Imaging of Spinal Cord Injury: Acute Cervical Spinal Cord Injury, Cervical Spondylotic Myelopathy, and Cord Herniation

Talekar, Kiran; Poplawski, Michael; Hegde, Rahul; Cox, Mougnyan; Flanders, Adam E.

doi:10.1053/j.sult.2016.05.007

Cited by 19 publications

(9 citation statements)

References 90 publications

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“…SC segmentation for acute SCI is particularly challenging given the high frequency of SC distortion related to compression and associated geometric distortion as well as heterogeneous intramedullary signal abnormality. 2,29,30 Our targeted, disease-specific approach to network training likely, in part, explains performance differences between BASICseg and Deepseg algorithms for our SCI cohort. All CNN-based algorithms (BASICseg1-3 and Deepseg) outperformed Propseg; this difference highlights the value of CNN applications for SC segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…Current standard of care for MR imaging evaluation of trau-matic SCI largely relies on subjective and qualitative descriptions of MR imaging findings such as the presence or absence of SC edema and hemorrhage. 2,4,31 Thus, few validated MR imaging biomarkers for SC injury stratification and prognosis have been described, despite 4 decades of MR imaging clinical application for SCI. 5,6 In addition to development of improved quantitative imaging sequences, advanced, nonbiased, and automated imageanalysis techniques may prove useful to accelerate robust MR imaging biomarker identification.…”

Section: Discussionmentioning

confidence: 99%

“…All MR imaging studies were performed on a single 3T Magnetom Skyra scanner with software Version E11 (Siemens, Erlangen, Germany). Only the axial and sagittal T2 fast spin-echo sequences from the routine cervical spine trauma protocol were used for image analysis and were performed with the following parameters: axial T2; TR ϭ 3870 Ϯ 400 ms, TE ϭ 96 Ϯ 4 ms, slice thickness ϭ 3 mm, echo-train length ϭ 16, FOV ϭ 240 ϫ 120 mm, nominal in-plane pixel size ϭ 0.47 mm 2 . Additional sequences performed as part of the routine clinical spine MR imaging protocol were not evaluated for this study.…”

Section: Mr Imaging Acquisition Parametersmentioning

confidence: 99%

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Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

McCoy

Gros

Cohen‐Adad

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Our aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR imaging in a cohort of patients with acute spinal cord injury. MATERIALS AND METHODS: Forty-seven patients who underwent 3T MR imaging within 24 hours of spinal cord injury were included. We developed an image-analysis pipeline integrating 2D convolutional neural networks for whole spinal cord and intramedullary spinal cord lesion segmentation. Linear mixed modeling was used to compare test segmentation results between our spinal cord injury convolutional neural network (Brain and Spinal Cord Injury Center segmentation) and current state-of-the-art methods. Volumes of segmented lesions were then used in a linear regression analysis to determine associations with motor scores. RESULTS: Compared with manual labeling, the average test set Dice coefficient for the Brain and Spinal Cord Injury Center segmentation model was 0.93 for spinal cord segmentation versus 0.80 for PropSeg and 0.90 for DeepSeg (both components of the Spinal Cord Toolbox). Linear mixed modeling showed a significant difference between Brain and Spinal Cord Injury Center segmentation compared with PropSeg (P Ͻ .001) and DeepSeg (P Ͻ .05). Brain and Spinal Cord Injury Center segmentation showed significantly better adaptability to damaged areas compared with PropSeg (P Ͻ .001) and DeepSeg (P Ͻ .02). The contusion injury volumes based on automated segmentation were significantly associated with motor scores at admission (P ϭ .002) and discharge (P ϭ .009). CONCLUSIONS: Brain and Spinal Cord Injury Center segmentation of the spinal cord compares favorably with available segmentation tools in a population with acute spinal cord injury. Volumes of injury derived from automated lesion segmentation with Brain and Spinal Cord Injury Center segmentation correlate with measures of motor impairment in the acute phase. Targeted convolutional neural network training in acute spinal cord injury enhances algorithm performance for this patient population and provides clinically relevant metrics of cord injury. ABBREVIATIONS: BASICseg ϭ Brain and Spinal Cord Injury Center segmentation; CNN ϭ convolutional neural network; SC ϭ spinal cord; SCI ϭ spinal cord injury; SCT ϭ Spinal Cord Toolbox T he natural history of recovery after spinal cord injury (SCI) is highly variable and often difficult to predict. 1,2 Early objective neurologic assessment of injury severity in these patients is challenging due to several confounding factors, such as traumatic brain injury, severe pain, intubation, spinal shock, and sedative medications, among others. 2,3 Early and accurate classification of patients with SCI as close to the time of injury as possible is imperative for the guidance of triage, acute management, prognostication, and for patient selection in the design of clinical trials in which novel therapies are tested. 2,3 MR imaging is the criterion standard imaging technique for evaluation of the ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Mr Imaging Acquisition Parametersmentioning

confidence: 99%

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Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

McCoy

Gros

Cohen‐Adad

et al. 2019

AJNR Am J Neuroradiol

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“…1,2 The most obvious difference between a traumatic and non-traumatic cervical myelopathy lies in the time profile of neural changes (acute onset in tSCI vs. slowly developing symptoms in DCM). [3][4][5][6] Due to progressive impairment of gait and the increasing risk of falls, DCM patients can develop a central cord syndrome, which per definition is a tSCI. 7 Experimental evidence suggests that tSCI and DCM share several aspects of myelopathy with a combination of alpha-motoneuron damage (lesion of the central gray), 8 demyelination, [9][10][11][12] and axonal damage of long projecting spinal nerve fiber tracts (white matter damage), 13,14 as well as edema and ischemic changes.…”

Section: Introductionmentioning

confidence: 99%

Cervical Cord Neurodegeneration in Traumatic and Non-Traumatic Spinal Cord Injury

Seif

Dávid

Huber

et al. 2020

Journal of Neurotrauma

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This study aimed to compare macrostructural and microstructural neurodegenerative changes remote from a cervical spinal cord injury in traumatic spinal cord injury (tSCI) and degenerative cervical myelopathy (DCM) patients using quantitative magnetic resonance imaging (MRI). Twenty-nine tSCI patients, 20 mild/moderate DCM patients, and 22 healthy controls underwent a high-resolution MRI protocol at the cervical cord (C2/C3). High-resolution T2*-weighted and diffusion-weighted scans provided data to calculate tissue-specific cross-sectional areas of the spinal cord and tractspecific diffusion indices of cord white matter, respectively. Regression analysis determined associations between neurodegeneration and clinical impairment. tSCI patients showed more impairment in upper limb strength and manual dexterity when compared with DCM patients. While macrostructural MRI measures revealed a similar extent of remote cord atrophy at cervical level, microstructural measures (diffusion indices) were able to distinguish more pronounced tract-specific neurodegeneration in tSCI patients when compared with DCM patients. Tract-specific neurodegeneration was associated with upper limb impairment. Despite clinical differences between severely impaired tSCI compared with mildly affected DCM patient, extensive cord atrophy is present remotely from the focal spinal cord injury. Diffusion indices revealed greater tract-specific alterations in tSCI patients. Therefore, diffusion indices are more sensitive than macrostructural MRI measures as these are able to distinguish between traumatic and non-traumatic spinal cord injury. Neuroimaging biomarkers of cervical cord integrity hold potential as predictors of recovery and might be suitable biomarkers for interventional trials both in traumatic and non-traumatic SCI.

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“…The segmental levels of both cranial and caudal involvement (CCI) are reference measures used to locate the upper and lower anatomical bounds of edema [11,17]. Edema length (EL) has been commonly measured and reported as the extent of edema along the cranial-caudal axis [7, 9-11, 13-15, 18].…”

Section: Introductionmentioning

confidence: 99%

Establishing the inter-rater reliability of spinal cord damage manual measurement using magnetic resonance imaging

Cummins

Connor

Heller

et al. 2019

Spinal Cord Ser Cases

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Study design Retrospective study. Objectives To establish the inter-rater reliability in the quantitative evaluation of spinal cord damage following cervical incomplete spinal cord injury (SCI) utilizing magnetic resonance imaging (MRI). MRI was used to perform manual measurements of the cranial and caudal boundaries of edema, edema length, midsagittal tissue bridge ratio, axial damage ratio, and edema volume in 10 participants with cervical incomplete SCI. Setting Academic university setting. Methods Structural MRIs of 10 participants with SCI were collected from Northwestern University's Neuromuscular Imaging and Research Lab. All manual measures were performed using OsiriX (Pixmeo Sarl, Geneva, Switzerland). Intraclass correlation coefficients (ICC) were used to determine inter-rater reliability across seven raters of varying experience.Results High-to-excellent inter-rater reliability was found for all measures. ICC values for cranial/caudal levels of involvement, edema length, midsagittal tissue bridge ratio, axial damage ratio, and edema volume were 0.99, 0.98, 0.90, 0.84, and 0.93, respectively. Conclusions Manual MRI measures of spinal cord damage are reliable between raters. Researchers and clinicians may confidently utilize manual MRI measures to quantify cord damage. Future research to predict functional recovery following SCI and better inform clinical management is warranted.

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Imaging of Spinal Cord Injury: Acute Cervical Spinal Cord Injury, Cervical Spondylotic Myelopathy, and Cord Herniation

Cited by 19 publications

References 90 publications

Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

Convolutional Neural Network–Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury

Cervical Cord Neurodegeneration in Traumatic and Non-Traumatic Spinal Cord Injury

Establishing the inter-rater reliability of spinal cord damage manual measurement using magnetic resonance imaging

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