ISMRM workshop on fat-water separation: insights, applications and progress in MRI.
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ABSTRACT:
SUBMITTER: Hu HH
PROVIDER: S-EPMC3575097 | biostudies-other | 2012 Aug
REPOSITORIES: biostudies-other
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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.
| S-EPMC9835035 | biostudies-literature
Project description:Since the introduction of simultaneous PET/MRI in 2011, there have been significant advancements. In this review, we highlight several technical advancements that have been made primarily in attenuation and motion correction and discuss the status of multiple clinical applications using PET/MRI. This review is based on the experience at the first PET/MRI conference cosponsored by the International Society for Magnetic Resonance in Medicine and the Society of Nuclear Medicine and Molecular Imaging.
| S-EPMC6785790 | biostudies-literature
Project description:PurposeTo develop a new water-fat separation and B0 estimation algorithm to effectively suppress the multiple resonances of fat signal in EPI. This is especially relevant for DWI where fat is often a confounding factor.MethodsWater-fat separation based on chemical-shift encoding enables robust fat suppression in routine MRI. However, for EPI the different chemical-shift displacements of the multiple fat resonances along the phase-encoding direction can be problematic for conventional separation algorithms. This work proposes a suitable model approximation for EPI under B0 and fat off-resonance effects, providing a feasible multi-peak water-fat separation algorithm. Simulations were performed to validate the algorithm. In vivo validation was performed in 6 volunteers, acquiring spin-echo EPI images in the leg (B0 homogeneous) and head-neck (B0 inhomogeneous) regions, using a TE-shifted interleaved EPI sequence with/without diffusion sensitization. The results are numerically and statistically compared with voxel-independent water-fat separation and fat saturation techniques to demonstrate the performance of the proposed algorithm.ResultsThe reference separation algorithm without the proposed spatial shift correction caused water-fat ambiguities in simulations and in vivo experiments. Some spectrally selective fat saturation approaches also failed to suppress fat in regions with severe B0 inhomogeneities. The proposed algorithm was able to achieve improved fat suppression for DWI data and ADC maps in the head-neck and leg regions.ConclusionThe proposed algorithm shows improved suppression of the multi-peak fat components in multi-shot interleaved EPI applications compared to the conventional fat saturation approaches and separation algorithms.
| S-EPMC8596522 | biostudies-literature
Project description:BackgroundParaspinal 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.MethodsTo 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.ResultsWe 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.ConclusionWe 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.
| S-EPMC10782057 | biostudies-literature
Project description:The purpose of this study was to develop a self-navigation strategy to improve scan efficiency and image quality of water/fat-separated, diffusion-weighted multishot echo-planar imaging (ms-EPI). This is accomplished by acquiring chemical shift-encoded diffusion-weighted data and using an appropriate water-fat and diffusion-encoded signal model to enable reconstruction directly from k-space data. Multishot EPI provides reduced geometric distortion and improved signal-to-noise ratio in diffusion-weighted imaging compared with single-shot approaches. Multishot acquisitions require corrections for physiological motion-induced shot-to-shot phase errors using either extra navigators or self-navigation principles. In addition, proper fat suppression is important, especially in regions with large B0 inhomogeneity. This makes the use of chemical shift encoding attractive. However, when combined with ms-EPI, shot-to-shot phase navigation can be challenging because of the spatial displacement of fat signals along the phase-encoding direction. In this work, a new model-based, self-navigated water/fat separation reconstruction algorithm is proposed. Experiments in legs and in the head-neck region of 10 subjects were performed to validate the algorithm. The results are compared with an image-based, two-dimensional (2D) navigated water/fat separation approach for ms-EPI and with a conventional fat saturation approach. Compared with the 2D navigated method, the use of self-navigation reduced the shot duration time by 30%-35%. The proposed algorithm provided improved diffusion-weighted water images in both leg and head-neck regions compared with the 2D navigator-based approach. The proposed algorithm also produced better fat suppression compared with the conventional fat saturation technique in the B0 inhomogeneous regions. In conclusion, the proposed self-navigated reconstruction algorithm can produce superior water-only diffusion-weighted EPI images with less artefacts compared with the existing methods.
| S-EPMC10078174 | biostudies-literature
Project description:To assess a 3D radial balanced steady-state free precession (SSFP) technique that provides submillimeter isotropic resolution and inherently registered fat and water image volumes in comparison to conventional T2-weighted RARE imaging for lesion characterization in breast magnetic resonance imaging (MRI).3D projection SSFP (3DPR-SSFP) combines a dual half-echo radial k-space trajectory with a linear combination fat/water separation technique (linear combination SSFP). A pilot study was performed in 20 patients to assess fat suppression and depiction of lesion morphology using 3DPR-SSFP. For all patients fat suppression was measured for the 3DPR-SSFP image volumes and depiction of lesion morphology was compared against corresponding T2-weighted fast spin echo (FSE) datasets for 15 lesions in 11 patients.The isotropic 0.63 mm resolution of the 3DPR-SSFP sequence demonstrated improved depiction of lesion morphology in comparison to FSE. The 3DPR-SSFP fat and water datasets were available in a 5-minute scan time while average fat suppression with 3DPR-SSFP was 71% across all 20 patients.3DPR-SSFP has the potential to improve the lesion characterization information available in breast MRI, particularly in comparison to conventional FSE. A larger study is warranted to quantify the effect of 3DPR-SSFP on specificity.
| S-EPMC3743726 | biostudies-literature
Project description:PurposesTo evaluate the feasibility of simultaneous quantification of liver iron concentration (LIC) and fat fraction (FF) using water-fat separation and quantitative susceptibility mapping (QSM).MethodsForty-five patients suspected of liver iron overload (LIO) were included. A volumetric interpolated breath-hold examination sequence for QSM and FF, a fat-saturated gradient echo sequence for R2*, a spin echo sequence for LIC measurements and MRS analyses for FF (FF-MRS) were performed. Magnetic susceptibility and FF were calculated using a water-fat separation method (FF-MRI). Correlation and receiver operating characteristic analyses were performed.ResultsMagnetic susceptibility showed strong correlation with LIC (rs=0.918). The optimal susceptibility cut-off values were 0.34, 0.63, 1.29 and 2.23 ppm corresponding to LIC thresholds of 1.8, 3.2, 7.0 and 15.0 mg/g dry weight. The area under the curve (AUC) were 0.948, 0.970, 1 and 1, respectively. No difference in AUC was found between susceptibility and R2* at all LIC thresholds. Correlation was found between FF-MRI and FF-MRS (R2=0.910).ConclusionsQSM has a high diagnostic performance for LIC quantification, similar to that of R2*. FF-MRI provides simultaneous fat quantification. Findings suggest QSM in combination with water-fat separation has potential value for evaluating LIO, especially in cases with coexisting steatosis.Key points• Magnetic susceptibility showed strong correlation with LIC (r s =0.918). • QSM showed high diagnostic performance for LIC, similar to that of R 2* . • Simultaneously estimated FF-MRI showed strong correlation with MR-Spectroscopy-based FF (R 2 =0.910). • QSM combining water-fat separation has quantitative value for LIO with coexisted steatosis.
| S-EPMC6030453 | biostudies-literature
Project description:PurposeTo perform multi-echo water/fat separated proton resonance frequency (PRF)-shift temperature mapping.MethodsState-of-the-art, iterative multi-echo water/fat separation algorithms produce high-quality water and fat images in the absence of heating but are not suitable for real-time imaging due to their long compute times and potential errors in heated regions. Existing fat-referenced PRF-shift temperature reconstruction methods partially address these limitations but do not address motion or large time-varying and spatially inhomogeneous B0 shifts. We describe a model-based temperature reconstruction method that overcomes these limitations by fitting a library of separated water and fat images measured before heating directly to multi-echo data measured during heating, while accounting for the PRF shift with temperature.ResultsSimulations in a mixed water/fat phantom with focal heating showed that the proposed algorithm reconstructed more accurate temperature maps in mixed tissues compared to a fat-referenced thermometry method. In a porcine phantom experiment with focused ultrasound heating at 1.5 Tesla, temperature maps were accurate to within 1∘ C of fiber optic probe temperature measurements and were calculated in 0.47 s per time point. Free-breathing breast and liver imaging experiments demonstrated motion and off-resonance compensation. The algorithm can also accurately reconstruct water/fat separated temperature maps from a single echo during heating.ConclusionsThe proposed model-based water/fat separated algorithm produces accurate PRF-shift temperature maps in mixed water and fat tissues in the presence of spatiotemporally varying off-resonance and motion.
| S-EPMC6550275 | biostudies-literature
Project description:The diesel soot (DS) coated non-woven fabric was studied for oil-water separation along with the adsorption of dyes, detergents, and pharmaceuticals. The DS coated non-woven fabric showed more than 95% separation efficiency and consistent repeatable performance during oil-water separation experiment. In addition to this, the DS coated non-woven fabric of 17.2 cm2 area successfully adsorbed ~85%, 97%, and 100% methylene blue (MB) dye, ciprofloxacin, and detergent, respectively from their respective solutions within 30 min, which was not possible using uncoated non-woven fabric. The DS coated non-woven fabric was found to be hydrophobic with the contact angle of 140° which was almost invariant upto 60 °C. Hence, the DS coated non-woven fabric showed promising performance in the oil-water separation and adsorption applications.
| S-EPMC6560123 | biostudies-literature
Project description:BackgroundTime-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat-water decomposition MRI.ResultsThis study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs.ConclusionsThis automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.
| S-EPMC7704819 | biostudies-literature