Normalization of Spinal Cord Total Cross-Sectional and Gray Matter Areas as Quantified With Radially Sampled Averaged Magnetization Inversion Recovery Acquisitions

Kesenheimer, Eva; Wendebourg, Maria Janina; Weigel, Matthias; Weidensteiner, Claudia; Haas, Tanja; Richter, Laura; Sander, Laura; Horvath, Antal; Baraković, Muhamed; Cattin, Philippe C.; Granziera, Cristina; Bieri, Oliver; Schlaeger, Regina

doi:10.3389/fneur.2021.637198

Cited by 11 publications

(43 citation statements)

References 33 publications

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“…We obtained mean CSA values of 66.2 mm 2 and 66.4 mm 2 for PMJ-based and vertebral-based CSA respectively, which is lower than what was reported in other studies (Kesenheimer et al, 2021; Nico Papinutto et al, 2020; Solstrand Dahlberg et al, 2020). Lower values could be explained by various factors.…”

Section: Discussioncontrasting

confidence: 84%

“…A strong correlation was also found between thalamus volume and SC CSA by Solstrand Dahlberg et al (2020). Axial canal area was also a significant factor and promising for normalization strategies, but has not been explored yet by many (Kesenheimer et al, 2021; Nico Papinutto et al, 2020). A notable difficulty for computing the axial canal area is the ability to properly segment it.…”

Section: Introductionmentioning

confidence: 94%

“…Intracranial volume was also considered for normalization of SC volume but had limited utility since it generally diminished the ability to detect clinical-radiological correlations (Healy et al, 2012; Oh et al, 2014). Since SC CSA is a useful metric to assess SC atrophy and brain volume changes have also been associated with those pathologies, intracranial volume would be a better factor to consider for normalization strategies (Kesenheimer et al, 2021). A strong correlation was also found between thalamus volume and SC CSA by Solstrand Dahlberg et al (2020).…”

Section: Introductionmentioning

confidence: 99%

“…Brain WM volume, sex and spinal canal area formed a relevant normalization strategie, total intracranial volume could also replace brain WM volume for subjet with diseases that affect WM. The main limitation here was the relatively small number of participants (N=61) (Kesenheimer et al, 2021). As we can observe, brain/skull metrics and sex are important factors to consider in a possible normalization method.…”

Section: Introductionmentioning

confidence: 99%

“…While SC CSA variability across participants was shown to be associated with multiple demographic, anatomical and biological factors (Kesenheimer et al, 2021; Nico Papinutto et al, 2015, 2020; Solstrand Dahlberg et al, 2020), no previous studies have addressed the variability associated with limitation of vertebral based SC CSA measurement in the search for a normalization method.…”

Section: Introductionmentioning

confidence: 99%

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Normalizing automatic spinal cord cross-sectional area measures

Bédard¹,

Cohen‐Adad

2021

Preprint

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Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in various neurodegenerative diseases. However, the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models were based on a relatively limited number of participants (typically < 300 participants), required manual intervention, and were not implemented in an open-access comprehensive analysis pipeline. Another limitation is related to the imprecise prediction of the spinal levels when using vertebral levels as a reference; a question never addressed before in the search for a normalization method. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated various factors to explain variability, and we developed normalization strategies on a large cohort (N=804). Cervical spinal cord CSA was computed on T1w MRI scans for 804 participants from the UK Biobank database. In addition to computing cross-sectional at the C2-C3 vertebral disc, it was also measured at 64 mm caudal from the PMJ. The effect of various biological, demographic and anatomical factors was explored by computing Pearson's correlation coefficients. A stepwise linear regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. The correlation between CSA measured at C2-C3 and using the PMJ was y = 0.98x + 1.78 (R2 = 0.97). The best normalization model included thalamus volume, brain volume, sex and interaction between brain volume and sex. With this model, the coefficient of variation went down from 10.09% (without normalization) to 8.59%, a reduction of 14.85%. In this study we identified factors explaining inter-subject variability of spinal cord CSA over a large cohort of participants, and developed a normalization model to reduce the variability. We implemented an approach, based on the PMJ, to measure CSA to overcome limitations associated with the vertebral reference. This approach warrants further validation, especially in longitudinal cohorts. The PMJ-based method and normalization models are readily available in the Spinal Cord Toolbox.

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Section: Discussioncontrasting

confidence: 84%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Normalizing automatic spinal cord cross-sectional area measures

Bédard¹,

Cohen‐Adad

2021

Preprint

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Feasibility of interleaved multislice averaged magnetization inversion‐recovery acquisitions of the spinal cord

Weigel,

Celicanin,

Haas

et al. 2024

Magnetic Resonance in Med

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PurposeTo establish an interleaved multislice variant of the averaged magnetization inversion‐recovery acquisitions (AMIRA) approach for 2D spinal cord imaging with increased acquisition efficiency compared with the conventional 2D single‐slice approach(es), and to determine essential prerequisites for a working interleaved multislice AMIRA approach in practice.MethodsThe general AMIRA concept is based on an inversion recovery–prepared, segmented, and time‐limited cine balanced SSFP sequence, generating images of different contrast. For AMIRA imaging of multiple, independent slices in a 2D interleaved fashion, a slice loop within the acquisition loops was programmed. The former non‐selective inversions were replaced with slice‐selective inversions with user‐definable slice thickness.ResultsThe thickness of the slice‐selective inversion in 2D interleaved multislice AMIRA should be doubled compared with the manufacturer's standard setting to avoid an increased sensitivity to flow and pulsation effects particularly in the CSF. However, this solution also limits its practical applicability, as slices located at directly adjacent vertebrae cannot be imaged together. Successful interleaved two‐slice AMIRA imaging for a “reference” in vivo protocol with 0.50 × 0.50 mm2 in‐plane resolution and 8‐mm slice thickness is demonstrated, therefore halving its acquisition time per slice from 3 min down to 1.5 min.ConclusionThe investigated 2D interleaved two‐slice AMIRA variant facilitates spinal cord imaging that maintains similar contrast and the same resolution as the conventional 2D single‐slice AMIRA approach, but does so with a halved acquisition time.

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