Top

Published in:

01-11-2018 | Magnetic Resonance

Reliable and fast volumetry of the lumbar spinal cord using cord image analyser (Cordial)

Authors: Charidimos Tsagkas, Anna Altermatt, Ulrike Bonati, Simon Pezold, Julia Reinhard, Michael Amann, Philippe Cattin, Jens Wuerfel, Dirk Fischer, Katrin Parmar, Arne Fischmann

Published in: European Radiology | Issue 11/2018

Abstract

Objective

To validate the precision and accuracy of the semi-automated cord image analyser (Cordial) for lumbar spinal cord (SC) volumetry in 3D T1w MRI data of healthy controls (HC).

Materials and methods

40 3D T1w images of 10 HC (w/m: 6/4; age range: 18–41 years) were acquired at one 3T-scanner in two MRI sessions (time interval 14.9±6.1 days). Each subject was scanned twice per session, allowing determination of test-retest reliability both in back-to-back (intra-session) and scan-rescan images (inter-session). Cordial was applied for lumbar cord segmentation twice per image by two raters, allowing for assessment of intra- and inter-rater reliability, and compared to a manual gold standard.

Results

While manually segmented volumes were larger (mean: 2028±245 mm³ vs. Cordial: 1636±300 mm³, p<0.001), accuracy assessments between manually and semi-automatically segmented images showed a mean Dice-coefficient of 0.88±0.05. Calculation of within-subject coefficients of variation (COV) demonstrated high intra-session (1.22–1.86%), inter-session (1.26–1.84%), as well as intra-rater (1.73–1.83%) reproducibility. No significant difference was shown between intra- and inter-session reproducibility or between intra-rater reliabilities. Although inter-rater reproducibility (COV: 2.87%) was slightly lower compared to all other reproducibility measures, between rater consistency was very strong (intraclass correlation coefficient: 0.974).

Conclusion

While under-estimating the lumbar SCV, Cordial still provides excellent inter- and intra-session reproducibility showing high potential for application in longitudinal trials.

Key Points

• Lumbar spinal cord segmentation using the semi-automated cord image analyser (Cordial) is feasible.

• Lumbar spinal cord is 40-mm cord segment 60 mm above conus medullaris.

• Cordial provides excellent inter- and intra-session reproducibility in lumbar spinal cord region.

• Cordial shows high potential for application in longitudinal trials.

Kearney H, Miller DH, Ciccarelli O (2015) Spinal cord MRI in multiple sclerosis—diagnostic, prognostic and clinical value. Nat Rev Neurol 11:327–338. https://doi.org/10.1038/nrneurol.2015.80 CrossRefPubMed

Han J, Kaufman B, El Yousef S et al (1983) NMR imaging of the spine. Am J Roentgenol 141:1137–1145. https://doi.org/10.2214/ajr.141.6.1137 CrossRef

Modic MT, Weinstein MA, Pavlicek W et al (1983) Nuclear magnetic resonance imaging of the spine. Radiology 148:757–762. https://doi.org/10.1148/radiology.148.3.6878697 CrossRefPubMed

Norman D, Mills C, Brant-Zawadzki M et al (1983) Magnetic resonance imaging of the spinal cord and canal: potentials and limitations. Am J Roentgenol 141:1147–1152. https://doi.org/10.2214/ajr.141.6.1147 CrossRef

Coulon O, Hickman SJ, Parker GJ et al (2002) Quantification of spinal cord atrophy from magnetic resonance images via a B-spline active surface model. Magn Reson Med 47:1176–1185. https://doi.org/10.1002/mrm.10162 CrossRefPubMed

Hickman SJ, Hadjiprocopis A, Coulon O et al (2004) Cervical spinal cord MTR histogram analysis in multiple sclerosis using a 3D acquisition and a B-spline active surface segmentation technique. Magn Reson Imaging 22:891–895. https://doi.org/10.1016/j.mri.2004.01.056 CrossRefPubMed

Horsfield MA, Sala S, Neema M et al (2010) Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage 50:446–455. https://doi.org/10.1016/j.neuroimage.2009.12.121 CrossRefPubMedPubMedCentral

Mukherjee DP, Cheng I, Ray N et al (2010) Automatic Segmentation of spinal cord MRI using symmetric boundary tracing. IEEE Trans Inf Technol Biomed 14:1275–1278. https://doi.org/10.1109/TITB.2010.2052060 CrossRefPubMedPubMedCentral

Van Uitert R, Bitter I, Butman JA (2005) Semi-automatic spinal cord segmentation and quantification. Int Congr Ser 1281:224–229. https://doi.org/10.1016/j.ics.2005.03.181 CrossRef

10.

Carbonell-Caballero J, Manjón JV, Martí-Bonmatí L et al (2006) Accurate quantification methods to evaluate cervical cord atrophy in multiple sclerosis patients. MAGMA 19:237–246. https://doi.org/10.1007/s10334-006-0052-0 CrossRef

11.

Koh J, Kim T, Chaudhary V, Dhillon G (2010) Automatic segmentation of the spinal cord and the dural sac in lumbar MR images using gradient vector flow field. Conf Proc IEEE Eng Med Biol Soc 2010:3117–3120. https://doi.org/10.1109/IEMBS.2010.5626097 CrossRefPubMed

12.

Chen M, Carass A, Oh J et al (2013) Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view. Neuroimage 83:1051–1062. https://doi.org/10.1016/j.neuroimage.2013.07.060 CrossRefPubMed

13.

Fonov VS, Le Troter A, Taso M et al (2014) Framework for integrated MRI average of the spinal cord white and gray matter: the MNI–Poly–AMU template. Neuroimage 102(Part 2):817–827. https://doi.org/10.1016/j.neuroimage.2014.08.057 CrossRefPubMed

14.

Leener BD, Cohen-Adad J, Kadoury S (2015) Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Trans Med Imaging 34:1705–1718. https://doi.org/10.1109/TMI.2015.2437192 CrossRefPubMed

15.

Amann M, Pezold S, Naegelin Y et al (2016) Reliable volumetry of the cervical spinal cord in MS patient follow-up data with cord image analyzer (Cordial). J Neurol 263:1364–1374. https://doi.org/10.1007/s00415-016-8133-0 CrossRefPubMed

16.

Pezold S, Ammann M, Weier K, et al (2014) A semi-automatic method for the quantification of spinal cord atrophy. In: computational methods and clinical applications for spine imaging: proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan. Springer, Cham, p 143–155

17.

Pezold S, Fundana K, Amann M, et al (2015) Automatic segmentation of the spinal cord using continuous max flow with cross-sectional similarity prior and tubularity features. 107–118. doi:https://doi.org/10.1007/978-3-319-14148-0_10 CrossRef

18.

Pezold S, Horváth A, Fundana K, et al (2016) Automatic, robust, and globally optimal segmentation of tubular structures. In: medical image computing and computer-assisted intervention -- MICCAI 2016. Springer, Cham, p 362–370

19.

Papinutto N, Schlaeger R, Panara V et al (2015) Age, gender and normalization covariates for spinal cord gray matter and total cross-sectional areas at cervical and thoracic levels: a 2D phase sensitive inversion recovery imaging study. PLoS One 10:e0118576. https://doi.org/10.1371/journal.pone.0118576 CrossRefPubMedPubMedCentral

20.

Stroman PW, Wheeler-Kingshott C, Bacon M et al (2014) The current state-of-the-art of spinal cord imaging: methods. Neuroimage 84:1070–1081. https://doi.org/10.1016/j.neuroimage.2013.04.124 CrossRefPubMed

21.

Wheeler-Kingshott CA, Stroman PW, Schwab JM et al (2014) The current state-of-the-art of spinal cord imaging: applications. Neuroimage 84:1082–1093. https://doi.org/10.1016/j.neuroimage.2013.07.014 CrossRefPubMed

22.

Yiannakas MC, Kakar P, Hoy LR et al (2014) The use of the lumbosacral enlargement as an intrinsic imaging biomarker: feasibility of grey matter and white matter cross-sectional area measurements using MRI at 3T. PLoS One 9:e105544. https://doi.org/10.1371/journal.pone.0105544 CrossRefPubMedPubMedCentral

23.

Ge Y, Grossman RI, Babb JS et al (2002) Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol 23:1327–1333PubMed

24.

Terribilli D, Schaufelberger MS, Duran FLS et al (2011) Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol Aging 32:354–368. https://doi.org/10.1016/j.neurobiolaging.2009.02 CrossRefPubMedPubMedCentral

Title: Reliable and fast volumetry of the lumbar spinal cord using cord image analyser (Cordial)
Authors: Charidimos Tsagkas
Anna Altermatt
Ulrike Bonati
Simon Pezold
Julia Reinhard
Michael Amann
Philippe Cattin
Jens Wuerfel
Dirk Fischer
Katrin Parmar
Arne Fischmann
Publication date: 01-11-2018
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 11/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5431-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Reliable and fast volumetry of the lumbar spinal cord using cord image analyser (Cordial)

Abstract

Objective

Materials and methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Materials and methods

Results

Conclusion

Key Points

Please log in to get access to this content

Other articles of this Issue 11/2018

Feasibility of balanced steady-state free precession sequence at 1.5T for the evaluation of hepatic steatosis in obese children and adolescents

Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study

Quantitative MR spectroscopy reveals metabolic changes in the dorsolateral prefrontal cortex of patients with temporal lobe epilepsy

Pre-operative MRI staging of endometrial cancer in a multicentre cancer network: can we match single centre study results?

Stylus/tablet user input device for MRI heart wall segmentation: efficiency and ease of use

Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months