Accuracy and precision of proton density fat fraction measurement across field strengths and scan intervals: A phantom and human study

2018 ◽  
Vol 50 (1) ◽  
pp. 305-314 ◽  
Author(s):  
Hye Jin Kim ◽  
Hyo Jung Cho ◽  
Bohyun Kim ◽  
Myung‐Won You ◽  
Jei Hee Lee ◽  
...  
Keyword(s):  
Human Study ◽  
Proton Density ◽  
Proton Density Fat Fraction ◽  
Accuracy And Precision ◽  
Fat Fraction ◽  
Fraction Measurement ◽  
Field Strengths

scholarly journals Temperature‐corrected proton density fat fraction estimation using chemical shift‐encoded MRI in phantoms

2021 ◽  
Vol 86 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Ruvini Navaratna ◽  
Ruiyang Zhao ◽  
Timothy J. Colgan ◽  
Houchun Harry Hu ◽  
Mark Bydder ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Proton Density ◽  
Proton Density Fat Fraction ◽  
Fat Fraction

scholarly journals Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water–Fat MRI

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 302
Author(s):  
Michael Dieckmeyer ◽  
Stephanie Inhuber ◽  
Sarah Schläger ◽  
Dominik Weidlich ◽  
Muthu R. K. Mookiah ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Muscle Strength ◽  
Neuromuscular Disorders ◽  
Texture Features ◽  
Thigh Muscle ◽  
Proton Density ◽  
Proton Density Fat Fraction ◽  
Thigh Muscles ◽  
Fat Fraction ◽  
Flexion Strength

Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water–fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF. Methods: 30 healthy subjects (age = 30 ± 6 years; 15 females) underwent CSE-MRI of the lumbar spine at 3T, using a six-echo 3D spoiled gradient echo sequence. Quadriceps (EXT) and ischiocrural (FLEX) muscles were segmented to extract mean PDFF and texture features. Muscle flexion and extension strength were measured with an isokinetic dynamometer. Results: Of the eleven extracted texture features, Variance(global) showed the highest significant correlation with extension strength (p < 0.001, R2adj = 0.712), and Correlation showed the highest significant correlation with flexion strength (p = 0.016, R2adj = 0.658). Multivariate linear regression models identified Variance(global) and sex, but not PDFF, as significant predictors of extension strength (R2adj = 0.709; p < 0.001), while mean PDFF, sex, and BMI, but none of the texture features, were identified as significant predictors of flexion strength (R2adj = 0.674; p < 0.001). Conclusions: Prediction of quadriceps muscle strength can be improved beyond mean PDFF by means of TA, indicating the capability to quantify muscular fat infiltration patterns.

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