Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data
Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data
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DOI：
10.1016/j.neuroimage.2014.10.026
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发表时间：
2015-01-15
期刊：
影响因子：
5.7
通讯作者：
Thiran, Jean-Philippe
中科院分区：
文献类型：
--
作者：
Daducci, Alessandro;Canales-Rodriguez, Erick J.;Thiran, Jean-Philippe
扩散磁共振(MR)数据的微结构成像是非侵入性研究组织形态和提供对其微结构组织的生物学洞察的宝贵工具。近年来，已经提出了各种生物物理模型来将测量信号中观察到的特定模式与神经元组织的特定微结构属性相关联，如轴突直径和纤维密度。尽管非常吸引人的结果表明，估计的微结构指数与组织学检查非常吻合，但现有技术需要计算非常昂贵的非线性程序来使模型与数据相匹配，这在实践中需要使用强大的计算机集群来进行大规模应用。在这项工作中，我们提出了一个基于凸优化的加速微结构成像的通用框架(AMICO)，并展示了如何将这类技术重新表述为方便的线性系统，然后，可以使用非常快的算法高效地求解。我们演示了两个特定模型(即ActiveAx和Node Di)的拟合问题的线性化，为这些技术中的参数估计提供了一个非常有吸引力的替代方案；然而，Amico框架足够通用和灵活，也可以用于更广泛的微结构成像方法。结果表明，AMICO是一种有效的方法，可以大大加快现有技术的拟合速度(快四个数量级)，同时保持估计模型参数的准确性和精确度(相关系数在0.9以上)。我们相信，这种超快算法的可获得性将有助于加速微结构成像向更大范围的患者队列的传播，并研究更广泛的神经疾病。(C)2014年提交人。由爱思唯尔公司出版。
Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders. (C) 2014 The Authors. Published by Elsevier Inc.