Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm

Unwrapping phase contrast MRI by iterative graph cuts

PURPOSE

To develop and evaluate a phase unwrapping method for cine phase contrast MRI based on graph cuts.

METHODS

A proposed Iterative Graph Cuts method was evaluated in 10 cardiac patients with two-dimensional flow quantification which was repeated at low venc settings to provoke wrapping. The images were also unwrapped by a path-following method (ROMEO), and a Laplacian-based method (LP). Net flow was quantified using semi-automatic vessel segmentation. High venc images were also wrapped retrospectively to asses the residual amount of wrapped voxels.

RESULTS

The absolute net flow error after unwrapping at venc = 100 cm/s was 1.8 mL, which was 0.83 mL smaller than for LP. The repeatability error at high venc without unwrapping was 2.5 mL. The error at venc = 50 cm/s was 7.5 mL, which was 8.2 mL smaller than for ROMEO and 5.7 mL smaller than for LP. For retrospectively wrapped images with synthetic venc of 100/50/25 cm/s, the residual amount of wrapped voxels was 0.00/0.12/0.79%, which was 0.09/0.26/8.0 percentage points smaller than for LP. With synthetic venc of 25 cm/s, omitting magnitude information resulted in 3.2 percentage points more wrapped voxels, and only spatial/temporal unwrapping resulted in 4.6/21 percentage points more wrapped voxels compared to spatiotemporal unwrapping.

CONCLUSION

Iterative Graph Cuts enables unwrapping of cine phase contrast MRI with very small errors, except for at extreme blood velocities, with equal or better performance compared to ROMEO and LP. The use of magnitude information and spatiotemporal unwrapping is recommended.

Water-Fat Separation for the Knee on a 50 mT Portable MRI Scanner

OBJECTIVE

The Dixon method is frequently employed in clinical and scientific research for fat suppression, because it has lower sensitivity to static magnetic field inhomogeneity compared to chemical shift selective saturation or its variants and maintains image signal-to-noise ratio (SNR). Recently, research on very-low-field (VLF < 100 mT) magnetic resonance imaging (MRI) has regained popularity. However, there is limited literature on water-fat separation in VLF MRI. Here, we present a modified two-point Dixon method specifically designed for VLF MRI.

METHODS

Most experiments were performed on a homemade 50 mT portable MRI scanner. The receiving coil adopted a homemade quadrature receiving coil. The data were acquired using spin-echo and gradient-echo sequences. We considered the T2* effect, and added priori information to existing two-point Dixon method. Then, the method used regional iterative phasor extraction (RIPE) to extract the error phasor. Finally, least squares solutions for water and fat were obtained and fat signal fraction was calculated.

RESULTS

For phantom evaluation, water-only and fat-only images were obtained and the local fat signal fractions were calculated, with two samples being 0.94 and 0.93, respectively. For knee imaging, cartilage, muscle and fat could be clearly distinguished. The water-only images were able to highlight areas such as cartilage that could not be easily distinguished without separation.

CONCLUSION

This work has demonstrated the feasibility of using a 50 mT MRI scanner for water-fat separation.

SIGNIFICANCE

To the best of our knowledge, this is the first reported result of water-fat separation at a 50 mT portable MRI scanner.

Comparative review of algorithms and methods for chemical-shift-encoded quantitative fat-water imaging

PURPOSE

To propose a standardized comparison between state-of-the-art open-source fat-water separation algorithms for proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ quantification using an open-source multi-language toolbox.

METHODS

Eight recent open-source fat-water separation algorithms were compared in silico, in vitro, and in vivo. Multi-echo data were synthesized with varying fat-fractions, B0 off-resonance, SNR and TEs. Experimental evaluation was conducted using calibrated fat-water phantoms acquired at 3T and multi-site open-source phantoms data. Algorithms' performances were observed on challenging in vivo datasets at 3T. Finally, reconstruction algorithms were investigated with different fat spectra to evaluate the importance of the fat model.

RESULTS

In silico and in vitro results proved most algorithms to be not sensitive to fat-water swaps andB 0 $$ {\mathrm{B}}_0 $$ offsets with five or more echoes. However, two methods remained inaccurate even with seven echoes and SNR = 50, and two other algorithms' precision depended on the echo spacing scheme (p < 0.05). The remaining four algorithms provided reliable performances with limits of agreement under 2% for PDFF and 6 s-1 forR 2 * $$ {R}_2^{\ast } $$ . The choice of fat spectrum model influenced quantification of PDFF mildly (<2% bias) and ofR 2 * $$ {R}_2^{\ast } $$ more severely, with errors up to 20 s-1 .

CONCLUSION

In promoting standardized comparisons of MRI-based fat and iron quantification using chemical-shift encoded multi-echo methods, this benchmark work has revealed some discrepancies between recent approaches for PDFF andR 2 * $$ {R}_2^{\ast } $$ mapping. Explicit choices and parameterization of the fat-water algorithm appear necessary for reproducibility. This open-source toolbox further enables the user to optimize acquisition parameters by predicting algorithms' margins of errors.

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation

Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.

Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R2 * quantification using self-gated stack-of-radial MRI

PURPOSE

To develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R2 * quantification with built-in uncertainty estimation using self-gated free-breathing stack-of-radial MRI.

METHODS

This work developed an uncertainty-aware physics-driven deep learning network (UP-Net) to (1) suppress radial streaking artifacts because of undersampling after self-gating, (2) calculate accurate quantitative maps, and (3) provide pixel-wise uncertainty maps. UP-Net incorporated a phase augmentation strategy, generative adversarial network architecture, and an MRI physics loss term based on a fat-water and R2 * signal model. UP-Net was trained and tested using free-breathing multi-echo stack-of-radial MRI data from 105 subjects. UP-Net uncertainty scores were calibrated in a validation dataset and used to predict quantification errors for liver PDFF and R2 * in a testing dataset.

RESULTS

Compared with images reconstructed using compressed sensing (CS), UP-Net achieved structural similarity index >0.87 and normalized root mean squared error <0.18. Compared with reference quantitative maps generated using CS and graph-cut (GC) algorithms, UP-Net achieved low mean differences (MD) for liver PDFF (-0.36%) and R2 * (-0.37 s-1 ). Compared with breath-holding Cartesian MRI results, UP-Net achieved low MD for liver PDFF (0.53%) and R2 * (6.75 s-1 ). UP-Net uncertainty scores predicted absolute liver PDFF and R2 * errors with low MD of 0.27% and 0.12 s-1 compared to CS + GC results. The computational time for UP-Net was 79 ms/slice, whereas CS + GC required 3.2 min/slice.

CONCLUSION

UP-Net rapidly calculates accurate liver PDFF and R2 * maps from self-gated free-breathing stack-of-radial MRI. The pixel-wise uncertainty maps from UP-Net predict quantification errors in the liver.

On the water-fat in-phase assumption for quantitative susceptibility mapping

PURPOSE

To (a) define multi-peak fat model-based effective in-phase echo times for quantitative susceptibility mapping (QSM) in water-fat regions, (b) analyze the relationship between fat fraction, field map quantification bias and susceptibility bias, and (c) evaluate the susceptibility mapping performance of the proposed effective in-phase echoes in comparison to single-peak in-phase echoes and water-fat separation for regions where both water and fat are present.

METHODS

Effective multipeak in-phase echo times for a bone marrow and a liver fat spectral model were derived from a single voxel simulation. A Monte Carlo simulation was performed to assess the field map estimation error as a function of fat fraction for the different in-phase echoes. Additionally, a phantom scan and in vivo scans in the liver, spine, and breast were performed and evaluated with respect to quantification accuracy.

RESULTS

The use of single-peak in-phase echoes can introduce a worst-case susceptibility bias of 0.43 $$ 0.43 $$  ppm. The use of effective multipeak in-phase echoes shows a similar quantitative performance in the numerical simulation, the phantom and in all in vivo anatomies when compared to water-fat separation-based QSM.

CONCLUSION

QSM based on the proposed effective multipeak in-phase echoes can alleviate the quantification bias present in QSM based on single-peak in-phase echoes. When compared to water-fat separation-based QSM the proposed effective in-phase echo times achieve a similar quantitative performance while drastically reducing the computational expense for field map estimation.

Assessment of Fat Fractions in the Tongue, Soft Palate, Pharyngeal Wall, and Parapharyngeal Fat Pad by the GOOSE and DIXON Methods

OBJECTIVE

The 2-point DIXON method is widely used to assess fat fractions (FFs) in magnetic resonance images (MRIs) of the tongue, pharyngeal wall, and surrounding tissues in patients with obstructive sleep apnea (OSA). However, the method is semiquantitative and is susceptible to B0 field inhomogeneities and R2* confounding factors. Using the method, although several studies have shown that patients with OSA have increased fat deposition around the pharyngeal cavity, conflicting findings was also reported in 1 study. This discrepancy necessitates that we examine the FF estimation method used in the earlier studies and seek a more accurate method to measure FFs.

MATERIALS AND METHODS

We examined the advantages of using the GOOSE (globally optimal surface estimation) method to replace the 2-point DIXON method for quantifying fat in the tongue and surrounding tissues on MRIs. We first used phantoms with known FFs (true FFs) to validate the GOOSE method and examine the errors in the DIXON method. Then, we compared the 2 methods in the tongue, soft palate, pharyngeal wall, and parapharyngeal fat pad of 63 healthy participants to further assess the errors caused by the DIXON method. Six participants were excluded from the comparison of the tongue FFs because of technical failures. Paired Student t tests were performed on FFs to detect significant differences between the 2 methods. All measures were obtained using 3 T Siemens MRI scanners.

RESULTS

In the phantoms, the FFs measured by GOOSE agreed with the true FF, with only a 1.2% mean absolute error. However, the same measure by DIXON had a 10.5% mean absolute error. The FFs obtained by DIXON were significantly lower than those obtained by GOOSE (P < 0.0001) in the human participants. We found strong correlations between GOOSE and DIXON in the tongue (R2 = 0.90), soft palate (R2 = 0.66), and parapharyngeal fat pad (R2 = 0.88), but the correlation was weaker in the posterior pharyngeal walls (R2 = 0.32) in participants.

CONCLUSIONS

The widely used 2-point DIXON underestimated FFs, relative to GOOSE, in phantom measurements and tissues studied in vivo. Thus, an advanced method, such as GOOSE, that uses multiecho complex data is preferred for estimating FF.

Deep Learning-Based Parameter Mapping with Uncertainty Estimation for Fat Quantification using Accelerated Free-Breathing Radial MRI

Deep learning has been applied to remove artifacts from undersampled MRI and to replace time-consuming signal fitting in quantitative MRI, but these have usually been treated as separate tasks, which does not fully exploit the shared information. This work proposes a new two-stage framework that completes these two tasks in a concerted approach and also estimates the pixel-wise uncertainty levels. Results from accelerated free-breathing radial MRI for liver fat quantification demonstrate that the proposed framework can achieve high image quality from undersampled radial data, high accuracy for liver fat quantification, and detect uncertainty caused by noisy input data. The proposed framework achieved 3-fold acceleration to <1 min scan time and reduced the computational time for signal fitting to <100 ms/slice in free-breathing liver fat quantification.

Lipid droplet-size mapping in human adipose tissue using a clinical 3T system

PURPOSE

To develop a methodology for probing lipid droplet sizes with a clinical system based on a diffusion-weighted stimulated echo-prepared turbo spin-echo sequence and to validate the methodology in water-fat emulsions and show its applicability in ex vivo adipose-tissue samples.

METHODS

A diffusion-weighted stimulated echo-prepared preparation was combined with a single-shot turbo spin-echo readout for measurements at different b-values and diffusion times. The droplet size was estimated with an analytical expression, and three fitting approaches were compared: magnitude-based spatial averaging with voxel-wise residual minimization, complex-based spatial averaging with voxel-wise residual minimization, and complex-based spatial averaging with neighborhood-regularized residual minimization. Simulations were performed to characterize the fitting residual landscape and the approaches' noise performance. The applicability was assessed in oil-in-water emulsions in comparison with laser deflection and in ten human white adipose tissue samples in comparison with histology.

RESULTS

The fitting residual landscape showed a minimum valley with increasing extent as the droplet size increased. In phantoms, a very good agreement of the mean droplet size was observed between the diffusion-weighted MRI-based and the laser deflection measurements, showing the best performance with complex-based spatial averaging with neighborhood-regularized residual minimization processing (R² /P: 0.971/0.014). In the human adipose-tissue samples, complex-based spatial averaging with neighborhood-regularized residual minimization processing showed a significant correlation (R² /P: 0.531/0.017) compared with histology.

CONCLUSION

The proposed acquisition and parameter-estimation methodology was able to probe restricted diffusion effects in lipid droplets. The methodology was validated using phantoms, and its feasibility in measuring an apparent lipid droplet size was demonstrated ex vivo in white adipose tissue.

Deep Learning-Based Parameter Mapping with Uncertainty Estimation for Fat Quantification using Accelerated Free-Breathing Radial MRI

Deep learning has been applied to remove artifacts from undersampled MRI and to replace time-consuming signal fitting in quantitative MRI, but these have usually been treated as separate tasks, which does not fully exploit the shared information. This work proposes a new two-stage framework that completes these two tasks in a concerted approach and also estimates the pixel-wise uncertainty levels. Results from accelerated free-breathing radial MRI for liver fat quantification demonstrate that the proposed framework can achieve high image quality from undersampled radial data, high accuracy for liver fat quantification, and detect uncertainty caused by noisy input data. The proposed framework achieved 3-fold acceleration to <1 min scan time and reduced the computational time for signal fitting to <100 ms/slice in free-breathing liver fat quantification.

[Regional iterative phase extraction Dixon water-lipid separation method based on second order differential quality weighting]

OBJECTIVE

To propose a regional iterative phase extraction Dixon method based on second order difference quality weighting (SOD-RIPE) for improving water-lipid separation in heterogeneous magnetic field.

OBJECTIVE

The in-phase angle of the asymmetric in-phase and opposite phase image matrix was eliminated using the Dixon's signal model to obtain J1 and J2, from which the possible water signal magnitude (B, S) and in-phase and opposite phase difference (P1, P2) was derived using the Cosine law. The phase quality map R of J2 was calculated using the second-order difference, and the phase difference was weighted to obtain (Pv, Pu). The Pv and Pu were iteratively selected to obtain the Pf. The water-fat separation diagram (W, F) was obtained using the least square method to bring the Pf into the Dixon signal model.

OBJECTIVE

Water-lipid separation was performed using 1000 pairs of in- and opposite-phase images on a Philips in Genia II 3.0T magnetic resonance scanner. The SOD-RIPE algorithm achieved better separation and stability than the automatic growth method and RIPE in all the parts of the body and in the stability test, and had a similar performance to mDixon-XD algorithm.

OBJECTIVE

SOD-RIPE can achieve robust water-fat separation with a good stability and can be used as a general Dixon water-fat separation method.

Improved body quantitative susceptibility mapping by using a variable-layer single-min-cut graph-cut for field-mapping

PURPOSE

To develop a robust algorithm for field-mapping in the presence of water-fat components, large B 0 field inhomogeneities and MR signal voids and to apply the developed method in body applications of quantitative susceptibility mapping (QSM).

METHODS

A framework solving the cost-function of the water-fat separation problem in a single-min-cut graph-cut based on the variable-layer graph construction concept was developed. The developed framework was applied to a numerical phantom enclosing an MR signal void, an air bubble experimental phantom, 14 large field of view (FOV) head/neck region in vivo scans and to 6 lumbar spine in vivo scans. Field-mapping and subsequent QSM results using the proposed algorithm were compared to results using an iterative graph-cut algorithm and a formerly proposed single-min-cut graph-cut.

RESULTS

The proposed method was shown to yield accurate field-map and susceptibility values in all simulation and in vivo datasets when compared to reference values (simulation) or literature values (in vivo). The proposed method showed improved field-map and susceptibility results compared to iterative graph-cut field-mapping especially in regions with low SNR, strong field-map variations and high R 2 ∗ values.

CONCLUSIONS

A single-min-cut graph-cut field-mapping method with a variable-layer construction was developed for field-mapping in body water-fat regions, improving quantitative susceptibility mapping particularly in areas close to MR signal voids.

Simultaneous proton density fat-fraction and R 2 ∗ imaging with water-specific T1 mapping (PROFIT1 ): application in liver

PURPOSE

To describe and validate a simultaneous proton density fat-fraction (PDFF) imaging and water-specific T₁ mapping (T_1(Water) ) approach for the liver (PROFIT₁ ) with R 2 ∗ mapping and low sensitivity to B 1 + calibration or inhomogeneity.

METHODS

A multiecho gradient-echo sequence, with and without saturation preparation, was designed for simultaneous imaging of liver PDFF, R 2 ∗ , and T_1(Water) (three slices in ~13 seconds). Chemical-shift-encoded MRI processing yielded fat-water separated images and R 2 ∗ maps. T_1(Water)  calculation utilized saturation and nonsaturation-recovery water-separated images. Several variable flip angle schemes across k-space (increasing flip angles in sequential RF pulses) were evaluated for minimization of T₁ weighting, to reduce the B 1 + dependence of T_1(Water)  and PDFF (reduced flip angle dependence). T_1(Water)  accuracy was validated in mixed fat-water phantoms, with various PDFF and T₁ values (3T). In vivo application was illustrated in five volunteers and five patients with nonalcoholic fatty liver disease (PDFF, T_1(Water) , R 2 ∗ ).

RESULTS

A sin³ (θ) flip angle pattern (0 < θ < π/2 over k-space) yielded the largest PROFIT₁ signal yield with negligible B 1 + dependence for both T_1(Water) and PDFF. Mixed fat-water phantom experiments illustrated excellent agreement between PROFIT₁ and gold-standard spectroscopic evaluation of PDFF and T_1(Water)  (<1% T₁ error). In vivo PDFF, T_1(Water) , and R 2 ∗ maps illustrated independence of the PROFIT₁ values from B 1 + inhomogeneity and significant differences between volunteers and patients with nonalcoholic fatty liver disease for T_1(Water) (927 ± 56 ms vs. 1033 ± 23 ms; P < .05) and PDFF (2.0% ± 0.8% vs. 13.4% ± 5.0%, P < .05).  R 2 ∗ was similar between groups.

CONCLUSION

The PROFIT₁ pulse sequence provides fast simultaneous quantification of PDFF, T_1(Water) , and R 2 ∗ with minimal sensitivity to B 1 + miscalibration or inhomogeneity.

Resolving estimation uncertainties of chemical shift encoded fat-water imaging using magnetization transfer effect

PURPOSE

B₀ field inhomogeneity may cause significant errors in chemical shift encoding-based fat-water (F/W) separation. We describe a new approach to improve its robustness using novel B₀ field map pre-estimation.

METHODS

Our method exploits insensitivity of fat to magnetization transfer effect, which allows generating fat-insensitive B₀ field priors with full or partial spatial support using a low-resolution magnetization transfer-weighted scan. The full prior can be employed by most F/W separation methods for initialization or data demodulation. We also propose a modified region-growing algorithm in which the partial prior is utilized for its initial seeding.

RESULTS

The magnetization transfer-based B₀ priors significantly reduced F/W errors of three representative F/W separation methods in all cases. In cases with moderate B₀ inhomogeneity, the full prior allowed error-free separation even with basic, voxel-independent processing. When coupled with methods exploiting B₀ field smoothness, it significantly improved separation accuracy even in the presence of strong inhomogeneities. Seeding the region-growing with the partial prior significantly improved performance of F/W separation, including cases with spatially disconnected tissues.

CONCLUSION

Magnetization transfer-based B₀ field pre-estimation provides valuable prior information for F/W separation, which may significantly improve its robustness at the expense of nominal (< 5%-10%) scan time increase.

Optimal surface segmentation with convex priors in irregularly sampled space

Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Thus the segmentation cannot attain an accuracy greater than a single unit voxel, i.e. the distance between two adjoining nodes in graph space. Segmentation accuracy higher than a unit voxel is achievable by exploiting partial volume information in the voxels which shall result in non-equidistant spacing between adjoining graph nodes. This paper reports a generalized graph based multiple surface segmentation method with convex priors which can optimally segment the target surfaces in an irregularly sampled space. The proposed method allows non-equidistant spacing between the adjoining graph nodes to achieve subvoxel segmentation accuracy by utilizing the partial volume information in the voxels. The partial volume information in the voxels is exploited by computing a displacement field from the original volume data to identify the subvoxel-accurate centers within each voxel resulting in non-equidistant spacing between the adjoining graph nodes. The smoothness of each surface modeled as a convex constraint governs the connectivity and regularity of the surface. We employ an edge-based graph representation to incorporate the necessary constraints and the globally optimal solution is obtained by computing a minimum s-t cut. The proposed method was validated on 10 intravascular multi-frame ultrasound image datasets for subvoxel segmentation accuracy. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional segmentations.

Measurement and correction of the bulk magnetic susceptibility effects of fat: application in venous oxygen saturation imaging

PURPOSE

To develop a correction method for the effects of the magnetic susceptibility of fat (χ_Fat ) on the calculation of venous oxygen saturation (SvO₂ ).

THEORY

The magnetic field shifts associated with the magnetic susceptibility of deoxyhemoglobin can be used to estimate SvO₂ , a measure of oxygen extraction and metabolism. However, the distinct magnetic susceptibility of fat surrounding targeted veins will give rise to magnetic field perturbations that will extend into the vein and surrounding tissues, potentially confounding the calculation of SvO₂ .

METHODS

Multi-echo modified Dixon fat-water separated imaging was used to quantify fat-water distributions around the superficial femoral vein (venous return from the lower leg). Fat fraction images were used to generate χ_Fat images, to calculate and remove the associated fat-susceptibility-induced magnetic field shifts before the estimation of SvO₂ . This approach was evaluated at rest and with plantar flexion exercise to evaluate calf muscle oxygen extraction in 10 healthy subjects.

RESULTS

The presence of fat around the vein resulted in complex magnetic field shifts and errors in estimated SvO₂ . Corrected resting SvO₂ values were significantly larger than those measured with conventional methods, at rest (72.6 ± 11.0% vs. 65.2 ± 12.2%, P < 0.05) and post-exercise (37.4 ± 12.3% vs. 31.7 ± 12.7%, P < 0.05), with larger errors in individuals and/or regions with increased fat volumes. Estimation and removal of the field-effects from χ_Fat enabled the use of fat tissues for the measurement and removal of the background magnetic field.

CONCLUSIONS

The magnetic susceptibility effects of fat can confound SvO₂ estimation, but the susceptibility field effects can estimated and removed with the use of modified Dixon fat-water separated imaging.

On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density

Purpose

To develop a methodological framework to simultaneously measure R2* and magnetic susceptibility in trabecularized yellow bone marrow and to investigate the sensitivity of Quantitative Susceptibility Mapping (QSM) for measuring trabecular bone density using a non‐UTE multi‐gradient echo sequence.

Methods

The ankle of 16 healthy volunteers and two patients was scanned using a time‐interleaved multi‐gradient‐echo (TIMGRE) sequence. After field mapping based on water–fat separation methods and background field removal based on the Laplacian boundary value method, three different QSM dipole inversion schemes were implemented. Mean susceptibility values in regions of different trabecular bone density in the calcaneus were compared to the corresponding values in the R2* maps, bone volume to total volume ratios (BV/TV) estimated from high resolution imaging (in 14 subjects), and CT attenuation (in two subjects). In addition, numerical simulations were performed in a simplified trabecular bone model of randomly positioned spherical bone inclusions to verify and compare the scaling of R2* and susceptibility with BV/TV.

Results

Differences in calcaneus trabecularization were well depicted in susceptibility maps, in good agreement with high‐resolution MR and CT images. Simulations and in vivo scans showed a linear relationship of measured susceptibility with BV/TV and R2*. The ankle in vivo results showed a strong linear correlation between susceptibility and R2* (R² = 0.88, p < 0.001) with a slope and intercept of −0.004 and 0.2 ppm, respectively.

Conclusions

A method for multi‐paramteric mapping, including R2*‐mapping and QSM was developed for measuring trabecularized yellow bone marrow, showing good sensitivity of QSM for measuring trabecular bone density.

A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE)

PURPOSE

To improve the graph model of our previous work GOOSE for fat-water decomposition with higher computational efficiency and quantitative accuracy.

METHODS

A modification of the GOOSE fat water decomposition algorithm is introduced while the global convergence guarantees of GOOSE are still inherited to minimize fat-water swaps and phase wraps. In this paper, two non-equidistant graph optimization frameworks are proposed as a single-step framework termed as rapid GOOSE (R-GOOSE), and a multi-step framework termed as multi-scale R-GOOSE (mR-GOOSE). Both frameworks contain considerably less graph connectivity than GOOSE, resulting in a great computation reduction thus making it readily applicable to multidimensional fat water applications. The quantitative accuracy and computational time of the novel frameworks are compared with GOOSE on the 2012 ISMRM Challenge datasets to demonstrate the improvement in performance.

RESULTS

Both frameworks accomplish the same level of high accuracy as GOOSE among all datasets. Compared to 100 layers in GOOSE, only 8 layers were used in the new graph model. Computational time is lowered by an order of magnitude to around 5 s for each dataset in (mR-GOOSE), R-GOOSE achieves an average run-time of 8 s.

CONCLUSION

The proposed method provides fat-water decomposition results with a lower run-time and higher accuracy compared to the previously proposed GOOSE algorithm. Magn Reson Med 79:2401-2407, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Water-fat separation incorporating spatial smoothing is robust to noise

PURPOSE

To develop and evaluate a noise-robust method for reconstruction of water and fat images for spoiled gradient multi-echo sequences.

METHODS

The proposed method performs water-fat separation by using a graph cut to minimize an energy function consisting of unary and binary terms. Spatial smoothing is incorporated to increase robustness to noise. The graph cut can fail to find a solution covering the entire image, in which case the relative weighting of the unary term is iteratively increased until a complete solution is found. The proposed method was compared to two previously published methods. Reconstructions were performed on 16 cases taken from the 2012 ISMRM water-fat reconstruction challenge dataset, for which reference reconstructions were provided. Robustness towards noise was evaluated by reconstructing images with different levels of noise added. The percentage of water-fat swaps were calculated to measure performance.

RESULTS

At low noise levels the proposed method produced similar results to one of the previously published methods, while outperforming the other. The proposed method significantly outperformed both of the previously published methods at moderate and high noise levels.

CONCLUSION

By incorporating spatial smoothing, an increased robustness towards noise is achieved when performing water-fat reconstruction of spoiled gradient multi-echo sequences.

Improving chemical shift encoding-based water-fat separation based on a detailed consideration of magnetic field contributions

Purpose

To improve the robustness of existing chemical shift encoding‐based water–fat separation methods by incorporating a priori information of the magnetic field distortions in complex‐based water–fat separation.

Methods

Four major field contributions are considered: inhomogeneities of the scanner magnet, the shim field, an object‐based field map estimate, and a residual field. The former two are completely determined by spherical harmonic expansion coefficients directly available from the magnetic resonance (MR) scanner. The object‐based field map is forward simulated from air–tissue interfaces inside the field of view (FOV). The missing residual field originates from the object outside the FOV and is investigated by magnetic field simulations on a numerical whole body phantom. In vivo the spatially linear first‐order component of the residual field is estimated by measuring echo misalignments after demodulation of other field contributions resulting in a linear residual field. Gradient echo datasets of the cervical and the ankle region without and with shimming were acquired, where all four contributions were incorporated in the water–fat separation with two algorithms from the ISMRM water–fat toolbox and compared to water–fat separation with less incorporated field contributions.

Results

Incorporating all four field contributions as demodulation steps resulted in reduced temporal and spatial phase wraps leading to almost swap‐free water–fat separation results in all datasets.

Conclusion

Demodulating estimates of major field contributions reduces the phase evolution to be driven by only small differences in local tissue susceptibility, which supports the field smoothness assumption of existing water–fat separation techniques.

A data-oriented self-calibration and robust chemical-shift encoding by using clusterization (OSCAR): Theory, optimization and clinical validation in neuromuscular disorders

Multi-echo Chemical Shift-Encoded (CSE) methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water/fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant (such as for neuromuscular disorders) because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. Results established that the presented algorithm is able to identify at least 4 different partitions from MRI dataset under which to perform independent self-calibration routines and was found robust in NMD imaging studies (as evaluated on a cohort of 24 subjects) against latest CSE techniques with either calibrated or non-calibrated approaches. Particularly, the PDFF of the thigh was more reproducible for the quantitative estimation of pathological muscular fat infiltrations, which may be promising to evaluate disease progression in clinical practice.

[A two-point Dixon technique for water-fat separation using multiresolution and region-growing algorithm]

OBJECTIVE

An improved water-fat separation method based on region-growing was proposed for use in regions with low signal-noise ratio (SNR).

METHODS

Region-growing method was applied to 4 sub-images acquired by a down- sampling operation on the acquired phasor maps. The spatial smoothing constraint was exploited to calculate 4 error phasor maps to construct the final smooth error phasor map, which was used in two-point Dixon technique for water-fat separation.

RESULTS

The simulation experiment showed that the proposed method produced smaller errors, and for clinical images of the knees, abdomen and lower limbs, the proposed method achieved accurate water-fat separations.

CONCLUSION

The proposed method is more robust and reliable than the original global region-growing algorithm, and serves as a promising water-fat separation method for clinical applications.

Compartmentalized low-rank recovery for high-resolution lipid unsuppressed MRSI

PURPOSE

To introduce a novel algorithm for the recovery of high-resolution magnetic resonance spectroscopic imaging (MRSI) data with minimal lipid leakage artifacts, from dual-density spiral acquisition.

METHODS

The reconstruction of MRSI data from dual-density spiral data is formulated as a compartmental low-rank recovery problem. The MRSI dataset is modeled as the sum of metabolite and lipid signals, each of which is support limited to the brain and extracranial regions, respectively, in addition to being orthogonal to each other. The reconstruction problem is formulated as an optimization problem, which is solved using iterative reweighted nuclear norm minimization.

RESULTS

The comparisons of the scheme against dual-resolution reconstruction algorithm on numerical phantom and in vivo datasets demonstrate the ability of the scheme to provide higher spatial resolution and lower lipid leakage artifacts. The experiments demonstrate the ability of the scheme to recover the metabolite maps, from lipid unsuppressed datasets with echo time (TE) = 55 ms.

CONCLUSION

The proposed reconstruction method and data acquisition strategy provide an efficient way to achieve high-resolution metabolite maps without lipid suppression. This algorithm would be beneficial for fast metabolic mapping and extension to multislice acquisitions. Magn Reson Med 78:1267-1280, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Accelerating multi-echo water-fat MRI with a joint locally low-rank and spatial sparsity-promoting reconstruction

OBJECTIVES

Our aim was to demonstrate the benefits of using locally low-rank (LLR) regularization for the compressed sensing reconstruction of highly-accelerated quantitative water-fat MRI, and to validate fat fraction (FF) and [Formula: see text] relaxation against reference parallel imaging in the abdomen.

MATERIALS AND METHODS

Reconstructions using spatial sparsity regularization (SSR) were compared to reconstructions with LLR and the combination of both (LLR+SSR) for up to seven fold accelerated 3-D bipolar multi-echo GRE imaging. For ten volunteers, the agreement with the reference was assessed in FF and [Formula: see text] maps.

RESULTS

LLR regularization showed superior noise and artifact suppression compared to reconstructions using SSR. Remaining residual artifacts were further reduced in combination with SSR. Correlation with the reference was excellent for FF with [Formula: see text] = 0.99 (all methods) and good for [Formula: see text] with [Formula: see text] = [0.93, 0.96, 0.95] for SSR, LLR and LLR+SSR. The linear regression gave slope and bias (%) of (0.99, 0.50), (1.01, 0.19) and (1.01, 0.10), and the hepatic FF/[Formula: see text] standard deviation was 3.5%/12.1 s[Formula: see text], 1.9%/6.4 s[Formula: see text] and 1.8%/6.3 s[Formula: see text] for SSR, LLR and LLR+SSR, indicating the least bias and highest SNR for LLR+SSR.

CONCLUSION

A novel reconstruction using both spatial and spectral regularization allows obtaining accurate FF and [Formula: see text] maps for prospectively highly accelerated acquisitions.

COMPARTMENTALIZED LOW-RANK REGULARIZATION WITH ORTHOGONALITY CONSTRAINTS FOR HIGH-RESOLUTION MRSI

We introduce a novel compartmental low rank algorithm for high resolution MR spectroscopic imaging. We model the field inhomogeneity compensated MRSI dataset as the sum of a lipid dataset and a metabolite dataset using the spatial compartmental information obtained from water reference data. Both these datasets are modeled as low-rank subspaces, and are assumed to be orthogonal to each other. We formulate the recovery of the dataset from spiral measurements as a low-rank recovery problem. Experiments using numerical phantom and in-vivo data demonstrates the ability of the algorithm to provide improved spatial resolution and nuisance signal free spectra.

[A two-point Dixon technique for water-fat separation using multiresolution and region-growing algorithm]

OBJECTIVE

An improved water-fat separation method based on region-growing was proposed for use in regions with low signal-noise ratio (SNR).

METHODS

Region-growing method was applied to 4 sub-images acquired by a down- sampling operation on the acquired phasor maps. The spatial smoothing constraint was exploited to calculate 4 error phasor maps to construct the final smooth error phasor map, which was used in two-point Dixon technique for water-fat separation.

RESULTS

The simulation experiment showed that the proposed method produced smaller errors, and for clinical images of the knees, abdomen and lower limbs, the proposed method achieved accurate water-fat separations.

CONCLUSION

The proposed method is more robust and reliable than the original global region-growing algorithm, and serves as a promising water-fat separation method for clinical applications.

Method for B0 off-resonance mapping by non-iterative correction of phase-errors (B0-NICE)

PURPOSE

To develop and evaluate a multiecho phase-unwrapping-based B0 mapping method.

METHODS

The proposed method estimates a B0 map by Non-Iterative Correction of phase-Errors (B0-NICE). The B0-NICE method generates an initial B0 map from a "pseudo in-phase" data set by introducing a bias frequency shift to the multipeak fat model, followed by correcting the phase errors using both phase and magnitude information. The performance of the B0-NICE method was evaluated with all data cases from the 2012 ISMRM Challenge.

RESULTS

The B0 field estimates from B0-NICE were compared with those generated by GlObally Optimal Surface Estimation (GOOSE). In the presence of large B0 inhomogeneity, the B0-NICE method was able to generate more realistic B0 maps from multiecho data, compared with GOOSE. Accurate estimation of fat-fraction (FF) map was also achieved using the proposed algorithm.

CONCLUSION

The primary finding of the present study is that accurate FF and B0 maps are achievable if magnitude data is processed independently and used to correct phase errors existing in B0 maps generated by phase unwrapping. The B0-NICE software is freely available to the scientific community.