Radtools: R utilities for convenient extraction of medical image metadata.

Pamela H Russell,Debashis Ghosh

doi:10.12688/f1000research.17139.2

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 Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts.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 https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network ( https://cran.r-project.org/package=radtools).

Highlights

R EV IS E D Amendments from Version 1. We thank both referees for their thoughtful comments. Both reports identified a lack of clarity around the specific value proposition of radtools, and highlighted the need for further explanation of its relationship to existing tools such as oro.dicom and oro.nifti
Medical image analysis often lies at the boundary of research and the clinic, presenting challenges in both domains
Several excellent image processing and analysis packages exist for the R environment[2,7,8,9,10], none currently offers special functionality for convenient presentation of image metadata; these tools generally present metadata in a form closely parallel to its original encoding

Summary

24 Dec 2018 report report report report report report

This article is included in the Neuroconductor collection We thank both referees for their thoughtful comments. Overall, both reports identified a lack of clarity around the specific value proposition of radtools, and highlighted the need for further explanation of its relationship to existing tools such as oro.dicom and oro.nifti. We have made a change to the package regarding support for DICOM objects that do not include pixel data such as SR objects. As these objects still contain useful metadata, we have added support for them, and included an SR object in our unit tests. We have modified the title to be more specific and removed the subjective word “smooth” from the manuscript; the language has been modified in the package description and documentation on CRAN

Introduction

Methods

Conclusions

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