High Inter-Rater Reliability of Manual Segmentation and Volume-Based Tractography in Healthy and Dystrophic Human Calf Muscle.

calf musculature diffusion tensor imaging muscle MRI neuromuscular diseases quantitative MRI tractography

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
24 Aug 2021
Historique:
received: 26 07 2021
revised: 19 08 2021
accepted: 20 08 2021
entrez: 28 9 2021
pubmed: 29 9 2021
medline: 29 9 2021
Statut: epublish

Résumé

Muscle diffusion tensor imaging (mDTI) is a promising surrogate biomarker in the evaluation of muscular injuries and neuromuscular diseases. Since mDTI metrics are known to vary between different muscles, separation of different muscles is essential to achieve muscle-specific diffusion parameters. The commonly used technique to assess DTI metrics is parameter maps based on manual segmentation (MSB). Other techniques comprise tract-based approaches, which can be performed in a previously defined volume. This so-called volume-based tractography (VBT) may offer a more robust assessment of diffusion metrics and additional information about muscle architecture through tract properties. The purpose of this study was to assess DTI metrics of human calf muscles calculated with two segmentation techniques-MSB and VBT-regarding their inter-rater reliability in healthy and dystrophic calf muscles. 20 healthy controls and 18 individuals with different neuromuscular diseases underwent an MRI examination in a 3T scanner using a 16-channel Torso XL coil. DTI metrics were assessed in seven calf muscles using MSB and VBT. Coefficients of variation (CV) were calculated for both techniques. MSB and VBT were performed by two independent raters to assess inter-rater reliability by ICC analysis and Bland-Altman plots. Next to analysis of DTI metrics, the same assessments were also performed for tract properties extracted with VBT. For both techniques, low CV were found for healthy controls (≤13%) and neuromuscular diseases (≤17%). Significant differences between methods were found for all diffusion metrics except for λ Both segmentation techniques can be used in the evaluation of DTI metrics in healthy controls and different NMD with low rater dependency and high precision but differ significantly from each other. Our findings underline that the same segmentation protocol must be used to ensure comparability of mDTI data.

Sections du résumé

BACKGROUND BACKGROUND
Muscle diffusion tensor imaging (mDTI) is a promising surrogate biomarker in the evaluation of muscular injuries and neuromuscular diseases. Since mDTI metrics are known to vary between different muscles, separation of different muscles is essential to achieve muscle-specific diffusion parameters. The commonly used technique to assess DTI metrics is parameter maps based on manual segmentation (MSB). Other techniques comprise tract-based approaches, which can be performed in a previously defined volume. This so-called volume-based tractography (VBT) may offer a more robust assessment of diffusion metrics and additional information about muscle architecture through tract properties. The purpose of this study was to assess DTI metrics of human calf muscles calculated with two segmentation techniques-MSB and VBT-regarding their inter-rater reliability in healthy and dystrophic calf muscles.
METHODS METHODS
20 healthy controls and 18 individuals with different neuromuscular diseases underwent an MRI examination in a 3T scanner using a 16-channel Torso XL coil. DTI metrics were assessed in seven calf muscles using MSB and VBT. Coefficients of variation (CV) were calculated for both techniques. MSB and VBT were performed by two independent raters to assess inter-rater reliability by ICC analysis and Bland-Altman plots. Next to analysis of DTI metrics, the same assessments were also performed for tract properties extracted with VBT.
RESULTS RESULTS
For both techniques, low CV were found for healthy controls (≤13%) and neuromuscular diseases (≤17%). Significant differences between methods were found for all diffusion metrics except for λ
CONCLUSIONS CONCLUSIONS
Both segmentation techniques can be used in the evaluation of DTI metrics in healthy controls and different NMD with low rater dependency and high precision but differ significantly from each other. Our findings underline that the same segmentation protocol must be used to ensure comparability of mDTI data.

Identifiants

pubmed: 34573863
pii: diagnostics11091521
doi: 10.3390/diagnostics11091521
pmc: PMC8466691
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Deutsche Forschungsgemeinschaft
ID : 122679504
Organisme : Ruhr-University Bochum
ID : F960R-2020, K139-20, K-139-44
Organisme : Sanofi Genzyme
ID : SGZ-2019-12541

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Auteurs

Johannes Forsting (J)

Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, 44789 Bochum, Germany.

Marlena Rohm (M)

Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, 44789 Bochum, Germany.
Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, 44789 Bochum, Germany.

Martijn Froeling (M)

Department of Radiology, University Medical Centre Utrecht, 3584 Utrecht, The Netherlands.

Anne-Katrin Güttsches (AK)

Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, 44789 Bochum, Germany.
Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, 44789 Bochum, Germany.

Matthias Vorgerd (M)

Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, 44789 Bochum, Germany.
Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, 44789 Bochum, Germany.

Lara Schlaffke (L)

Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, 44789 Bochum, Germany.
Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, 44789 Bochum, Germany.

Robert Rehmann (R)

Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, 44789 Bochum, Germany.
Department of Neurology, Klinikum Dortmund, University Witten-Herdecke, 44137 Dortmund, Germany.

Classifications MeSH