Fat Quantification in the Vertebral Body: Comparison of Modified Dixon Technique with Single-Voxel Magnetic Resonance Spectroscopy

Lee, Sang Hyup; Yoo, Hye Jin; Yu, Seung-Man; Hong, Sung Hwan; Choi, Ja Young; Chae, Hee Dong

doi:10.3348/kjr.2018.0174

Cited by 28 publications

(16 citation statements)

References 29 publications

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“…In water-fat imaging, multiple echoes in a gradient echo acquisition are used to create a time-dependent phase shift between water and fat MR signals ( 9 ). Strong correlations have been shown between bone marrow fat fractions in lumbar vertebral bodies obtained from modified Dixon sequences and those obtained from single-voxel magnetic resonance spectroscopy (MRS), which is considered a gold standard method for quantification of bone marrow fat ( 10 , 11 ). The iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) technique has also been developed through further modifications of the Dixon technique to overcome field inhomogeneities during water-fat separation with maximal signal-to-noise ratio (SNR) and minimal scanning time ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification

et al. 2020

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Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: ( 1 ) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. ( 2 ) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset ( n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae ( n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only −0.605% (CI 95% = −0.847 to −0.363%) and agreement limits of −3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

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Section: Introductionmentioning

confidence: 99%

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification

et al. 2020

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“…While MRI‐determined PDFF has been proven as a reliable noninvasive quantitative imaging biomarker (QIB) for the assessment of liver steatosis, its impact on bone marrow fat quantification is a significant, yet not so well‐explored issue. Owing to the distinct microarchitectural characteristics of the bone, it is important to validate the precision of MRI‐determined PDFF for bone marrow fat quantification for several reasons: first, bone marrow adipose tissue is not homogeneously distributed within the trabecular bone matrix and can exhibit a wider range of fat content than most visceral organs, ranging from 0–80% . Second, susceptibility differences at the interface between bone marrow and trabecular bone induce a rapid decay of the measured gradient echo signal with increasing echo time and can confound PDFF estimation, especially at high field strengths .…”

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confidence: 99%

“…Owing to the distinct microarchitectural characteristics of the bone, it is important to validate the precision of MRI-determined PDFF for bone marrow fat quantification for several reasons: first, bone marrow adipose tissue is not homogeneously distributed within the trabecular bone matrix and can exhibit a wider range of fat content than most visceral organs, ranging from 0-80%. 12 Second, susceptibility differences at the interface between bone marrow and trabecular bone induce a rapid decay of the measured gradient echo signal with increasing echo time and can confound PDFF estimation, especially at high field strengths. 13,14 The first validation studies have shown high accuracy for MRI-determined PDFF using the chemically determined fat content in ex vivo water-fat trabecular bone phantoms 15,16 and single-voxel 1 H-MR spectroscopy (MRS)-based fat fraction estimations of spine marrow 14 as the reference standard.…”

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confidence: 99%

Proton density fat fraction MRI of vertebral bone marrow: Accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms

Schmeel

Vomweg²,

Träber

et al. 2019

Magnetic Resonance Imaging

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Background Chemical shift‐encoding based water‐fat MRI is an emerging method to noninvasively assess proton density fat fraction (PDFF), a promising quantitative imaging biomarker for estimating tissue fat concentration. However, in vivo validation of PDFF is still lacking for bone marrow applications. Purpose To determine the accuracy and precision of MRI‐determined vertebral bone marrow PDFF among different readers and across different field strengths and imager manufacturers. Study Type Repeatability/reproducibility. Subjects Twenty‐four adult volunteers underwent lumbar spine MRI with one 1.5T and two different 3.0T MR scanners from two vendors on the same day. Field Strength/Sequence 1.5T and 3.0T/3D spoiled‐gradient echo multipoint Dixon sequences. Assessment Two independent readers measured intravertebral PDFF for the three most central slices of the L1–5 vertebral bodies. Single‐voxel MR spectroscopy (MRS)‐determined PDFF served as the reference standard for PDFF estimation. Statistical Tests Accuracy and bias were assessed by Pearson correlation, linear regression analysis, and Bland–Altman plots. Repeatability and reproducibility were evaluated by Wilcoxon signed rank test, Friedman test, and coefficients of variation. Intraclass correlation coefficients were used to validate intra‐ and interreader as well as intraimager agreements. Results MRI‐based PDFF estimates of lumbar bone marrow were highly correlated (r2 = 0.899) and accurate (mean bias, –0.6%) against the MRS‐determined PDFF reference standard. PDFF showed high linearity (r2 = 0.972–0.978) and small mean bias (0.6–1.5%) with 95% limits of agreement within ±3.4% across field strengths, imaging platforms, and readers. Repeatability and reproducibility of PDFF were high, with the mean overall coefficient of variation being 0.86% and 2.77%, respectively. The overall intraclass correlation coefficient was 0.986 as a measure for an excellent interreader agreement. Data Conclusion MRI‐based quantification of vertebral bone marrow PDFF is highly accurate, repeatable, and reproducible among readers, field strengths, and MRI platforms, indicating its robustness as a quantitative imaging biomarker for multicentric studies. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1762–1772.

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“…Основным результатом следует признать обнаруженную обратную корреляцию (r = -0,55, p = 0,0004) между СЖ и МПКТ, которая свидетельствует о том, что процессы снижения МПКТ и увеличения содержания жира в здоровых позвонках протекают одновременно. Аналогичная достоверная корреляция между этими расчетными параметрами, измеренными в телах поясничных позвонков, была найдена для испытуемых среднего и пожилого возраста [11,12]. В работе [12] исследовалась группа волонтеров, средний возраст которых составлял 49,1 ± 19,0 года.…”

Section: Discussionunclassified

Proton magnetic resonance spectroscopy as an alternative method for quantitative assessment of mineral bone density

Ivantsova

Menshchikov

Polyakova

et al. 2021

Almanac of Clinical Medicine

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Aims: 1) To evaluate an association between the fat fraction (FF) and bone mineral density (BMD) measured by localized proton magnetic resonance spectroscopy (1H-MRS) and quantitative computed tomography (QCT) densitometry, respectively, in healthy vertebrae of children after a compression fracture; 2) To compare the FF and BMD values with the severity of the compression vertebrae fractures.Materials and methods: Twenty (20) patients (aged 11.1±2.1 years) with a trauma-induced compression vertebral fractures participated in the study. The BMD of L3, L4 vertebrae (mg/cm3) was measured in by QCT (Philips Brilliance 16). FF in the same area was measured from 1H-MR-spectra (STEAM, echo time (TE)=12.8 ms, repetition time (TR)=3000 ms, voxel size=20×15×10 mm) using Philips Achieva TX 3.0T MRI scanner.Results: Correlation analysis revealed a significant inverse linear correlation (r=-0.55, p=0.0004) between FF and BMD of L3 и L4 vertebrae. In addition, in the patients with severe compression vertebral fracture (more than 2 fractured vertebrae) there was a significant increase in FF values and a BMD decrease, compared to the values in the patients with mild fractures (1–2 fractured vertebrae).Conclusion: The correlation suggests that the increase of FF in the bone marrow and the decrease of BMD in children go in parallel. Therefore, 1H-MRS could be an alternative to QCT and dual-energy X-ray absorptiometry. The absence of radiation load allows for recommendation to use 1Н-MRS for screening and follow-up, as well as for the control of BMD.

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Fat Quantification in the Vertebral Body: Comparison of Modified Dixon Technique with Single-Voxel Magnetic Resonance Spectroscopy

Cited by 28 publications

References 29 publications

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification

Proton density fat fraction MRI of vertebral bone marrow: Accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms

Proton magnetic resonance spectroscopy as an alternative method for quantitative assessment of mineral bone density

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