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Article

Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength

1
Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany
2
Department of Sport and Health Sciences, Technical University of Munich, Georg-Brauchle-Ring 60, 80992 Munich, Germany
3
Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar der Technischen Universitär München, Ismaninger 22, 81675 Munich, Germany
4
VAMPIRE Project, Computing (SSEN), University of Dundee, Nethergate, Dundee DD1 4HN, UK
5
Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Evangelos Terpos
Diagnostics 2021, 11(2), 239; https://doi.org/10.3390/diagnostics11020239
Received: 16 December 2020 / Revised: 28 January 2021 / Accepted: 1 February 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Spine Imaging: Novel Image Acquisition Techniques and Analysis Tools)
Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water–fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2–L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R2adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R2adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP. View Full-Text
Keywords: magnetic resonance imaging; texture analysis; proton density fat fraction; paraspinal muscles; muscle strength magnetic resonance imaging; texture analysis; proton density fat fraction; paraspinal muscles; muscle strength
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MDPI and ACS Style

Dieckmeyer, M.; Inhuber, S.; Schlaeger, S.; Weidlich, D.; Mookiah, M.R.K.; Subburaj, K.; Burian, E.; Sollmann, N.; Kirschke, J.S.; Karampinos, D.C.; Baum, T. Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength. Diagnostics 2021, 11, 239. https://doi.org/10.3390/diagnostics11020239

AMA Style

Dieckmeyer M, Inhuber S, Schlaeger S, Weidlich D, Mookiah MRK, Subburaj K, Burian E, Sollmann N, Kirschke JS, Karampinos DC, Baum T. Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength. Diagnostics. 2021; 11(2):239. https://doi.org/10.3390/diagnostics11020239

Chicago/Turabian Style

Dieckmeyer, Michael, Stephanie Inhuber, Sarah Schlaeger, Dominik Weidlich, Muthu R.K. Mookiah, Karupppasamy Subburaj, Egon Burian, Nico Sollmann, Jan S. Kirschke, Dimitrios C. Karampinos, and Thomas Baum. 2021. "Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength" Diagnostics 11, no. 2: 239. https://doi.org/10.3390/diagnostics11020239

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