Dataset Information

Measurement of fat fraction in the human thymus by localized NMR and three-point Dixon MRI techniques.

ABSTRACT: PURPOSE:To develop a protocol to non-invasively measure and map fat fraction, fat/(fat+water), as a function of age in the adult thymus for future studies monitoring the effects of interventions aimed at promoting thymic rejuvenation and preservation of immunity in older adults. MATERIALS AND METHODS:Three-dimensional spoiled gradient echo 3T MRI with 3-point Dixon fat-water separation was performed at full inspiration for thymus conspicuity in 36 volunteers 19 to 56?years old. Reproducible breath-holding was facilitated by real-time pressure recording external to the console. The MRI method was validated against localized spectroscopy in vivo, with ECG triggering to compensate for stretching during the cardiac cycle. Fat fractions were corrected for T1 and T2 bias using relaxation times measured using inversion recovery-prepared PRESS with incremented echo time. RESULTS:In thymus at 3?T, T1water?=?978?±?75?ms, T1fat?=?323?±?37?ms, T2water?=?43.4?±?9.7?ms and T2fat?=?52.1?±?7.6?ms were measured. Mean T1-corrected MRI fat fractions varied from 0.2 to 0.8 and were positively correlated with age, weight and body mass index (BMI). In subjects with matching MRI and MRS fat fraction measurements, the difference between these measurements exhibited a mean of -0.008 with a 95% confidence interval of (0.123, -0.138). CONCLUSIONS:3-point Dixon MRI of the thymus with T1 bias correction produces quantitative fat fraction maps that correlate with T2-corrected MRS measurements and show age trends consistent with thymic involution.

SUBMITTER: Fishbein KW

PROVIDER: S-EPMC5971004 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Measurement of fat fraction in the human thymus by localized NMR and three-point Dixon MRI techniques.

Fishbein Kenneth W KW Makrogiannis Sokratis K SK Lukas Vanessa A VA Okine Marilyn M Ramachandran Ramona R Ferrucci Luigi L Egan Josephine M JM Chia Chee W CW Spencer Richard G RG

Magnetic resonance imaging 20180329

<h4>Purpose</h4>To develop a protocol to non-invasively measure and map fat fraction, fat/(fat+water), as a function of age in the adult thymus for future studies monitoring the effects of interventions aimed at promoting thymic rejuvenation and preservation of immunity in older adults.<h4>Materials and methods</h4>Three-dimensional spoiled gradient echo 3T MRI with 3-point Dixon fat-water separation was performed at full inspiration for thymus conspicuity in 36 volunteers 19 to 56 years old. Re ...[more]

PMID: 29605590

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Project description:Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.Supplementary informationThe online version contains supplementary material available at 10.1186/s40537-022-00677-1.

Project description: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.

Project description:PURPOSE:Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1 , T2 , and M0 maps. Here, we develop a 3-point Dixon-cMRF approach to enable simultaneous water specific T1 , T2 , and M0 mapping of the heart and fat fraction (FF) estimation in a single breath-hold scan. METHODS:Dixon-cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3-echo GRE acquisition with golden angle radial readout and a high-dimensional low-rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water-fat separation of the Dixon-cMRF time series is performed to allow for water- and fat-specific T1 , T2 , and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon-cMRF was evaluated in a standardized T1 -T2 phantom, in a water-fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2 -GRASE, and 6-point Dixon proton density FF (PDFF) mapping. RESULTS:Dixon-cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon-cMRF FF and reference PDFF (R2 > 0.99) and between Dixon-cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water-fat phantom. In vivo Dixon-cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2 -GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon-cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2 -GRASE and water Dixon-cMRF, respectively. Dixon-cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. CONCLUSION:The proposed Dixon-cMRF allows to simultaneously quantify myocardial water T1 , water T2 , and FF in a single breath-hold scan, enabling multi-parametric T1 , T2 , and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water-fat partial volume was demonstrated in phantom experiments.

Dataset Information

Measurement of fat fraction in the human thymus by localized NMR and three-point Dixon MRI techniques.

Publications

Measurement of fat fraction in the human thymus by localized NMR and three-point Dixon MRI techniques.

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