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Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases

  • Magnetic Resonance
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Abstract

Objectives

Magnetic resonance imaging (MRI) constitutes a powerful outcome measure in neuromuscular disorders, yet there is a broad diversity of approaches in data acquisition and analysis. Since each neuromuscular disease presents a specific pattern of muscle involvement, the recommended analysis is assumed to be the muscle-by-muscle approach. We, therefore, performed a comparative analysis of different segmentation approaches, including global muscle segmentation, to determine the best strategy for evaluating disease progression.

Methods

In 102 patients (21 immune-mediated necrotizing myopathy/IMNM, 21 inclusion body myositis/IBM, 10 GNE myopathy/GNEM, 19 Duchenne muscular dystrophy/DMD, 12 dysferlinopathy/DYSF, 7 limb-girdle muscular dystrophy/LGMD2I, 7 Pompe disease, 5 spinal muscular atrophy/SMA), two MRI scans were obtained at a 1-year interval in thighs and lower legs. Regions of interest (ROIs) were drawn in individual muscles, muscle groups, and the global muscle segment. Standardized response means (SRMs) were determined to assess sensitivity to change in fat fraction (ΔFat%) in individual muscles, muscle groups, weighted combinations of muscles and muscle groups, and in the global muscle segment.

Results

Global muscle segmentation gave high SRMs for ΔFat% in thigh and lower leg for IMNM, DYSF, LGMD2I, DMD, SMA, and Pompe disease, and only in lower leg for GNEM and thigh for IBM.

Conclusions

Global muscle segment Fat% showed to be sensitive to change in most investigated neuromuscular disorders. As compared to individual muscle drawing, it is a faster and an easier approach to assess disease progression. The use of individual muscle ROIs, however, is still of interest for exploring selective muscle involvement.

Key Points

• MRI-based evaluation of fatty replacement in muscles is used as an outcome measure in the assessment of 1-year disease progression in 8 different neuromuscular diseases.

• Different segmentation approaches, including global muscle segmentation, were evaluated for determining 1-year fat fraction changes in lower limb skeletal muscles.

• Global muscle segment fat fraction has shown to be sensitive to change in lower leg and thigh in most of the investigated neuromuscular diseases.

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Abbreviations

AL:

Adductor longus

AM:

Adductor magnus

BF:

Biceps femoris

DMD:

Duchenne muscle dystrophy

DYSF:

Dysferlinopathy

ED:

Extensor digitorum

Fat%:

Fat fraction

FIB:

Fibularis

FOV:

Field of view

GL:

Gastrocnemius lateralis

GM:

Gastrocnemius medialis

GNEM:

GNE myopathy

GRA:

Gracilis

HSTR:

Hamstring

IBM:

Inclusion body myositis

I-M FAT:

Intermuscular fat

IMNM:

Immune-mediated necrotizing myopathy

LEG_ANT:

Anterior part of leg

LGMD2I:

Limb-girdle muscular dystrophy type 2I

MSE:

Multi-spin-echo

PER:

Peroneus

QUAD:

Quadriceps

RF:

Rectus femoris

SAR:

Sartorius

SM:

Semimembranosus

SMA:

Spinal muscular atrophy

SOL:

Soleus

SRM:

Standardized response mean

ST:

Semitendinosus

TA:

Tibialis anterior

TE:

Echo time

TP:

Tibialis posterior

TR:

Repetition time

VI:

Vastus intermedius

VL:

Vastus lateralis

VM:

Vastus medialis

ΔFat%:

Fat fraction change after one year

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Acknowledgments

The authors thank the patients, their families and caregivers, for their participation in the respective studies. Also, the assistance of many people involved in this work, including Mélanie Annoussamy and colleagues from I-Motion (Trousseau Hospital, Paris, France), Jean-Yves Hogrel from the Neuromuscular Investigation Center at the Institute of Myology (Paris, France), as well as the Neurology and MRI departments at Cincinnati Children’s Hospital Medical Center (Cincinnati, Ohio, USA) is greatly acknowledged. Some of the data presented in this work were collected as part of the BIOIMAGE-NMD FP7 framework program for research, technological development and demonstration (DMD data from Cincinnati), the AFM-funded DMD natural history study, the Clinical Outcome Study for Dysferlinopathy steered by the JAIN foundation, and the SMA natural history study co-funded by Roche and the Institute of Myology.

Funding

The PRO-DMD-01 natural history study (NCT01753804) was sponsored by Prosensa Therapeutics B.V. and BioMarin. The DMD natural history study (NCT02780492) was sponsored by AFM. The DYSF COS natural history study (NCT01676077) was sponsored by the JAIN foundation. The SMA natural history study (NCT0239183) was sponsored by Roche and the Institute of Myology. The EU Bioimage-NMD program with project ID 602485 was funded under FP7-HEALTH-2013-INNOVATION-1.

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Authors and Affiliations

  1. NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France

    Harmen Reyngoudt, Benjamin Marty, Jean-Marc Boisserie, Julien Le Louër, Cedi Koumako & Pierre G. Carlier

  2. NMR Laboratory, CEA/DRF/IBFJ/MIRCen, Bâtiment Babinski, Groupe Hospitalier Pitié-Salpêtrière, 47-83 boulevard Vincent Auriol, 75651, Paris Cedex 13, France

    Harmen Reyngoudt, Benjamin Marty, Jean-Marc Boisserie, Julien Le Louër, Cedi Koumako & Pierre G. Carlier

  3. Consultants for Research in Imaging and Spectroscopy, Tournai, Belgium

    Pierre-Yves Baudin

  4. Department of Neurology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, USA

    Brenda Wong

  5. Department of Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA

    Brenda Wong

  6. Neuromuscular Reference Center, Institute of Myology, Pitié-Salpêtrière Hospital (AP-HP), Paris, France

    Tanya Stojkovic & Anthony Béhin

  7. I-Motion – Pediatric Clinical Trials Department, Trousseau Hospital (AP-HP), Paris, France

    Teresa Gidaro & Laurent Servais

  8. Department of Internal Medicine and Clinical Immunology, University Pierre et Marie Curie, AP-HP, GH Pitié-Salpêtrière, Paris, France

    Yves Allenbach & Olivier Benveniste

  9. Department of Pediatrics, CHU, University of Liège, Liège, Belgium

    Laurent Servais

  10. MDUK Oxford Neuromuscular Center, Department of Pediatrics, University of Oxford, Oxford, UK

    Laurent Servais

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  1. Harmen Reyngoudt
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  2. Benjamin Marty
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  14. Pierre G. Carlier
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Corresponding author

Correspondence to Harmen Reyngoudt.

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Guarantor

The scientific guarantor of this publication is Harmen Reyngoudt, PhD.

Conflict of interest

HR, BM, JMB, JLL, CK, PYB, TS, AB, TG, YA, OB and PGC report no disclosures relevant to the manuscript; BW worked as an investigator of clinical trials of BioMarin (including the PRO-DMD-01 NCT01753804 natural history study that included the DMD cohorts in this study) and received honoraria from serving on the advisory board of BioMarin; LS worked as an investigator for the NCT02780492 DMD natural history study and the NCT0239183 SMA natural history study co-funded by Roche and he performed consultancy for Roche, Avexis, Cytokinetics, Biogen, Sarepta, Biomarin, Pfizer, Biophytis.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in

Gidaro T, Reyngoudt H, Le Louër J et al (2020) Quantitative nuclear magnetic resonance imaging detects subclinical changes over 1 year in skeletal muscle of GNE myopathy. J Neurol 267:228-238.

Willis TA, Hollingsworth KG, Coombs A et al (2013) Quantitative muscle MRI as an assessment tool for monitoring disease progression in LGMD2I: A multicentre longitudinal study. PLoS One 8:e70993.

Carlier PG, Azzabou N, de Sousa PL et al (2015) Skeletal muscle quantitative nuclear magnetic resonance imaging follow-up of adult Pompe patients. J Inherit Metab Dis 38:565–572.

Naarding KJ, Reyngoudt H, van Zwet EW et al (2020) MRI vastus lateralis fat fraction predicts loss of ambulation in Duchenne muscular dystrophy. Neurology 94:e1386-1394.

Chabanon A, Seferian AM, Daron A et al (2018) Prospective and longitudinal natural history study of patients with Type 2 and 3 spinal muscular atrophy: Baseline data NatHis-SMA study. PLoS One 13:e0201004.

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Reyngoudt, H., Marty, B., Boisserie, JM. et al. Global versus individual muscle segmentation to assess quantitative MRI-based fat fraction changes in neuromuscular diseases. Eur Radiol 31, 4264–4276 (2021). https://doi.org/10.1007/s00330-020-07487-0

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