Compressive sensing using directional filters for magnetic resonance image reconstruction under different k-space trajectories

Dissertação (Mestrado em Engenharia Biomédica). Universidade de Brasília, Brasília, 2019.Nós vivemos na era do big data, da Internet das coisas, do aprimoramento genético e da recodificação do genoma. No entanto, visualizar os componentes internos do corpo humano e suas funcionalidades ainda ́e um desafio. Uma breve pesquisa sobre os artigos mais recentes publicados de 2018 a 2019 no campo da imagiologia médica com Ressonância Magnética (RM) nas bases de dados da Biblioteca Digital IEEE Xplore, PubMed e Web of Science, apresentou uma coleção de 50.574 estudos sobre deteção, segmentação, extração, delineação, diagnóstico e classifica ̧c ̃ao das patologias do corpo humano e características fisiológicas. A prevalência de tais estudos indica que os pesquisadores est ̃ao direcionando seus esforços para serem precisos e assertivos em relação ao diagnóstico, procedimentos e tratamentos. O imageamento por ressonância magnética (IRM) tem mostrado ser uma ferramenta poderosa e flexível capaz de gerar imagens de diferentes aspectos do corpo humano. O exame de RM ́e conhecido principalmente devido à sua superioridade no que diz respeito ao contraste do tecido, o que aumenta as chances de diagnóstico em compara ̧c ̃ao com outras técnicas como raios-X, radiografia e tomografia computadorizada (CT). Além disso, a RM permite o uso de várias técnicas e dispositivos auxiliares para adquirir imagens de alta resolução úteis em muitas fases da intervenção médica. Pesquisas recentes sobre IRM mostramavan ̧cos no diagnóstico precoce da síndrome de Sturge-Weber, a detec ̧c ̃ao de lipossarcoma mixóide metástases antes dos sintomas clínicos e metástases pulmonares, a predição de distúbios respiratórios em pacientes com Esclerose Lateral Amiotrófica (ELA), revelando atrofia medular cervical precoce, e a adequa ̧c ̃ao de IRM na triagem de câncer de pulmão e nódulos com diâmetros acima de 6 milímetros. Embora o cenário para o IRM seja promissor em relação a novas aplicações e técnicas relacionadas à escolha de pulsos RF e trajetórias de aquisição, o exame ainda enfrenta complicações e problemas relacionados à aceitação do paciente. Os exames leva de 45 a 60 minutos por parte do corpo, e o paciente tem que ficar parado por um longo período de tempo, frequentemente em posições muito desconfortáveis.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Recent research in Magnetic Resonance Imaging (MRI) shows improvements in diag- nostic and early diagnostic for a wide range of pathologies and the superiority of the contrast for softy tissues when compared to other imaging techniques shows the applica- bility and importance of MRI exams. Even though the promising research, the MRI exam still face obstacles due to the time required to scan specific areas of the body and how this problem escalates with pediatric and claustrophobic patients. Research in Compressive Sensing (CS) showed positive results in terms of diminishing the number of measures and thus the time required for scanning the human body. Although, these research showed positive results when implementing prefiltering with compressive sensing, they did not explore different scenarios, as directional filters and different trajectories. That being said, this research proposes five strategies in designing directional filters for prefiltering with Compressive Sensing. We show the mathematical steps adopted in each strategy and we reconstruct two phantoms and a real image of the head with several filters set for each scenario. We also give an special attention to the quality indexes used to assess image quality and what they actually measure in terms of image fidelity. All the images were recon- structed from simulated measures acquired in a radial and spiral k-space trajectory using an Iterative Reweighted Least Square (IRLS) algorithm with prefiltering. Finally, we showed that directional filters projected from ideal frequency response dis- tributions and windowed by Hann, Hamming, Blackman and rectangular window present better Signal-to-Error Ratio (SER) and Structural Similarity Index Measure (SSIM) re- sults for a real image of the head reconstruction when compared to the reconstruction of Haar filters

Context-Dependent Probabilistic Prior Information Strategy for MRI Reconstruction

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2021.Obtaining images from a Magnetic Resonance Imaging (MRI) scan is a challenging task due to the arduous process of obtaining the measurements from the machine and it is practically impossible to collect all the signal of a subject for a given scan. To mitigate this issue, Compressed Sensing (CS) based algorithms have been widely used in academia to achieve high-quality images with much fewer measurements needed. CS is capable of reconstructing MRI images at a sampling rate much lower than the Nyquist rate whilst maintaining sufficient quality. Since its introduction, CS has been significantly improved by the usage of preprocessing techniques like sparsifying filters and prior information, that are focused on improving the quality of the input data used in the CS algorithm. With that in mind, we have improved the prior information theory by utilizing non-deterministic support positions as well as multiple variances for the regions in the image that contain different levels of motion. This is the intuition behind our proposed method Context-Dependent Probabilistic Prior Information (CoDePPI) which parts from an image segmentation based on the motion of an image to address the different levels of confidence that a particular region in the image is part of a support position in other frames of a dynamic MRI. This makes our method more robust by minimizing the introduced error and by maximizing the probability to accurately use values from support regions. Our proposed method has shown better results in MRI reconstruction when compared to the classical prior information algorithm and non-prior information usage. Our method was evaluated in a dynamic cardiac MRI where we had four different motion levels regarding the movement in internal organs throughout the frames in the exam. Additionally, this research also produced Deep Learning (DL) content intended to be used in the improvement of CoDePPI by either utilizing Generative Adversarial Network (GAN)s for support positions generation from an image or by automatizing the segmentation step with a motion-detection model. A generation experiment was done to validate the usage of GANs for signal generation for future experimentation with MRI signal

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

3D spatio-temporal analysis for compressive sensing in magnetic resonance imaging of the murine cardiac cycle

This thesis consists of two major contributions, each of which has been prepared in a conference paper. These papers will be submitted for publication in the SPIE 2013 Medical Imaging Conference and the ASEE 2013 Annual Conference. The first paper explores a three-dimensional compressive sensing (CS) technique for reducing measurement time in MR imaging of the murine (mouse) cardiac cycle. By randomly undersampling a single 2D slice of a mouse heart at regular time intervals as it expands and contracts through the stages of a heartbeat, a CS reconstruction algorithm can be made to exploit transform sparsity in time as well as space. For the purposes of measuring the left ventricular volume in the mouse heart, this 3D approach offers significant advantages against classical 2D spatial compressive sensing. The second paper describes the modification and testing of a set of laboratory exercises for developing an undergraduate level understanding of Simulink. An existing partial set of lab exercises for Simulink was obtained and improved considerably in pedagogical utility, and then the completed set of pilot exercises was taught as a part of a communications course at the Missouri University of Science and Technology in order to gauge student responses and learning experiences. In this paper, the content of the laboratory exercises with corresponding educational approaches are discussed, along with student feedback and future improvements. --Abstract, page iv