Herz Christian, Fillion-Robin Jean-Christophe, Onken Michael, Riesmeier Jörg, Lasso Andras, Pinter Csaba, Fichtinger Gabor, Pieper Steve, Clunie David, Kikinis Ron, Fedorov Andriy
Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts.
Harvard Medical School, Harvard University, Boston, Massachusetts.
Cancer Res. 2017 Nov 1;77(21):e87-e90. doi: 10.1158/0008-5472.CAN-17-0336.
Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi .
临床图像数据的定量分析是一个活跃的研究领域，有望推动精准医学、治疗反应的早期评估以及疾病的客观特征描述。鉴于所提出的定量分析方法数量呈爆炸式增长，互操作性、数据共享以及挖掘所得数据的能力变得越来越重要。医学数字成像和通信（DICOM）标准在放射学中被广泛用于图像和元数据。（用于定量成像的DICOM）是一个免费的开源库，可将常用研究格式存储的数据转换为标准DICOM表示形式。源代码根据BSD风格的许可进行分发。它以预编译二进制包的形式免费提供给每个主流操作系统，也作为Docker镜像以及3D Slicer的扩展。安装和使用说明在GitHub仓库https://github.com/qiicr/dcmqi中提供。
