconstruir uma quantidade limitada de pontos também por estes métodos. _______________________________________________________________________________________________ ABSTRACT / Fourier velocity encoding (FVE) is useful in the assessment of valvular disease, as it eliminates partial volume effects that may cause loss of diagnostic information in phase-contrast imaging. FVE has also been proposed as a method for measuring wall shear rate in the carotid arteries. Although the scan-time of Two-dimensional Fourier Transform (2DFT) FVE is prohibitively long for clinical use, the spiral FVE method shows promise, as it is substantially faster. However, the reconstruction of spiral FVE data is time-consuming, due to its multidimensionality and the use of non- Cartesian sampling. This is particularly true for multi-slice/3D and/or multi-channel acquisitions. Spiral FVE datasets consist of temporally-resolved stacks-of-spirals in kx-ky-kv space. The spatial-temporal-velocity distribution, m(x, y, v, t), is typically obtained from the k-space data, M(kx, ky, kv, t), by first using a non-Cartesian inverse Fourier transform along kx-ky, followed by a Cartesian inverse Fourier transform along kv. With this approach, the entire m(x, y, v, t) matrix is calculated. However, we are typically only interested in the velocity distributions associated with a small regionof- interest within the x-y plane. We propose the use of single-voxel direct Fourier transform (DrFT) to reconstruct spiral FVE data. While whole-image DrFT is orders of magnitude slower than the gridding and Non Uniform Fast Fourier Transform( NUFFT) algorithms, the DrFT equation allows the reconstruction of individual voxels of interest, which considerably reduces the computation time. Additionally, we propose the use of general-purpose computing on graphics processing units (GPGPUs) to further accelerate computation and achieve seemingly instantaneous spiral FVE reconstruction. We also propose a method that shows potential to accelerate reconstructions using gridding and NUFFT by means of using the Goertzel algorithm to reconstruct onlya small number of pixels.

Single Ventricle congenital heart defects with cyanotic mixing between systemic and pulmonary circulations afflict 2 per 1000 live births. The total cavo-pulmonary connection (TCPC), where the superior and inferior vena cavae are sutured to the left and right pulmonary arteries, is the current procedure of choice. It is believed that reducing the fluid mechanical power losses in the TCPC will relieve strain on the single functional ventricle. It is hypothesized that a proposed idealized TCPC design, decreases power losses to a level below that of any other TCPC designs, while providing other advantages and increased flexibility. Physical models with slightly different geometries of the proposed design were created, and in vitro experiments carried out with particle image velocimetry (PIV), phase contrast magnetic resonance imaging (PC-MRI), and control volume flow analysis at physiological flow rates. Computational fluid dynamics (CFD) was used for numerical studies of the same geometries as in the physical models. Power losses were calculated using the control volume method and the viscous power dissipation function. The latter method incorporated registration of high-resolution PC-MRI velocity vectors to tetrahedral meshes followed by inverse interpolation of the vectors onto the meshes. Detailed flow structures were analyzed. Results show that the new design is more energy efficient than any other idealized models. Furthermore, a tool was developed to extract flow and vessel information from PC-MRI datasets obtained from patients with Fontan connections. The tool utilized a display algorithm, which was developed for optimal noise detection in PC-MRI images. This enabled accurate segmentation. Comparing PC-MRI images before and after this accurate segmentation showed that the standard deviations of the pixels at the perimeter of the segmented vessel were statistically significantly smaller after the segmentation in 94.1% of the datasets investigated. The developed tool was able to extract flow, flow in the quadrants of vessels, area of the segmented vessel, velocities and pulsatility indices. The velocity vectors were exported for use as CFD boundary conditions in models reconstructed from patient anatomies. A database was created with patient PC-MRI data from approximately 140 patients, which is probably the largest database in the world.

The purpose of this research is to determine if it is possible to validate the new 1D method for measuring pulse wave velocity in the aorta in vivo and estimate compliance. Arterial pressure and blood flow characterize the traveling of blood from the heart to the arterial system and have played a significant role in the evaluation of cardiovascular diseases. Blood vessel distensibility can give some information on the evolution of cardiovascular disease. A patient’s aorta cannot be explanted to measure compliance; therefore we are using a flow phantom model to validate the 1D pulse wave velocity technique to estimate compliance.

Magnetic resonance images (MRI) are acquired by sampling the current of induced electromotiveforce (EMF). EMF is induced due to flux of the net magnetic field from coherent nuclear spins with intrinsic magnetic dipole moments. The spins are excited by (non-ionizing) radio frequency electromagnetic radiation in conjunction with stationary and gradient magnetic fields. These images reveal detailed internal morphological structures as well as enable functional assessment of the body that can help diagnose a wide range of medical conditions. The aim of this project was to unwrap phase contrast cine magnetic resonance images, targeting the great vessels. The maximum encoded velocity (venc) is limited to the angular phase range [-π, π] radians. This may result in aliasing if the venc is set too low by the MRI personnel. Aliased images yield inaccurate cardiac stroke volume measurements and therefore require acquisition retakes. The retakes might be avoided if the images could be unwrapped in post-processing instead. Using computer vision, the angular phase of flow measurements as well as the angular phase of retrospectively wrapped image sets were unwrapped. The performances of three algorithms were assessed, Laplacian algorithm, sequential tree-reweighted message passing and iterative graph cuts. The associated energy formulation was also evaluated. Iterative graph cuts was shown to be the most robust with respect to the number of wraps and the energies correlated with the errors. This thesis shows that there is potential to reduce the number of acquisition retakes, although the MRI personnel still need to verify that the unwrapping performances are satisfactory. Given the promising results of iterative graph cuts, next it would be valuable to investigate the performance of a globally optimal surface estimation algorithm.