Elsevier

NeuroImage

Volume 145, Part A, 15 January 2017, Pages 24-43
NeuroImage

SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data

https://doi.org/10.1016/j.neuroimage.2016.10.009Get rights and content

Highlights

  • SCT (Spinal Cord Toolbox): Software package for processing spinal cord MRI data.

  • Features Templates & atlases of spinal cord, gray matter and white matter tracts.

  • State-of-the-art segmentation, registration and atlas-based analysis methods.

  • Open-source, extensive testing framework, documentation and support via forum.

  • Enables standardized, automatic, robust and reproducible multi-center studies of large datasets.

Abstract

For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures.

Introduction

Pathologies of the spinal cord can result from neurodegenerative and vascular diseases, disc degeneration, trauma and cancer, all of which can induce severe functional disabilities and neuropathic pain (Adams and Salam-Adams, 1991, Rowland et al., 2008). Precise assessment of the structural (e.g., extent of the lesion) and functional damage to the spinal cord is critical for informing on the prognosis and for guiding the intervention therapy program (Bozzo et al., 2011, van Middendorp et al., 2011). Moreover, the development of novel therapeutic approaches require objective biomarkers that can help assessing the efficiency and specific effect of these treatments, e.g., regeneration and axonal growth in spinal cord injury (Bradbury and McMahon, 2006) or remyelination in multiple sclerosis (Harlow et al., 2015, Luessi et al., 2014). While conventional Magnetic Resonance Imaging (MRI) based on relaxation parameters and proton density provides useful structural information that complement clinical and neurophysiological exams (Cadotte et al., 2011, Kearney et al., 2015), it can sometimes miss subtle pathological events such as Wallerian degeneration (Mac Donald et al., 2007, Zhang et al., 2009) or diffusely abnormal white matter in multiple sclerosis (Laule et al., 2011, West et al., 2014). The ongoing development of multi-parametric MRI (mpMRI), e.g., diffusion tensor imaging (DTI), magnetization transfer ratio (MTR) and functional MRI (fMRI) (Cohen-Adad, 2014, Tofts, 2003) provides a closer look at white matter microstructure and neuronal function and thus can more precisely characterize the pathological spinal cord (Martin et al., 2016a, Martin et al., 2016b).

While mpMRI has been used in the brain for several decades now, spinal cord imaging in research and clinics is still largely underutilized (Stroman et al., 2014, Wheeler-Kingshott et al., 2014). One reason is the difficulty in acquiring good quality data due to the numerous artifacts and the small cross-sectional size of the spinal cord. For the past few years though, researchers have developed methods to overcome these challenges, such as advanced coil designs (Cohen-Adad et al., 2011) and pulse sequences (Dowell et al., 2009, Finsterbusch, 2009, Wilm et al., 2007). A second reason is that, in contrast to the brain, fewer tools exist that are dedicated to processing of spinal cord images (Stroman et al., 2014). Unlike popular software packages for brain neuroimaging (e.g., FSL, SPM, BrainVoyager, FreeSurfer, AFNI, MINC Toolkit), spinal cord researchers have only been developing specific tools, e.g., for spinal cord segmentation (De Leener et al., 2016). Moreover, these single tools don’t provide an integrative framework that is compatible with most usages (i.e., adapted to a large variety of mpMRI protocols) and that can reach the community at large (i.e., open source, licence-free scripting environment, extensive documentation, support forum, continuous integration service for integrity testing). At the same time, brain software packages are not optimized for spinal cord images because (i) the spine is an articulated structure, therefore standard motion-correction algorithms assuming rigid or affine transformation are inadequate, (ii) brain extraction and segmentation tools are not adapted to the spinal cord because of different shape, contrast-to-noise ratio, etc., (iii) common brain MRI templates (Evans et al., 1992, Fonov et al., 2011) and atlases (Brodmann, 1909) are de facto not usable for spinal cord imaging, (iv) useful features are de facto missing, such as spinal cord cross-sectional area (CSA) measurement. The lack of a standard processing platform has had negative impacts on the spinal cord neuroimaging community as it has limited the ability of researchers to compare and reproduce published results, as well at to conduct collaborative and multi-center studies.

In this paper we introduce the Spinal Cord Toolbox (SCT), a software package specifically designed to process spinal cord mpMRI data and to perform atlas-based analysis. SCT includes state-of-the-art tools, which have been validated in this manuscript (see Appendix) and/or are already published (see list of publications in the next section). SCT is compatible with any scanner brand as it uses NIfTI format and is designed to accept a variety of sequences, modalities and contrasts. Being based on Python language, SCT ensures cross-platform compatibility, is free and open source. The article is organized as follows. First, an overview of the software is presented, along with a description of the main features. Then, technical details are presented (e.g., coding language, installation) followed by example applications and a discussion.

Section snippets

Main features

SCT is a comprehensive and open-source library of analysis tools for multi-parametric MRI of the spinal cord. The primary objective of SCT is to provide a common pre-processing platform, which supplements what is missing from the common brain software package. Hence, the goal of SCT is not to replace entirely what other software already offers (e.g., first- and second-level analysis of fMRI data), but to provide the necessary tools to pre-process mpMRI data, to perform group analysis within

Licence, language and dependences

SCT is an open-source project that falls under the MIT license3. SCT is written in Python and has been designed in an object-oriented programming fashion in order to improve modularity and extensibility. SCT tools are available via two interfaces: (i) a command-line software, meaning that tools can be called within a Unix terminal, and (ii) a Python library called within Python code.

Dependent Python libraries include: nibabel4 for reading/writing

Analysis of mpMRI data

An adult subject was scanned at 3 T (TIM Trio, Siemens Healthcare) using the following sequences: (i) 3D T2-weighted fast spin echo, (ii) gradient echo FLASH with and without magnetization transfer and (iii) diffusion-weighted EPI (b-value = 800 s/mm2). For details on sequence parameters see Fonov et al. (2014). Processing included spinal cord segmentation, vertebral labeling, gray matter segmentation, cross-sectional measurement, registration to template, motion correction using SliceReg and

Discussion

SCT is an open-source image processing software dedicated to spinal cord mpMRI data. SCT includes state-of-the-art MRI templates and atlases of the spinal cord internal structure, robust methods to register new data to the template and motion correction methods for diffusion and functional time series.

Conclusion

SCT is a comprehensive software package dedicated to the processing of mpMRI data of the spinal cord. SCT is tailored towards standardization and automation of processing pipeline (intuitive batch scripts), versatility (user-oriented development of new features) modularity (possibility to reuse some SCT functions or to contribute to new features) and wide distribution (open-source, extensive testing framework, active support via forum). Preliminary applications of SCT cover a variety of

Acknowledgments

The authors would like to acknowledge all core contributors of SCT: Tanguy Duval, Charley Gros, Pierre-Olivier Quirion, Julien Touati, Augustin Roux, Tanguy Magnan, Olivier Comtois, Geoffrey Lévêque, Marc Benhamou and all collaborators who contributed to some of the tools: Drs. Michael Fehlings, Allan Martin, David Cadotte, Adam Cadotte, Brian Avants, Manuel Taso, Arnaud Le Troter and Michael Sdika. The following people are acknowledged for useful discussions: Drs. Pierre Bellec, Eleftherios

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