Radtools: R utilities for convenient extraction of medical image metadata

Introduction

Medical image analysis often lies at the boundary of research and the clinic, presenting challenges in both domains. Institutional and privacy concerns can compete with the objective of open data for research purposes. In particular, it remains standard practice to perform analysis with proprietary software or unpublished scripts. Additionally, the majority of imaging studies do not make image data publically available due to patient privacy requirements. These complex challenges can present barriers for scientists working in the image analysis domain.

In recent years, a small but growing number of open source computational tools have been developed to process and analyze medical images, promoting sharing of code; some of the most widely adopted are described in 1–3. To address the issue of availability of public image data, our group previously developed TCIApathfinder⁴, an open source R package to simplify access to the thousands of publicly available images in The Cancer Imaging Archive⁵. Here, we present radtools⁶, an open source R package that lowers barriers to image analysis by simplifying the extraction of image properties and complex header information. Although several excellent image processing and analysis packages exist for the R environment^2,7–10, 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. Radtools⁶ specifically addresses the complexity of image metadata, improving upon metadata extraction methods in existing packages. The package implements a layer of processing to convert image metadata to familiar R data structures, eliminating the need for specialized knowledge and custom code to dig into metadata.

Radtools⁶ supports the two most common medical image formats, DICOM (Digital Imaging and Communication in Medicine)¹¹ and NIfTI-1 (Neuroimaging Informatics Technology Initiative)¹². The industry standard DICOM format combines a header and two-dimensional pixel data into one file, so that an image acquisition typically produces multiple DICOM files. (Some valid DICOM objects do not contain pixel data; these are still supported by radtools.) DICOM header fields consist of a “tag” that identifies the attribute, followed by the attribute value. There is no fixed size for a DICOM header; any number of thousands of possible attributes may be included. Each DICOM file for an acquisition contains its own header; many attributes will be constant across image slices. NIfTI-1 format was developed primarily for multidimensional imaging data as an improvement over the previous ANALYZE format¹³. NIfTI-1 combines header information and the entire multidimensional image acquisition into either a single file or two files (one header file and one image file). 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. Radtools⁶ provides simple functions to explore and return image properties and header data from both image formats in familiar R data structures. For convenience, radtools⁶ also provides wrappers around existing methods for extraction of pixel data and viewing of image slices.

Methods

Use cases

Radtools⁶ can extract image properties and header data from any valid DICOM or NIfTI-1 file. Image datasets are loaded with the `read_dicom` and `read_nifti1` functions. Several generic functions extract attributes from either data type, including `img_dimensions`, `num_slices`, `header_fields`, which reports the set of header fields present, and `header_value`, which returns the value(s) of a particular attribute. Additionally, functions are provided to specifically address one format or the other. All header data present in a DICOM acquisition can be extracted into a matrix, where rows are attributes and columns are slices, with the `dicom_metadata_matrix` function. As most DICOM headers contain numerous attributes and many of these are constant across all slices, the `dicom_constant_header_values` function produces a named list of common attributes across slices. NIfTI-specific functions include `nifti1_num_dim`, which returns the number of dimensions, and `nifti1_header_values`, which returns a named list of all metadata attributes for the image.

The image itself can be extracted as a multidimensional matrix of intensities for either file format with `img_data_to_mat`. Image slices can be visualized with `view_slice`.

Finally, functions are provided to explore aspects of the DICOM standard itself. The functions `dicom_all_valid_header_tags`, `dicom_all_valid_header_names`, and `dicom_all_valid_header_keywords` return complete lists of valid DICOM header attributes. The functions `dicom_search_header_names` and `dicom_search_header_keywords` return attributes matching a search term.

For a demonstration of package usage including examples with publically available data, see the package vignette available at https://cran.r-project.org/web/packages/radtools/vignettes/radtools_usage.html.

Conclusions

Radtools⁶ fills a specific need in the existing ecosystem of R packages for image processing and analysis: namely, the need for convenient extraction of image metadata. The package will accelerate workflow development and provide researchers with easy access to attributes that they may not have otherwise considered using. The inclusion of the package on CRAN, along with clear documentation, make it trivially simple for R users to obtain and begin using radtools.

Data availability

No data are associated with this article.

Software availability

Radtools can be installed with the R command “install.packages(“radtools”).

Radtools is available from CRAN: https://cran.r-project.org/package=radtools.

Source code available from: https://github.com/pamelarussell/radtools.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.2542069¹⁴.

License: MIT License.