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Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord., (© 2021. The Author(s).)

Faults in vascular (VN) and neuronal networks of spinal cord are responsible for serious neurodegenerative pathologies. Because of inadequate investigation tools, the lacking knowledge of the complete fine structure of VN and neuronal system represents a crucial problem. Conventional 2D imaging yields incomplete spatial coverage leading to possible data misinterpretation, whereas standard 3D computed tomography imaging achieves insufficient resolution and contrast. We show that X-ray high-resolution phase-contrast tomography allows the simultaneous visualization of three-dimensional VN and neuronal systems of ex-vivo mouse spinal cord at scales spanning from millimeters to hundreds of nanometers, with nor contrast agent nor sectioning and neither destructive sample-preparation. We image both the 3D distribution of micro-capillary network and the micrometric nerve fibers, axon-bundles and neuron soma. Our approach is very suitable for pre-clinical investigation of neurodegenerative pathologies and spinal-cord-injuries, in particular to resolve the entangled relationship between VN and neuronal system.

Functional magnetic resonance imaging (fMRI) techniques are widely exploited for the study of brain activation. In recent years, similar approaches have been attempted for the study of spinal cord function; however, obtaining good functional images of spinal cord still represents a technical and scientific challenge. Some of the main limiting factors can be classified under the broad category of "physiological noise," and are related to 1) the cerebrospinal fluid (CSF) flux in the subarachnoid space surrounding the spinal cord; 2) the cord motion itself; and 3) the small area of the cord, which makes it critical to have a high image resolution. In addition, the different magnetic susceptibility properties of tissues surrounding the spinal cord reduce the local homogeneity of the static magnetic field, causing image distortion, reduction of the effective resolution, and signal loss, all effects that are modulated by motion. For these reasons, a number of methods have been developed for the purpose of denoising spinal cord fMRI time series. In this work, after a short introduction on the relevant features of the spinal cord anatomy, we review the main sources of physiological noise in spinal cord fMRI and discuss the main approaches useful for its mitigation., (© 2013 Wiley Periodicals, Inc.)

Noninvasive functional studies on human spinal cord by means of magnetic resonance imaging (MRI) are gaining attention because of the promising applications in the study of healthy and injured central nervous system. The findings obtained are generally consistent with the anatomic knowledge based on invasive methods, but the origin and specificity of functional contrast is still debated. In this paper, a review of current knowledge and major issues about functional MRI (fMRI) in the human spinal cord is presented, with emphasis on the main methodological and technical problems and on forthcoming applications as clinical tool.

Abstract: Functional magnetic resonance imaging (fMRI) has emerged during the last decade as the main non-invasive technique for the investigation of human brain function. More recently, fMRI was also proposed for functional studies of the human spinal cord, but with controversial results. In fact, the functional contrast is not well-characterized, and even its origin has been challenged. In the present work, we characterized the temporal features of the functional signal evoked in the human spinal cord by a motor task, studied with an approach based on time-locked averaging of functional time series of different durations. Based on the results here reported, we defined an impulse-response function (irf) able to explain the functional response for motor tasks in the interval of 15-42 s of duration, thus suggesting the linearity of the phenomenon in this interval. Conversely, with stimulation durations ranging between 3 and 9 s, the functional signal was not detectable, and was under the level predicted by a linear behavior, suggesting deviation from linearity during short stimulations. The impulse-response function appeared slower than in the brain, peaking at about 9 s after its beginning. The observed contrast was generally larger than in the brain, on the order of about 5.4% of baseline signal at 1.5 T. The findings further suggested that the physiological origin of T 2⁎ weighted functional imaging is similar in the spinal cord and in the brain. [Copyright &y& Elsevier]

A correction to the article "Simultaneous submicrometric 3D imaging of the micro-vascular network and the neuronal system in a mouse spinal cord" that was published in the 2015 issue is presented.

Abstract: The number of functional magnetic resonance imaging (fMRI) studies performed on the human spinal cord (SC) has considerably increased in recent years. The lack of a validated processing pipeline is, however, a significant obstacle to the spread of SC fMRI. One component likely to be involved in any such pipeline is the process of SC masking, analogous to brain extraction in cerebral fMRI. In general, SC masking has been performed manually, with the incumbent costs of being very time consuming and operator dependent. To overcome these drawbacks, we have developed a tailored semiautomatic method for segmenting echoplanar images (EPI) of human spine that is able to identify the spinal canal and the SC. The method exploits both temporal and spatial features of the EPI series and was tested and optimized on EPI images of cervical spine acquired at 3 T. The dependence of algorithm performance on the degree of EPI image distortion was assessed by computing the displacement warping field that best matched the EPI to the corresponding high-resolution T2 images. Segmentation accuracy was above 80%, a significant improvement over values obtained with similar approaches, but not exploiting temporal information. Geometric distortion was found to explain about 50% of the variance of algorithm classification efficiency. [Copyright &y& Elsevier]