| Campo DC | Valor | Idioma |
|---|
| dc.contributor.advisor | Farias, Mylène Christine Queiroz de | - |
| dc.contributor.author | Lima, Jonathan Alis Salgado | - |
| dc.date.accessioned | 2020-06-15T10:48:32Z | - |
| dc.date.available | 2020-06-15T10:48:32Z | - |
| dc.date.issued | 2020-06-15 | - |
| dc.date.submitted | 2019-08-09 | - |
| dc.identifier.citation | LIMA, Jonathan Alis Salgado. The application of analysis filters in compressed sensing algorithms for magnetic resonance imaging reconstruction. 2019. xix, 97 f., il. Tese (Doutorado em Informática)—Universidade de Brasília, Brasília, 2019. | pt_BR |
| dc.identifier.uri | https://repositorio.unb.br/handle/10482/38031 | - |
| dc.description | Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019. | pt_BR |
| dc.description.sponsorship | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). | pt_BR |
| dc.language.iso | Inglês | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | The application of analysis filters in compressed sensing algorithms for magnetic resonance imaging reconstruction | pt_BR |
| dc.title.alternative | Métodos de filtragem digital em compressed sensing para reconstrução de imagens de ressonância magética | pt_BR |
| dc.type | Tese | pt_BR |
| dc.subject.keyword | Compressed sensing | pt_BR |
| dc.subject.keyword | Imageamento médico | pt_BR |
| dc.subject.keyword | Filtragem | pt_BR |
| dc.subject.keyword | Ressonância magnética | pt_BR |
| dc.rights.license | A concessão da licença deste item refere-se ao termo de autorização impresso assinado pelo autor com as seguintes condições: Na qualidade de titular dos direitos de autor da publicação, autorizo a Universidade de Brasília e o IBICT a disponibilizar por meio dos sites www.bce.unb.br, www.ibict.br, http://hercules.vtls.com/cgi-bin/ndltd/chameleon?lng=pt&skin=ndltd sem ressarcimento dos direitos autorais, de acordo com a Lei nº 9610/98, o texto integral da obra disponibilizada, conforme permissões assinaladas, para fins de leitura, impressão e/ou download, a título de divulgação da produção científica brasileira, a partir desta data. | pt_BR |
| dc.contributor.advisorco | Miosso, Cristiano Jacques | - |
| dc.description.abstract1 | Magnetic Resonance Imaging (MRI) exams usually take a long time to be performed because
they require a great amount of measurements to reconstruct an image with good
quality. Decreasing the acquisition time of MRI can prevent motion artifacts, make possible
to perform new types of exams, and also reduce MRI costs.
Compressed Sensing (CS) techniques are able to reconstruct MRI images at a sub-
Nyquist rate, provided that the signals are sparse in a known domain. A CS method known
as total variation (TV) minimization, minimizes the nite di erences to reconstruct the
signal. This operation can be interpreted as a ltering operation that is performed in the
reconstruction steps. On the other hand, the pre- ltering method reconstructs ltered
versions of the image with CS and recombine their spectrum to obtain a better image
quality. This method relies on the fact that (high-pass) ltered versions of the images are
sparse in the pixel domain and can be reconstructed with CS using fewer measurements.
In this work, I use ltering methods with CS to improve the quality of the undersampled
MRI image reconstructions. The lters provide sparsity to the images, and generate
better CS reconstructions. In the pre- ltering methods, I proposed a systematical test
to evaluate a large number of lter banks, which were still not tested in the pre- ltering
literature. I also proposed a threshold method to include measurements in the solution
space, based on the stop-band of the lters. Finally, I proposed the ltering norms, a
method that uses lters in the reconstruction algorithm. This method generalizes the
TV minimization for any type of lter. I simulated the methods extensively for di erent
sampling density and on a large set of images, and use objective metrics to evaluate the
reconstruction quality. The pre- ltering, for low order lters designed with windowing
method obtained SNR values between 1 and 2.9 dB higher than the TV minimization.
Filtering norms with a combination of lters resulted in SNR values between 1.2 and
1.5 dB higher than values obtained with the TV. In most cases, the threshold method
improved the image quality results. However, the highest quality improvements were
observed for poor reconstructions. | pt_BR |
| dc.contributor.email | jonathanalis@gmail.com | pt_BR |
| dc.description.unidade | Instituto de Ciências Exatas (IE) | pt_BR |
| dc.description.unidade | Departamento de Ciência da Computação (IE CIC) | pt_BR |
| dc.description.ppg | Programa de Pós-Graduação em Informática | pt_BR |
| Aparece nas coleções: | Teses, dissertações e produtos pós-doutorado
|
