Jessica Ornowski, Lucas Dziesinski, Madeline Hess, Roland Krug, Maryse Fortin, Abel Torres-Espin, Sharmila Majumdar, Valentina Pedoia, Noah B. Bonnheim, Jeannie F. Bailey
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Advanced MRI techniques, including chemical-shift encoding (CSE) based water–fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water–fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (<i>r</i>), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water–fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water–fat images for all thresholding techniques (<i>r =</i> 0.70–0.86, <i>p <</i> 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water–fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.</p>\n </section>\n </div>","PeriodicalId":14876,"journal":{"name":"JOR Spine","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jsp2.1301","citationCount":"0","resultStr":"{\"title\":\"Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water–fat MRI\",\"authors\":\"Jessica Ornowski, Lucas Dziesinski, Madeline Hess, Roland Krug, Maryse Fortin, Abel Torres-Espin, Sharmila Majumdar, Valentina Pedoia, Noah B. Bonnheim, Jeannie F. Bailey\",\"doi\":\"10.1002/jsp2.1301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water–fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water–fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (<i>r</i>), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water–fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water–fat images for all thresholding techniques (<i>r =</i> 0.70–0.86, <i>p <</i> 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water–fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.</p>\\n </section>\\n </div>\",\"PeriodicalId\":14876,\"journal\":{\"name\":\"JOR Spine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jsp2.1301\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOR Spine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jsp2.1301\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOR Spine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jsp2.1301","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0
摘要
棘旁肌肉脂肪浸润与脊柱退变和腰痛有关,然而,使用临床磁共振成像(MRI)技术量化肌肉脂肪仍然是一个挑战。先进的MRI技术,包括基于化学移位编码(CSE)的水脂肪MRI,可以精确测量肌肉脂肪,但这些技术在常规临床实践中并不广泛使用。为了便于临床影像学评估棘旁肌脂肪,我们比较了使用T1和T2加权图像估计肌肉脂肪分数(FF)的四种阈值方法,并将水脂肪MRI测量结果作为基本事实:高斯阈值法、Otsu方法、K均值聚类和二次判别分析。用水脂肪MRI对腰多裂肌(MF)、竖脊肌(ES)、腰方肌(QL)和腰肌(PS)以及所有肌肉进行T1 -和T2 -加权MRI的FF估计,计算Pearson相关系数(r)、平均绝对误差和平均偏倚误差。我们发现,对于所有肌肉,T1和T2加权图像的FF测量值与所有阈值技术的水-脂肪图像测量值呈强正相关(r = 0.70-0.86, p < 0.0001),肌肉间相关强度的变化远远大于方法间相关强度的变化。我们得出结论,肌肉FF可以使用阈值T1和T2加权MRI图像进行量化,相对于水脂肪MRI,特别是MF和ES,其偏差和绝对误差相对较低,阈值技术的选择应取决于感兴趣的肌肉和临床MRI序列。
Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water–fat MRI
Background
Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water–fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice.
Methods
To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water–fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water–fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined.
Results
We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water–fat images for all thresholding techniques (r = 0.70–0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength.
Conclusion
We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water–fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.