Share:
Share this content in WeChat
X
Technical Article
Feasibility study on quantification method of fat infiltration in thigh muscle based on axial T1WI image
YAN Jun  WANG Ling  HUANG Yilong  WANG Haolei  ZHU Hongli  LUO Lin  HE Bo 

Cite this article as: Yan J, Wang L, Huang YL, et al. Feasibility study on quantification method of fat infiltration in thigh muscle based on axial T1WI image[J]. Chin J Magn Reson Imaging, 2021, 12(12): 49-54. DOI:10.12015/issn.1674-8034.2021.12.010.


[Abstract] Objective To explore the feasibility of using ImageJ to segment and quantify subcutaneous adipose tissue (SAT), intramuscular fat (IntraMF) and intermuscular fat (InterMF) on MRI T1WI images. Materials and Methods: MRI scans of the midthigh were performed on 28 volunteers, including 14 patients with type 2 diabetes. Goutallier classification was performed for the degree of muscle fat infiltration on the axial T1 image. The SAT, IntraMF and InterMF areas of the thigh were measured by ImageJ segmentation. The area of IntraMF was calculated by the fat fraction as measured by iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence (IDEAL-IQ). The correlation between ImageJ segmentation and Goutallier classification and IDEA-IQ fat quantification method was analyzed. The intra-observer and inter-observer reliability of the ImageJ segmentation method was tested.Results There was a strong correlation between the ImageJ segmentation and the fat fraction as measured by IDEA-IQ (r=0.998, P<0.001); the inter-observer and intra-observer ICC for ImageJ segmentation of thigh SAT area was 0.999, P<0.001; the inter-observer ICC of InterMF area was 0.941, P=0.003, intra-observer ICC was 0.992, P<0.001; the inter-observer ICC of thigh IntraMF area was 1.000, P<0.001, and the intra-observer ICC was 0.997, P<0.001.Conclusion ImageJ was reliable in quantifying thigh SAT, IntraMF and InterMF on MR T1 images, and was strongly correlated with IDEA-IQ fat quantification. ImageJ segmentation is a feasible alternative to semi-quantitative Goutallier classification.
[Keywords] skeletal muscle;fatty infiltration;ImageJ;fat quantification;magnetic resonance imaging

YAN Jun1, 2   WANG Ling3   HUANG Yilong1   WANG Haolei1   ZHU Hongli1   LUO Lin2   HE Bo1*  

1 Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China

2 Department of Radiology, Qujing First People's Hospital, Qujing 655000, China

3 Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China

He B, E-mail: 929883137@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Graduate Innovation Fund Project of Kunming Medical University (No. 2020S036).
Received  2021-09-28
Accepted  2021-11-05
DOI: 10.12015/issn.1674-8034.2021.12.010
Cite this article as: Yan J, Wang L, Huang YL, et al. Feasibility study on quantification method of fat infiltration in thigh muscle based on axial T1WI image[J]. Chin J Magn Reson Imaging, 2021, 12(12): 49-54. DOI:10.12015/issn.1674-8034.2021.12.010.

[1]
Kim JE, Dunville K, Li J, et al. Intermuscular Adipose Tissue Content and Intramyocellular Lipid Fatty Acid Saturation Are Associated with Glucose Homeostasis in Middle-Aged and Older Adults[J]. Endocrinology and Metabolism, 2017, 32(2): 257-264. DOI: 10.3803/EnM.2017.32.2.257.
[2]
Yu FY, He B, Chen L, et al. Intermuscular Fat Content in Young Chinese Men With Newly Diagnosed Type 2 Diabetes: Based on MR mDIXON-Quant Quantitative Technique. Front Endocrinol (Lausanne), 2021, 12: 536018. DOI: 10.3389/fendo.2021.536018.
[3]
Waters DL. Intermuscular Adipose Tissue: A Brief Review of Etiology, Association With Physical Function and Weight Loss in Older Adults[J]. Ann Geriatr Med Res, 2019, 23(1): 3-8. DOI: 10.4235/agmr.19.0001.
[4]
Zhang XX, Zhang H, Nan J, et al. Muscle fat measurement by magnetic resonance technology: The progresses in muscle disease[J]. Chin J Magn Reson Imaging, 2019, 10(6): 474-478. DOI: 10.12015/issn.1674-8034.2019.06.017.
[5]
Biltz NK, Collins KH, Shen KC, et al. Infiltration of intramuscular adipose tissue impairs skeletal muscle contraction[J]. J Physiol, 2020, 598(13): 2669-2683. DOI: 10.1113/JP279595.
[6]
Strijkers GJ, Araujo E, Azzabou N, et al. Exploration of New Contrasts, Targets, and MR Imaging and Spectroscopy Techniques for Neuromuscular Disease-A Workshop Report of Working Group 3 of the Biomedicine and Molecular Biosciences COST Action BM1304 MYO-MRI[J]. IOS Press Open Library, 2020, 6(1): 1-30. DOI: 10.3233/JND-180333.
[7]
Dabiri S, Popuri K, Feliciano E, et al. Deep learning method for localization and segmentation of abdominal CT[J]. Comput Med Imag Grap, 2020, 85: 101776. DOI: 10.1016/j.compmedimag.2020.101776.
[8]
Lippe J, Spang JT, Leger RR, et al. Inter-rater agreement of the Goutallier, Patte, and Warner classification scores using preoperative magnetic resonance imaging in patients with rotator cuff tears.[J]. Arthroscopy-the Journal of Arthroscopic & Related Surgery, 2012, 28(2): 154-159. DOI: 10.1016/j.arthro.2011.07.016.
[9]
Agten CA, Rosskopf AB, Gerber C, et al. Quantification of early fatty infiltration of the rotator cuff muscles: comparison of multi-echo Dixon with single-voxel MR spectroscopy[J]. Eur Radiol, 2015, 26(10): 1-9. DOI: 10.1007/s00330-015-4144-y.
[10]
Karampinos DC, Baum T, Nardo L, et al. Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift‐based water/fat separation[J]. J Magn Reson Imaging, 2012, 35(4): 899-907. DOI: 10.1002/jmri.23512.
[11]
Kang GH, Cruite I, Shiehmorteza M, et al. Reproducibility of MRI-determined proton density fat fraction across two different MR scanner platforms[J]. J Magn Reson Imaging, 2011, 34(4): 928-934. DOI: 10.1002/jmri.22701.
[12]
Alizai H, Nardo L, Karampinos DC, et al. Comparison of clinical semi-quantitative assessment of muscle fat infiltration with quantitative assessment using chemical shift-based water/fat separation in MR studies of the calf of post-menopausal women[J]. Eur Radiol, 2012, 22(7): 1592-1600. DOI: 10.1007/s00330-012-2404-7.
[13]
Lee J, Koh D, Ong CN. Statistical evaluation of agreement between two methods for measuring a quantitative variable[J]. Computers in Biology & Medicine, 1989, 19(1): 61-70. DOI: 10.1016/0010-4825(89)90036-x.
[14]
Konopka AR, Wolff CA, Suer MK, et al. Relationship between Intermuscular Adipose Tissue Infiltration and Myostatin before and after Aerobic Exercise Training[J]. Am J Physiol-Reg I, 2018, 315(3): R461-R468. DOI: 10.1152/ajpregu.00030.2018.
[15]
Sara O, Claudio LL, Fabio R, et al. Automatic Muscle and Fat Segmentation in the Thigh From T1-Weighted MRI[J]. J Magn Reson Imaging, 2016, 43(3): 601–610. DOI: 10.1002/jmri.25031.
[16]
Chaudry O, Friedberger A, Grimm A, et al. Segmentation of the fascia lata and reproducible quantification of intermuscular adipose tissue (IMAT) of the thigh[J]. Magn Reson Mater Phy, 2021, 34: 367-376. DOI: 10.1007/s10334-020-00878-w.

PREV USPIO enhanced SWI MRI to evaluate the effects of cinobufacini on a nude orthotopic hepatocellular carcinoma tumor model
NEXT Features fusion of brain networks and its application to autism recognition by machine learning based on resting-state functional magnetic resonance imaging
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn