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OXSA:MATLAB 中的一个开源磁共振波谱分析工具箱。

OXSA: An open-source magnetic resonance spectroscopy analysis toolbox in MATLAB.

作者信息

Purvis Lucian A B, Clarke William T, Biasiolli Luca, Valkovič Ladislav, Robson Matthew D, Rodgers Christopher T

机构信息

Oxford Centre for Clinical Magnetic Resonance Research (OCMR), University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom.

Department of Imaging Methods, Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia.

出版信息

PLoS One. 2017 Sep 22;12(9):e0185356. doi: 10.1371/journal.pone.0185356. eCollection 2017.

Abstract

In vivo magnetic resonance spectroscopy provides insight into metabolism in the human body. New acquisition protocols are often proposed to improve the quality or efficiency of data collection. Processing pipelines must also be developed to use these data optimally. Current fitting software is either targeted at general spectroscopy fitting, or for specific protocols. We therefore introduce the MATLAB-based OXford Spectroscopy Analysis (OXSA) toolbox to allow researchers to rapidly develop their own customised processing pipelines. The toolbox aims to simplify development by: being easy to install and use; seamlessly importing Siemens Digital Imaging and Communications in Medicine (DICOM) standard data; allowing visualisation of spectroscopy data; offering a robust fitting routine; flexibly specifying prior knowledge when fitting; and allowing batch processing of spectra. This article demonstrates how each of these criteria have been fulfilled, and gives technical details about the implementation in MATLAB. The code is freely available to download from https://github.com/oxsatoolbox/oxsa.

摘要

体内磁共振波谱技术有助于深入了解人体新陈代谢。人们经常提出新的数据采集方案,以提高数据收集的质量或效率。同时,还必须开发处理流程,以便最佳地利用这些数据。当前的拟合软件要么针对一般波谱拟合,要么针对特定方案。因此,我们引入了基于MATLAB的牛津波谱分析(OXSA)工具箱,使研究人员能够快速开发自己的定制处理流程。该工具箱旨在通过以下方式简化开发过程:易于安装和使用;无缝导入西门子医学数字成像和通信(DICOM)标准数据;允许对波谱数据进行可视化;提供强大的拟合程序;在拟合时灵活指定先验知识;以及允许对波谱进行批量处理。本文展示了如何满足上述每一项标准,并给出了MATLAB实现的技术细节。代码可从https://github.com/oxsatoolbox/oxsa免费下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a6c/5609763/ec9375dc7ad0/pone.0185356.g001.jpg

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