Automated measurement of fat infiltration in the hip abductors from Dixon magnetic resonance imaging.


Journal

Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883

Informations de publication

Date de publication:
10 2020
Historique:
received: 06 03 2020
revised: 09 06 2020
accepted: 25 06 2020
pubmed: 3 7 2020
medline: 29 1 2021
entrez: 3 7 2020
Statut: ppublish

Résumé

Intramuscular fat infiltration is a dynamic process, in response to exercise and muscle health, which can be quantified by estimating fat fraction (FF) from Dixon MRI. Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle as they have a fundamental role in walking. The automated measurement of the abductors' FF requires the challenging task of segmenting them. We aimed to design, develop and evaluate a multi-atlas based method for automated measurement of fat fraction in the main hip abductor muscles: gluteus maximus (GMAX), gluteus medius (GMED), gluteus minimus (GMIN) and tensor fasciae latae (TFL). We collected and manually segmented Dixon MR images of 10 healthy individuals and 7 patients who underwent MRI for hip problems. Twelve of them were selected to build an atlas library used to implement the automated multi-atlas segmentation method. We compared the FF in the hip abductor muscles for the automated and manual segmentations for both healthy and patients groups. Measures of average and spread were reported for FF for both methods. We used the root mean square error (RMSE) to quantify the method accuracy. A linear regression model was used to explain the relationship between FF for automated and manual segmentations. The automated median (IQR) FF was 20.0(16.0-26.4) %, 14.3(10.9-16.5) %, 15.5(13.9-18.6) % and 16.2(13.5-25.6) % for GMAX, GMED, GMIN and TFL respectively, with a FF RMSE of 1.6%, 0.8%, 2.1%, 2.7%. A strong linear correlation (R The automated measurement of FF of hip abductor muscles from Dixon MRI had good agreement with FF measurements from manually segmented images. The method was accurate for both healthy and patients groups.

Identifiants

pubmed: 32615150
pii: S0730-725X(20)30171-5
doi: 10.1016/j.mri.2020.06.019
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

61-70

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Martin A Belzunce (MA)

Royal National Orthopaedic Hospital, Stanmore HA7 4LP, UK. Electronic address: martin.belzunce@nhs.net.

Johann Henckel (J)

Royal National Orthopaedic Hospital, Stanmore HA7 4LP, UK.

Anastasia Fotiadou (A)

Royal National Orthopaedic Hospital, Stanmore HA7 4LP, UK.

Anna Di Laura (A)

Royal National Orthopaedic Hospital, Stanmore HA7 4LP, UK.

Alister Hart (A)

Royal National Orthopaedic Hospital, Stanmore HA7 4LP, UK; Institute of Orthopaedics and Musculoskeletal Science, University College London, Stanmore HA7 4LP, UK.

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