RESUMO
Diffusion MRI (DMRI) plays an essential role in diagnosing brain disorders related to white matter abnormalities. However, it suffers from heavy noise, which restricts its quantitative analysis. The total variance (TV) regularization is an effective noise reduction technique that penalizes noise-induced variances. However, existing TV-based denoising methods only focus on the spatial domain, overlooking that DMRI data lives in a combined spatioangular domain. It eventually results in an unsatisfactory noise reduction effect. To resolve this issue, we propose to remove the noise in DMRI using graph total variance (GTV) in the spatioangular domain. Expressly, we first represent the DMRI data using a graph, which encodes the geometric information of sampling points in the spatioangular domain. We then perform effective noise reduction using the powerful GTV regularization, which penalizes the noise-induced variances on the graph. GTV effectively resolves the limitation in existing methods, which only rely on spatial information for removing the noise. Extensive experiments on synthetic and real DMRI data demonstrate that GTV can remove the noise effectively and outperforms state-of-the-art methods.
Assuntos
Encefalopatias/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética/estatística & dados numéricos , Neuroimagem/estatística & dados numéricos , Algoritmos , Biologia Computacional , Gráficos por Computador , Simulação por Computador , Bases de Dados Factuais/estatística & dados numéricos , Humanos , Cadeias de Markov , Imagens de Fantasmas , Razão Sinal-Ruído , Estatísticas não Paramétricas , Biologia Sintética/estatística & dados numéricosRESUMO
Just over two years ago there was an article in Nature entitled "Five Hard Truths for Synthetic Biology". Since then, the field has moved on considerably. A number of economic commentators have shown that synthetic biology very significant industrial potential. This paper addresses key issues in relation to the state of play regarding synthetic biology. It first considers the current background to synthetic biology, whether it is a legitimate field and how it relates to foundational biological sciences. The fact that synthetic biology is a translational field is discussed and placed in the context of the industrial translation process. An important aspect of synthetic biology is platform technology, this topic is also discussed in some detail. Finally, examples of application areas are described.