Abstract
The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice. The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files. We present radtools, an R package for convenient extraction of medical image metadata. Radtools provides simple functions to explore and return metadata in familiar R data structures. For convenience, radtools also includes wrappers of existing tools for extraction of pixel data and viewing of image slices. The package is freely available under the MIT license at GitHub and is easily installable from the Comprehensive R Archive Network.
Highlights
We have created a new vignette demonstrating the convenience of radtools compared to achieving the same results with oro*, and in several cases, demonstrating useful radtools functionality that is not provided by oro* at all
Medical image analysis often lies at the boundary of research and the clinic, presenting challenges in both domains
Unlike DICOM, NIfTI-1 specifies a particular set of required header attributes, and the header conforms to a fixed size with an option to add extended header information
Summary
Response to Dr Volker Schmid: To demonstrate the value of radtools, we have created a new vignette (https://cran.r-project.org/web/packages/radtools/ vignettes/oro_compare.html) comparing radtools to existing stateof-the-art tools oro.dicom and oro.nifti, and have summarized this information in the “Use cases” section of the manuscript. We have created a new vignette (https://cran.r-project.org/web/packages/ radtools/vignettes/oro_compare.html) demonstrating the convenience of radtools compared to achieving the same results with oro*, and in several cases, demonstrating useful radtools functionality that is not provided by oro* at all. We summarize this information in a new paragraph in the “Use cases” section of the manuscript
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