Topologically preserving straightening of spinal cord MRI

Leener, Benjamin De; Mangeat, Gabriel; Dupont, Sara M.; Martín, Allan; Callot, Virginie; Stikov, Nikola; Fehlings, Michael G.; Cohen‐Adad, Julien

doi:10.1002/jmri.25622

Cited by 22 publications

(20 citation statements)

References 40 publications

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“…The majority of slices were therefore acquired in a region where the spinal cord was relatively straight. Finally, the Spinal Cord Template could have been used to automatically straighten the cord and define ROIs and spinal levels, and should be considered in future relaxometry studies. Future work may also improve upon these measurements by acquiring data at a higher in‐plane resolution while increasing the number of acquisitions to partially compensate for the decrease in signal‐to‐noise ratio, and investigate possible asymmetric changes in T 2 * due to spinal cord pathology (e.g., in multiple sclerosis).…”

Section: Discussionmentioning

confidence: 99%

Measurement of T₂* in the human spinal cord at 3T

Barry

Smith

2019

Magnetic Resonance in Med

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Purpose To measure the transverse relaxation time T2* in healthy human cervical spinal cord gray matter (GM) and white matter (WM) at 3T. Methods Thirty healthy volunteers were recruited. Axial images were acquired using an averaged multi‐echo gradient‐echo (mFFE) T2*‐weighted sequence with 5 echoes. We used the signal equation for an mFFE sequence with constant dephasing gradients after each echo to jointly estimate the spin density and T2* for each voxel. Results No global difference in T2* was observed between all GM (41.3 ± 5.6 ms) and all WM (39.8 ± 5.4 ms). No significant differences were observed between left (43.2 ± 6.8 ms) and right (43.4 ± 5.5 ms) ventral GM, left (38.3 ± 6.1 ms) and right (38.6 ± 6.5 ms) dorsal GM, and left (39.4 ± 5.8 ms) and right (40.3 ± 5.8 ms) lateral WM. However, significant regional differences were observed between ventral (43.4 ± 5.7 ms) and dorsal (38.4 ± 6.0 ms) GM (p < 0.05), as well as between ventral (42.9 ± 6.5 ms) and dorsal (37.9 ± 6.2 ms) WM (p < 0.05). In analyses across slices, inferior T2* was longer than superior T2* in GM (44.7 ms vs. 40.1 ms; p < 0.01) and in WM (41.8 ms vs. 35.9 ms; p < 0.01). Conclusions Significant differences in T2* are observed between ventral and dorsal GM, ventral and dorsal WM, and superior and inferior GM and WM. There is no evidence for bilateral asymmetry in T2* in the healthy cord. These values of T2* in the spinal cord are notably lower than most reported values of T2* in the cortex.

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Section: Discussionmentioning

confidence: 99%

Measurement of T₂* in the human spinal cord at 3T

Barry

Smith

2019

Magnetic Resonance in Med

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“…Mean cross-sectional area (CSA) and volume (CSV) were calculated for each vertebra and for the C2-C3 pair (CSA23 and CSV23), given the better sensitivity of this combined level to disease severity (Coulon et al, 2002;Liu et al, 2015;Prados et al, 2016;De Leener et al, 2017b). CSA is computed by counting pixels in each slice and then geometrically adjusting it multiplying by the angle (in degrees) between the spinal cord centerline and the inferior-superior direction.…”

Section: Spinal Cord Analysismentioning

confidence: 99%

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Lorenzi

Palesi

Castellazzi

et al. 2020

Front. Cell. Neurosci.

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Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer's disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients' classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further

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“…Figure 2 details the post-processing pipeline to detect the PMJ. The SC is first straightened along the extracted centerline, with a robust algorithm that preserves the SC topology and its internal and adjacent structure (De Leener et al, 2017b). Straightening of the SC is used to normalize the shape of the PMJ regardless of the patient position during the MRI scan.…”

Section: Optimization Problem Resolutionmentioning

confidence: 99%

“…Localizing the SC on MRI scans is a key step for automating quantitative analysis pipelines such as SC (De Leener et al, 2014;Horsfield et al, 2010) and grey matter (Dupont et al, 2017;Prados et al, 2017Prados et al, , 2016 segmentations, template registration (De Leener et al, 2017b;Stroman et al, 2008) and B0 susceptibility-related distortion correction (Johanna Vannesjo et al, 2017;Topfer et al, 2016). While localizing the SC might appear as a rudimentary computerized task, it is much more challenging to achieve it robustly and accurately across a broad range of SC shapes, craniocaudal vertebral length, pathologies, image field of view (FOV), image resolution and orientation, types of contrast and image artifacts (e.g.…”

Section: Introductionmentioning

confidence: 99%

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization

Gros

Leener

Dupont

et al. 2018

Medical Image Analysis

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During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as "OptiC", from a large dataset involving 20 centers, 4 contrasts (T-weighted n = 287, T-weighted n = 120, T-weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value < .01). Images containing brain regions were identified with a 99% precision, on which brain and spine regions were separated with a distance error of 9.37 mm compared to ground-truth. Validation results on a challenging dataset suggest that OptiC could reliably be used for subsequent quantitative analyses tasks, opening the door to more robust analysis on pathological cases.

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Topologically preserving straightening of spinal cord MRI

Abstract: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1209-1219.

Cited by 22 publications

References 40 publications

Measurement of T₂* in the human spinal cord at 3T

Measurement of T₂* in the human spinal cord at 3T

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization

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Topologically preserving straightening of spinal cord MRI

Abstract: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1209-1219.

Cited by 22 publications

References 40 publications

Measurement of T2* in the human spinal cord at 3T

Measurement of T2* in the human spinal cord at 3T

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization

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Product

Resources

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Measurement of T₂* in the human spinal cord at 3T

Measurement of T₂* in the human spinal cord at 3T