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Neuromuscular
Short report
Quantitative MRI outcome measures in CMT1A using automated lower limb muscle segmentation
  1. Luke F O'Donnell1,
  2. Menelaos Pipis1,
  3. John S Thornton1,
  4. Baris Kanber2,
  5. Stephen Wastling1,
  6. Amy McDowell1,
  7. Nick Zafeiropoulos1,
  8. Matilde Laura1,
  9. Mariola Skorupinska1,
  10. Christopher J Record1,
  11. Carolynne M Doherty1,
  12. David N Herrmann3,
  13. Henrik Zetterberg4,5,
  14. Amanda J Heslegrave4,5,
  15. Rhiannon Laban5,
  16. Alexander M Rossor1,
  17. Jasper M Morrow1,
  18. Mary M Reilly1
  1. 1 Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
  2. 2 UCL Centre for Medical Image Computing, London, UK
  3. 3 Department of Neurology, University of Rochester, Rochester, New York, USA
  4. 4 Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
  5. 5 UK Dementia Research Institute at UCL, London, UK
  1. Correspondence to Professor Mary M Reilly, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK; m.reilly{at}ucl.ac.uk

Abstract

Background Lower limb muscle magnetic resonance imaging (MRI) obtained fat fraction (FF) can detect disease progression in patients with Charcot-Marie-Tooth disease 1A (CMT1A). However, analysis is time-consuming and requires manual segmentation of lower limb muscles. We aimed to assess the responsiveness, efficiency and accuracy of acquiring FF MRI using an artificial intelligence-enabled automated segmentation technique.

Methods We recruited 20 CMT1A patients and 7 controls for assessment at baseline and 12 months. The three-point-Dixon fat water separation technique was used to determine thigh-level and calf-level muscle FF at a single slice using regions of interest defined using Musclesense, a trained artificial neural network for lower limb muscle image segmentation. A quality control (QC) check and correction of the automated segmentations was undertaken by a trained observer.

Results The QC check took on average 30 seconds per slice to complete. Using QC checked segmentations, the mean calf-level FF increased significantly in CMT1A patients from baseline over an average follow-up of 12.5 months (1.15%±1.77%, paired t-test p=0.016). Standardised response mean (SRM) in patients was 0.65. Without QC checks, the mean FF change between baseline and follow-up, at 1.15%±1.68% (paired t-test p=0.01), was almost identical to that seen in the corrected data, with a similar overall SRM at 0.69.

Conclusions Using automated image segmentation for the first time in a longitudinal study in CMT, we have demonstrated that calf FF has similar responsiveness to previously published data, is efficient with minimal time needed for QC checks and is accurate with minimal corrections needed.

  • MRI
  • NEUROPATHY

https://doi.org/10.1136/jnnp-2023-332454

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    Footnotes

    • AMR, JMM and MMR are joint senior authors.

    • LFO and MP are joint first authors.

    • LFO and MP contributed equally.

    • AMR, JMM and MMR contributed equally.

    • Contributors LFO’D and MP collected data, performed the primary analysis and wrote the manuscript. AMR, JMM and MMR contributed equally to this paper. AMR, JMM and MMR were responsible for the paper concept and design, data collection, primary analysis, writing the manuscript and edits. JST, BK, SW, AM, NZ, ML, MS, CJR, CMD, DNH, HZ, AJH and RL were responsible for data interpretation and edits.

    • Funding The study was funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (BRC) and British Medical Association (BMA) Vera Down grant.

    • Competing interests HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen and Roche, and is a cofounder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work).

    • Provenance and peer review Not commissioned; externally peer reviewed.