Denoising spinal cord fMRI data: Approaches to acquisition and analysis

Falk Eippert, Yazhuo Kong, Mark Jenkinson, Irene Tracey, Jonathan C.W. Brooks

Research output: Contribution to journalArticle (Academic Journal)peer-review

20 Citations (Scopus)
358 Downloads (Pure)

Abstract

Functional magnetic resonance imaging (fMRI) of the human spinal cord is a difficult endeavour due to the cord's small cross-sectional diameter, signal drop-out as well as image distortion due to magnetic field inhomogeneity, and the confounding influence of physiological noise from cardiac and respiratory sources. Nevertheless, there is great interest in spinal fMRI due to the spinal cord's role as the principal sensorimotor interface between the brain and the body and its involvement in a variety of sensory and motor pathologies. In this review, we give an overview of the various methods that have been used to address the technical challenges in spinal fMRI, with a focus on reducing the impact of physiological noise. We start out by describing acquisition methods that have been tailored to the special needs of spinal cord fMRI and aim to increase the signal-to-noise ratio and reduce distortion in obtained images. Following this, we concentrate on image processing and analysis approaches that address the detrimental effects of noise. While these include variations of standard pre-processing methods such as motion correction and spatial filtering, the main focus lies on denoising techniques that can be applied to task-based as well as resting-state data sets. We review both model-based approaches that rely on externally acquired respiratory and cardiac signals as well as data-driven approaches that estimate and correct for noise using the data themselves. We conclude with an outlook on techniques that have been successfully applied for noise reduction in brain imaging and whose use might be beneficial for fMRI of the human spinal cord.
Original languageEnglish
Pages (from-to)255-266
Number of pages12
JournalNeuroImage
Volume154
Early online date28 Sep 2016
DOIs
Publication statusPublished - 1 Jul 2017

Structured keywords

  • Brain and Behaviour

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