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The role of MRI to estimate liver iron concentration (LIC) for identifying patients with iron overload and guiding the titration of chelation therapy is increasingly established for routine clinical practice. However, the existence of multiple MRI-based LIC quantification techniques limits standardization and widespread clinical adoption. In this article, we review the existing and widely accepted MRI-based LIC estimation methods at 1.5 T and 3 T: signal intensity ratio (SIR) and relaxometry (R2 and R2*) and discuss the basic principles, acquisition and analysis protocols, and MRI-LIC calibrations for each technique. Further, we provide an up-to-date information on MRI vendor implementations and available offline commercial and free software for each MRI-based LIC quantification approach. We also briefly review the emerging and advanced MRI techniques for LIC estimation and their current limitations for clinical use. Lastly, we discuss the implications of MRI-based LIC measurements on clinical use and decision-making in the management of patients with iron overload. Some of the key highlights from this review are as follows: 1) Both R2 and R2* can estimate accurate and reproducible LIC, when validated acquisition parameters and analysis protocols are applied, 2) Although the Ferriscan R2 method has been widely used, recent consensus and guidelines endorse R2*-MRI as the most accurate and reproducible method for LIC estimation, 3) Ongoing efforts aim to establish R2*-MRI as the standard approach for quantifying LIC, and 4) Emerging R2*-MRI techniques employ radial sampling strategies and offer improved motion compensation and broader dynamic range for LIC estimation. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

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PURPOSE: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths. METHODS: A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data from multiple acquisition settings, including differences in field strengths, acceleration, and contrasts. The conditional network is validated using datasets acquired with different acquisition settings. RESULTS: The comparison of the adaptive framework, which trains a single model using the data from all the settings, shows that it can offer consistently improved performance for each acquisition condition. The comparison of the proposed scheme with networks that are trained independently for each acquisition setting shows that it requires less training data per acquisition setting to offer good performance. CONCLUSION: The Ada-MoDL framework enables the use of a single model-based unrolled network for multiple acquisition settings. In addition to eliminating the need to train and store multiple networks for different acquisition settings, this approach reduces the training data needed for each acquisition setting.

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Background The off-label use of ferumoxytol (FE), an intravenous iron preparation for iron deficiency anemia, as a contrast agent for MRI is increasing; therefore, it is critical to understand its pharmacokinetics. Purpose To evaluate the pharmacokinetics of FE in the abdomen and pelvis, as assessed with quantitative 1.5- and 3.0-T MRI relaxometry. Materials and Methods R2*, an MRI technique used to estimate tissue iron content in the abdomen and pelvis, was performed at 1.5 and 3.0 T in 12 healthy volunteers between April 2015 and January 2016. Volunteers were randomly assigned to receive an FE dose of 2 mg per kilogram of body weight (FE2mg) or 4 mg/kg (FE4mg). MRI was repeated at 1.5 and 3.0 T for each volunteer at five time points: days 1, 2, 4, 7, and 30. A radiologist experienced in MRI relaxometry measured R2* in organs of the mononuclear phagocyte system (MPS) (ie, liver, spleen, and bone marrow), non-MPS anatomy (kidney, pancreas, and muscle), inguinal lymph nodes (LNs), and blood pool. A paired Student t test was used to compare changes in tissue R2*. Results Volunteers (six female; mean age, 44.3 years ± 12.2 [standard deviation]) received either FE2 mg (n = 5) or FE4 mg (n = 6). Overall R2* trend analysis was temporally significant (P < .001). Time to peak R2* in the MPS occurred on day 1 for FE2mg and between days 1 and 4 for FE4mg (P < .001 to P < .002). Time to peak R2* in non-MPS anatomy, LNs, and blood pool occurred on day 1 for both doses (P < .001 to P < .09). Except for the spleen (at 1.5 T) and liver, MPS R2* remained elevated through day 30 for both doses (P = .02 to P = .03). Except for the kidney and pancreas, non-MPS, LN, and blood pool R2* returned to baseline levels between days 2 and 4 at FE2mg (P = .06 to P = .49) and between days 4 and 7 at FE4mg (P = .06 to P = .63). There was no difference in R2* change between non-MPS and LN R2* at any time (range, 1-71 sec-1 vs 0-50 sec-1; P = .06 to P = .97). Conclusion The pharmacokinetics of ferumoxytol in lymph nodes are distinct from those in mononuclear phagocyte system (MPS) organs, parallel non-MPS anatomy, and the blood pool. © RSNA, 2019 Online supplemental material is available for this article.

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BACKGROUND: Whole-organ, noninvasive techniques for the detection and quantification of nonalcoholic fatty liver disease features have clinical and research applications. However, the effect of time of day, hydration status, and meals are unknown factors with potential to impact bias, precision, reproducibility, and repeatability of chemical shift-encoded MRI (CSE-MRI) to quantify liver proton density fat fraction (PDFF). PURPOSE: To assess the effect of diurnal variation on PDFF using CSE-MRI, including the effect of time of day, the effect of meals and hydration status, as well as the day to day variability. STUDY TYPE: Prospective. SUBJECTS: Eleven healthy subjects and nine patients with observed hepatic steatosis. FIELD STRENGTH/SEQUENCES: A commercial quantitative confounder-corrected CSE-MRI sequence (IDEAL IQ) and an MR spectroscopy (MRS) sequence (multiecho STEAM) were acquired at 1.5T. ASSESSMENT: MRI-PDFF and MRS-PDFF estimates were compared across six visits (before and after a controlled breakfast, before and after an uncontrolled lunch, at approximately 4 pm, and then before breakfast on the following day) with three repeated measures for a total of 360 MRI-PDFF and MRS-PDFF measurements. STATISTICAL TESTS: Linear regression, Bland-Altman analysis, and mixed effect models were used to determine the bias, precision, and repeatability of PDFF measurements. RESULTS: No statistically significant linear trend was observed across visits for either MRI-PDFF or MRS-PDFF (P = 0.31 and 0.37, respectively). The repeatability was measured to be 0.86% for MRI-PDFF and 1.1% for MRS-PDFF over all six visits. For MRI-PDFF, the variability between all six visits (0.94%) was only slightly higher than within each visit (0.66%), with P < 0.001. For MRS-PDFF, the variability between all six visits was 1.29%, compared with 0.87% within each visit (P < 0.001). DATA CONCLUSION: Our results may indicate that it is not necessary to control for the time of day or the fasting/fed state of the patient when measuring PDFF using CSE-MRI. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:407-414.

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OBJECTIVES: The aim of this study was to determine the relaxation properties of ferumoxytol, an off-label alternative to gadolinium-based contrast agents, under physiological conditions at 1.5 T and 3.0 T. MATERIALS AND METHODS: Ferumoxytol was diluted in gradually increasing concentrations (0.26-4.2 mM) in saline, human plasma, and human whole blood. Magnetic resonance relaxometry was performed at 37°C at 1.5 T and 3.0 T. Longitudinal and transverse relaxation rate constants (R1, R2, R2*) were measured as a function of ferumoxytol concentration, and relaxivities (r1, r2, r2*) were calculated. RESULTS: A linear dependence of R1, R2, and R2* on ferumoxytol concentration was found in saline and plasma with lower R1 values at 3.0 T and similar R2 and R2* values at 1.5 T and 3.0 T (1.5 T: r1saline = 19.9 ± 2.3 smM; r1plasma = 19.0 ± 1.7 smM; r2saline = 60.8 ± 3.8 smM; r2plasma = 64.9 ± 1.8 smM; r2*saline = 60.4 ± 4.7 smM; r2*plasma = 64.4 ± 2.5 smM; 3.0 T: r1saline = 10.0 ± 0.3 smM; r1plasma = 9.5 ± 0.2 smM; r2saline = 62.3 ± 3.7 smM; r2plasma = 65.2 ± 1.8 smM; r2*saline = 57.0 ± 4.7 smM; r2*plasma = 55.7 ± 4.4 smM). The dependence of relaxation rates on concentration in blood was nonlinear. Formulas from second-order polynomial fittings of the relaxation rates were calculated to characterize the relationship between R1blood and R2 blood with ferumoxytol. CONCLUSIONS: Ferumoxytol demonstrates strong longitudinal and transverse relaxivities. Awareness of the nonlinear relaxation behavior of ferumoxytol in blood is important for ferumoxytol-enhanced magnetic resonance imaging applications and for protocol optimization.

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The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi-institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application-specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48-61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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PURPOSE: To evaluate the accuracy and reproducibility of quantitative chemical shift-encoded (CSE) MRI to quantify proton-density fat-fraction (PDFF) in a fat-water phantom across sites, vendors, field strengths, and protocols. METHODS: Six sites (Philips, Siemens, and GE Healthcare) participated in this study. A phantom containing multiple vials with various oil/water suspensions (PDFF:0%-100%) was built, shipped to each site, and scanned at 1.5T and 3T using two CSE protocols per field strength. Confounder-corrected PDFF maps were reconstructed using a common algorithm. To assess accuracy, PDFF bias and linear regression with the known PDFF were calculated. To assess reproducibility, measurements were compared across sites, vendors, field strengths, and protocols using analysis of covariance (ANCOVA), Bland-Altman analysis, and the intraclass correlation coefficient (ICC). RESULTS: PDFF measurements revealed an overall absolute bias (across sites, field strengths, and protocols) of 0.22% (95% confidence interval, 0.07%-0.38%) and R2 > 0.995 relative to the known PDFF at each site, field strength, and protocol, with a slope between 0.96 and 1.02 and an intercept between -0.56% and 1.13%. ANCOVA did not reveal effects of field strength (P = 0.36) or protocol (P = 0.19). There was a significant effect of vendor (F = 25.13, P = 1.07 × 10-10 ) with a bias of -0.37% (Philips) and -1.22% (Siemens) relative to GE Healthcare. The overall ICC was 0.999. CONCLUSION: CSE-based fat quantification is accurate and reproducible across sites, vendors, field strengths, and protocols. Magn Reson Med 77:1516-1524, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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PURPOSE: The purpose of this work was to characterize the effects of concomitant gradients (CGs) on chemical shift encoded (CSE)-based estimation of B0 field map, proton density fat fraction (PDFF), and R2*. THEORY: A theoretical framework was used to determine the effects of CG-induced phase errors on CSE-MRI data. METHODS: Simulations, phantom experiments, and in vivo experiments were conducted at 3 Tesla to assess the effects of CGs on quantitative CSE-MRI techniques. Correction of phase errors attributable to CGs was also investigated to determine whether these effects could be removed. RESULTS: Phase errors attributed to CGs introduce errors in the estimation of B0 field map, PDFF, and R2*. Phantom and in vivo experiments demonstrated that CGs can introduce estimation errors greater than 30 Hz in the B0 field map, 10% in PDFF, and 16 s-1 in R2*, 16 cm off isocenter. However, CG phase correction before parameter estimation was able to reduce estimation errors to less than 10 Hz in the B0 field map, 1% in PDFF, and 2 s-1 in R2*. CONCLUSION: CG effects can impact CSE-MRI, leading to inaccurate estimation of B0 field map, PDFF, and R2*. However, correction for phase errors caused by CGs improve the accuracy of quantitative parameters estimated from CSE-MRI acquisitions. Magn Reson Med 78:730-738, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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PURPOSE: We aimed to determine the agreement between quantitative susceptibility mapping (QSM)-based biomagnetic liver susceptometry (BLS) and confounder-corrected R2* mapping with superconducting quantum interference device (SQUID)-based biomagnetic liver susceptometry in patients with liver iron overload. METHODS: Data were acquired from two healthy controls and 22 patients undergoing MRI and SQUID-BLS as part of routine monitoring for iron overload. Magnetic resonance imaging was performed on a 3T system using a three-dimensional multi-echo gradient-echo acquisition. Both magnetic susceptibility and R2* of the liver were estimated from this acquisition. Linear regression was used to compare estimates of QSM-BLS and R2* to SQUID-BLS. RESULTS: Both QSM-BLS and confounder-corrected R2* were sensitive to the presence of iron in the liver. Linear regression between QSM-BLS and SQUID-BLS demonstrated the following relationship: QSM-BLS = (-0.22 ± 0.11) + (0.49 ± 0.05) · SQUID-BLS with r2 = 0.88. The coefficient of determination between liver R2* and SQUID-BLS was also r2 = 0.88. CONCLUSION: We determined a strong correlation between both QSM-BLS and confounder-corrected R2* to SQUID-BLS. This study demonstrates the feasibility of QSM-BLS and confounder-corrected R2* for assessing liver iron overload, particularly when SQUID systems are not accessible. Magn Reson Med 78:264-270, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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PURPOSE: Ferumoxytol (FE) has gained interest as an alternative to gadolinium-based contrast agents (GBCAs). The purpose of this study was to evaluate and optimize ferumoxytol dose and T1 weighting, in comparison to a conventional GBCA. MATERIALS AND METHODS: Twelve healthy volunteers (six women / six men, mean age 44.3 years) were recruited for this study. Scanning was performed on a clinical 3 Tesla (T) MRI system. Gadobenate dimeglumine (GD)-enhanced MRA was performed followed by FE-enhanced MRA 1 month later. Volunteers were randomly assigned to a diluted (n = 6) or undiluted (n = 6) dose of GD (0.1 mmol/kg), and to FE doses of 4 mg/kg (n = 6) or 2 mg/kg (n = 6). First pass and steady-state MRA were performed for GD- and FE-enhanced MRA. Flip-angle optimization was performed after FE administration. Quantitative analysis included relative contrast-to-noise ratio (relCNR) measurements for all acquisitions. First pass GD- and FE-enhanced MRA images were evaluated qualitatively. RESULTS: RelCNR was significantly higher with undiluted GD (31.8, 95% confidence interval [CI], 27.7-35.9) compared with diluted GD (16.2; 95% CI, 12.2-20.3; P = 0.001) and both 4 mg/kg FE (12.5; 95% CI, 8.5-16.4; P < 0.001) and 2 mg/kg FE (9.1; 95% CI, 5.1-13.2; P < 0.001) during first pass. Relative CNR did not decrease with FE 5 min postinjection compared with GD. Flip-angle analysis revealed relative CNR-peaks at 30° for FE 4 mg/kg and at 20° for FE 2 mg/kg. Diluted GD (P = 0.013) and FE 4 mg/kg (P = 0.01) revealed significantly higher image quality scores compared with undiluted GD during first pass. CONCLUSION: This study shows an equivalent image quality of FE and GD for first pass MRA even though GD showed significantly higher relative CNR. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1617-1626.

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UNLABELLED: Emerging magnetic resonance imaging (MRI) biomarkers of hepatic steatosis have demonstrated tremendous promise for accurate quantification of hepatic triglyceride concentration. These methods quantify the proton density fat-fraction (PDFF), which reflects the concentration of triglycerides in tissue. Previous in vivo studies have compared MRI-PDFF with histologic steatosis grading for assessment of hepatic steatosis. However, the correlation of MRI-PDFF with the underlying hepatic triglyceride content remained unknown. The aim of this ex vivo study was to validate the accuracy of MRI-PDFF as an imaging biomarker of hepatic steatosis. Using ex vivo human livers, we compared MRI-PDFF with magnetic resonance spectroscopy-PDFF (MRS-PDFF), biochemical triglyceride extraction, and histology as three independent reference standards. A secondary aim was to compare the precision of MRI-PDFF relative to biopsy for the quantification of hepatic steatosis. MRI-PDFF was prospectively performed at 1.5 Tesla in 13 explanted human livers. We performed colocalized paired evaluation of liver fat content in all nine Couinaud segments using single-voxel MRS-PDFF (n=117) and tissue wedges for biochemical triglyceride extraction (n=117), and five core biopsies performed in each segment for histologic grading (n=585). Accuracy of MRI-PDFF was assessed through linear regression with MRS-PDFF, triglyceride extraction, and histology. Intraobserver agreement, interobserver agreement, and repeatability of MRI-PDFF and histologic grading were assessed through Bland-Altman analyses. MRI-PDFF showed an excellent correlation with MRS-PDFF (r=0.984, confidence interval 0.978-0.989) and strong correlation with histology (r=0.850, confidence interval 0.791-0.894) and triglyceride extraction (r=0.871, confidence interval 0.818-0.909). Intraobserver agreement, interobserver agreement, and repeatability showed a significantly smaller variance for MRI-PDFF than for histologic steatosis grading (all P<0.001). CONCLUSION: MRI-PDFF is an accurate, precise, and reader-independent noninvasive imaging biomarker of liver triglyceride content, capable of steatosis quantification over the entire liver.

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PURPOSE: The purpose of this work was to develop and demonstrate feasibility and initial clinical validation of quantitative susceptibility mapping (QSM) in the abdomen as an imaging biomarker of hepatic iron overload. THEORY AND METHODS: In general, QSM is faced with the challenges of background field removal and dipole inversion. Respiratory motion, the presence of fat, and severe iron overload further complicate QSM in the abdomen. We propose a technique for QSM in the abdomen that addresses these challenges. Data were acquired from 10 subjects without hepatic iron overload and 33 subjects with known or suspected iron overload. The proposed technique was used to estimate the susceptibility map in the abdomen, from which hepatic iron overload was measured. As a reference, spin-echo data were acquired for R2-based LIC estimation. Liver R2* was measured for correlation with liver susceptibility estimates. RESULTS: Correlation between susceptibility and R2-based LIC estimation was R(2) = 0.76 at 1.5 Tesla (T) and R(2) = 0.83 at 3T. Furthermore, high correlation between liver susceptibility and liver R2* (R(2) = 0.94 at 1.5T; R(2) = 0.93 at 3T) was observed. CONCLUSION: We have developed and demonstrated initial validation of QSM in the abdomen as an imaging biomarker of hepatic iron overload.

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PURPOSE: The purpose of this work was to improve the robustness of existing chemical shift encoded water-fat separation methods by incorporating object-based information of the B0 field inhomogeneity. THEORY: The primary challenge in water-fat separation is the estimation of phase shifts that arise from B0 field inhomogeneity, which is composed of the background field and susceptibility-induced field. The susceptibility-induced field can be estimated if the susceptibility distribution is known or can be approximated. In this work, the susceptibility distribution is approximated from the source images using the known susceptibility values of water, fat, and air. The field estimate is then demodulated from the source images before water-fat separation. METHODS: Chemical shift encoded source images were acquired in anatomical regions that are prone to water-fat swaps. The images were processed using algorithms from the ISMRM Fat-Water Toolbox, with and without the object-based field map information. The estimates were compared to examine the benefit of using the object-based field map information. RESULTS: Multiple cases are shown in which water-fat swaps were avoided by using the object-based information of the B0 field map. CONCLUSION: Object-based information of the B0 field may improve the robustness of existing chemical shift encoded water-fat separation methods.

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PURPOSE: To characterize the confounding effect of temperature on chemical shift-encoded (CSE) fat quantification. METHODS: The proton resonance frequency of water, unlike triglycerides, depends on temperature. This leads to a temperature dependence of the spectral models of fat (relative to water) that are commonly used by CSE-MRI methods. Simulation analysis was performed for 1.5 Tesla CSE fat-water signals at various temperatures and echo time combinations. Oil-water phantoms were constructed and scanned at temperatures between 0 and 40°C using spectroscopy and CSE imaging at three echo time combinations. An explanted human liver, rejected for transplantation due to steatosis, was scanned using spectroscopy and CSE imaging. Fat-water reconstructions were performed using four different techniques: magnitude and complex fitting, with standard or temperature-corrected signal modeling. RESULTS: In all experiments, magnitude fitting with standard signal modeling resulted in large fat quantification errors. Errors were largest for echo time combinations near TEinit ≈ 1.3 ms, ΔTE ≈ 2.2 ms. Errors in fat quantification caused by temperature-related frequency shifts were smaller with complex fitting, and were avoided using a temperature-corrected signal model. CONCLUSION: Temperature is a confounding factor for fat quantification. If not accounted for, it can result in large errors in fat quantifications in phantom and ex vivo acquisitions.

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PURPOSE: The aim of this study was to determine to what degree current compressed sensing methods are capable of accelerating clinical magnetic resonance neuroimaging sequences. METHODS: Two 2-dimensional clinical sequences were chosen for this study because of their long scan times. A pilot study was used to establish the sampling scheme and regularization parameter needed in compressed sensing reconstruction. These findings were used in a subsequent blinded study in which images reconstructed using compressed sensing were evaluated by 2 board-certified neuroradiologists. Image quality was evaluated at up to 10 anatomical features. RESULTS: The findings indicate that compressed sensing may provide 2-fold acceleration of certain clinical magnetic resonance neuroimaging sequences. A global ringing artifact and image blurring were identified as the 2 primary artifacts that would hinder the ability to confidently discern abnormality. CONCLUSION: Compressed sensing is able to moderately accelerate certain neuroimaging sequences without severe loss of clinically relevant information. For those sequences with coarser spatial resolution and/or at a higher acceleration factor, artifacts degrade the quality of the reconstructed image to a point where they are of little to no clinical value.

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PURPOSE: To develop a T2*-compensated parallel imaging and compressed sensing framework for water-fat separation, and to demonstrate accelerated quantitative imaging of proton density fat fraction. MATERIALS AND METHODS: The proposed method extends a previously developed framework for water-fat separation by additionally compensating for T2* decay. A two-stage estimation was formulated that first determines an approximation of the B0 field map and then jointly estimates and refines the R2* (=1/T2*) and B0 field maps, respectively. The method was tested using a set of water-fat phantoms as well as liver datasets that were acquired from seven asymptomatic adult volunteers. The fat fraction estimates were compared to those from a commonly used nonaccelerated water-fat imaging method and also to a sequential parallel imaging and water-fat imaging method. RESULTS: The proposed method properly compensated for T2* decay to yield accurate fat fraction estimates in the water-fat phantoms. Further, linear regression analysis from the liver datasets showed that the proposed method accurately estimated fat fraction at acceleration factors that were higher than those achievable by the sequential parallel imaging and water-fat imaging method. Accurate fat fraction estimates were demonstrated at acceleration factors up to 4×, although some image artifacts were observed. CONCLUSION: The proposed T2*-compensated parallel imaging and compressed sensing framework demonstrates the potential to further accelerate water-fat imaging while maintaining accurate estimates of proton density fat fraction.

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Chemical shift encoded techniques have received considerable attention recently because they can reliably separate water and fat in the presence of off-resonance. The insensitivity to off-resonance requires that data be acquired at multiple echo times, which increases the scan time as compared to a single echo acquisition. The increased scan time often requires that a compromise be made between the spatial resolution, the volume coverage, and the tolerance to artifacts from subject motion. This work describes a combined parallel imaging and compressed sensing approach for accelerated water-fat separation. In addition, the use of multiscale cubic B-splines for B(0) field map estimation is introduced. The water and fat images and the B(0) field map are estimated via an alternating minimization. Coil sensitivity information is derived from a calculated k-space convolution kernel and l(1)-regularization is imposed on the coil-combined water and fat image estimates. Uniform water-fat separation is demonstrated from retrospectively undersampled data in the liver, brachial plexus, ankle, and knee as well as from a prospectively undersampled acquisition of the knee at 8.6x acceleration.

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Water-fat separation techniques play an important role in a variety of clinical and research applications. In particular, multiecho separation methods remain a topic of great interest due to their ability to resolve water and fat images in the presence of B(0)-field inhomogeneity. However, these methods are inherently slow as they require multiple measurements. An accelerated technique with reduced k-space sampling is desirable to decrease the scan time. This work presents a new method for water-fat separation from accelerated multiecho acquisitions. The proposed approach does not require the region-growing or region-merging schemes that are typically used for field map estimation. Instead, the water, fat, and field map signals are estimated directly from the undersampled k-space measurements. In this work, up to 2.5×-acceleration is demonstrated in a water-fat phantom, ankle, knee, and liver.

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