Accelerated free-breathing liver fat and R 2 * quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation.

PURPOSE: To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k-space undersampling. METHODS: A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. RESULTS: Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s-1 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s-1 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. CONCLUSION: This proposed method may allow accelerated free-breathing liver PDFF and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations.

Chemical Shift MRI Monitoring of Chemoembolization Delivery for Hepatocellular Carcinoma: Multicenter Feasibility of Initial Clinical Translation.

Purpose To demonstrate the feasibility of using chemical shift fat-water MRI methods to visualize and measure intrahepatic delivery of ethiodized oil to liver tumors following conventional transarterial chemoembolization (cTACE). Materials and Methods Twenty-eight participants (mean age, 66 years ± 8 [SD]; 22 men) with hepatocellular carcinoma (HCC) treated with cTACE were evaluated with follow-up chemical shift MRI in this Health Insurance Portability and Accountability Act-compliant prospective, institutional review board-approved study. Uptake of ethiodized oil was evaluated at 1-month follow-up chemical shift MRI. Measurements of tumor size (MRI and CT), attenuation and enhancement (CT), fat content percentage, and tumor:normal ratio (MRI) were compared by lesion for responders versus nonresponders, as assessed with modified Response Evaluation Criteria in Solid Tumors and European Association for the Study of the Liver (EASL) criteria. Adverse events and overall survival by the Kaplan-Meier method were secondary end points. Results Focal tumor ethiodized oil retention was 46% (12 of 26 tumors) at 24 hours and 47% (18 of 38 tumors) at 1 month after cTACE. Tumor volume at CT did not differ between EASL-defined responders and nonresponders (P = .06). Tumor ethiodized oil volume measured with chemical shift MRI was statistically significantly higher for EASL-defined nonresponders (P = .02). Doxorubicin dosing (P = .53), presence of focal fat (P = .83), and a combined end point of focal fat and low doxorubicin dosing (P = .97) did not stratify overall survival after cTACE. Conclusion Chemical shift MRI allowed for assessment of tumor delivery of ethiodized oil out to 1 month after cTACE in participants with HCC and demonstrated tumor ethiodized oil volume as a potential tool for stratification of tumor response by EASL criteria. Keywords: MRI, Chemical Shift Imaging, CT, Hepatic Chemoembolization, Ethiodized Oil Clinicaltrials.gov registration no.: NCT02173119 Supplemental material is available for this article. © RSNA, 2023.

Comparison of Inline R2* MRI versus FerriScan for liver iron quantification in patients on chelation therapy for iron overload: preliminary results.

OBJECTIVES: MRI quantification of liver iron concentration (LIC) using R2 or R2* relaxometry requires offline post-processing causing reporting delays, administrative overhead, and added costs. A prototype 3D multi-gradient-echo pulse sequence, with inline post-processing, allows immediate calculation of LIC from an R2* map (inline R2*-LIC) without offline processing. We compared inline R2*-LIC to FerriScan and offline R2* calibration methods. METHODS: Forty patients (25 women, 15 men; age 18-82 years), prospectively underwent FerriScan and the prototype sequence, which produces two R2* maps, with and without fat modeling, as well as an inline R2*-LIC map derived from the R2* map with fat modeling, with informed consent. For each map, the following contours were drawn: ROIs, whole-axial-liver contour, and an exact copy of contour utilized by FerriScan. LIC values from the FerriScan report and those calculated using an alternative R2 calibration were the reference standards. Results were compared using Pearson and interclass correlation coefficients (PCC, ICC), linear regression, Bland-Altman analysis, and estimation of area under the receiver operator curve (ROC-AUC). RESULTS: Inline R2*-LIC demonstrated good agreement with the reference standards. Compared to FerriScan, inline R2*-LIC with whole-axial-liver contour, ROIs, and FerriScan contour demonstrated PCC of 94.8%, 94.8%, and 92%; ICC 93%, 92.7%, and 90.2%; regression slopes 1.004, 0.974, and 1.031; mean bias 5.54%, 10.91%, and 0.36%; and ROC-AUC estimates 0.903, 0.906, and 0.890 respectively. Agreement was maintained when adjusted for sex, age, diagnosis, liver fat content, and fat-water swap. CONCLUSION: Inline R2*-LIC provides robust and comparable quantification of LIC compared to FerriScan, without the need for offline post-processing. KEY POINTS: • In patients being treated for iron overload with chelation therapy, liver iron concentration (LIC) is regularly assessed in order to monitor and adjust therapy. • Magnetic resonance imaging (MRI) is commonly used to quantify LIC. Several R2 and R2* methods are available, all of which require offline post-processing. • A novel R2* MRI method allows for immediate calculation of LIC and provides comparable quantification of LIC to the FerriScan and recently published alternative R2* methods.

Free-Breathing Volumetric Liver R2* and Proton Density Fat Fraction Quantification in Pediatric Patients Using Stack-of-Radial MRI With Self-Gating Motion Compensation.

BACKGROUND: Stack-of-radial multiecho gradient-echo MRI is promising for free-breathing liver R2* quantification and may benefit children. PURPOSE: To validate stack-of-radial MRI with self-gating motion compensation in phantoms, and to evaluate it in children. STUDY TYPE: Prospective. PHANTOMS: Four vials with different R2* driven by a motion stage. SUBJECTS: Sixteen pediatric patients with suspected nonalcoholic fatty liver disease or steatohepatitis (five females, 13 ± 4 years, body mass index 29.2 ± 8.6 kg/m2 ). FIELD STRENGTH/SEQUENCES: Stack-of-radial, and 2D and 3D Cartesian multiecho gradient-echo sequences at 3T. ASSESSMENT: Ungated and gated stack-of-radial proton density fat fraction (PDFF) and R2* maps were reconstructed without and with self-gating motion compensation. Stack-of-radial R2* measurements of phantoms without and with motion were validated against reference 2D Cartesian results of phantoms without motion. In subjects, free-breathing stack-of-radial and reference breath-hold 3D Cartesian were acquired. Subject inclusion for statistical analysis and region of interest placement were determined independently by two observers. STATISTICAL TESTS: Phantom results were fitted with a weighted linear model. Demographic differences between excluded and included subjects were tested by multivariate analysis of variance. PDFF and R2* measurements were compared using Bland-Altman analysis. Interobserver agreement was assessed by the intraclass correlation coefficient (ICC). RESULTS: Ungated stack-of-radial R2* inside moving phantom vials showed a significant positive bias of 64.3 s-1 (P < 0.00001), unlike gated results (P > 0.31). Subject inclusion decisions for statistical analysis from two observers were consistent. No significant differences were found between four excluded and 12 included subjects (P = 0.14). Compared to breath-hold Cartesian, ungated and gated free-breathing stack-of-radial exhibited mean R2* differences of 18.5 s-1 and 3.6 s-1 . Mean PDFF differences were 1.1% and 1.0% for ungated and gated measurements, respectively. Interobserver agreement was excellent (ICC for PDFF = 0.99, ICC for R2* = 0.90; P < 0.0003). DATA CONCLUSION: Stack-of-radial MRI with self-gating motion compensation seems to allow free-breathing liver R2* and PDFF quantification in children. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Effect of respiratory motion on free-breathing 3D stack-of-radial liver R2∗ relaxometry and improved quantification accuracy using self-gating.

PURPOSE: To develop an accurate free-breathing 3D liver R2∗ mapping approach and to evaluate it in vivo. METHODS: A free-breathing multi-echo stack-of-radial sequence was applied in 5 normal subjects and 6 patients at 3 Tesla. Respiratory motion compensation was implemented using the inherent self-gating signal. A breath-hold Cartesian acquisition was the reference standard. Proton density fat fraction and R2∗ were measured and compared between radial and Cartesian methods using Bland-Altman plots. The normal subject results were fitted to a linear mixed model (P < .05 considered significant). RESULTS: Free-breathing stack-of-radial without self-gating exhibited signal attenuation in echo images and artifactually elevated apparent R2∗ values. In the Bland-Altman plots of normal subjects, compared to breath-hold Cartesian, free-breathing stack-of-radial acquisitions of 22, 30, 36, and 44 slices, had mean R2∗ differences of 27.4, 19.4, 10.9, and 14.7 s-1 with 800 radial views, and they had 18.4, 11.9, 9.7, and 27.7 s-1 with 404 views, which were reduced to 0.4, 0.9, -0.2, and -0.7 s-1 and to -1.7, -1.9, -2.1, and 0.5 s-1 with self-gating, respectively. No substantial proton density fat fraction differences were found. The linear mixed model showed free-breathing radial R2∗ results without self-gating were significantly biased by 17.2 s-1 averagely (P = .002), which was eliminated with self-gating (P = .930). Proton density fat fraction results were not different (P > .234). For patients, Bland-Altman plots exhibited mean R2∗ differences of 14.4 and 0.1 s-1 for free-breathing stack-of-radial without self-gating and with self-gating, respectively, but no substantial proton density fat fraction differences. CONCLUSION: The proposed self-gating method corrects the respiratory motion bias and enables accurate free-breathing stack-of-radial quantification of liver R2∗ .

Glucose dysregulation in patients with iron overload: is there a relationship with quantitative pancreas and liver iron and fat content measured by MRI?

OBJECTIVES: The aim was to investigate the relationship between pancreatic and hepatic iron and fat to glucose metabolism in patients with iron overload and address conflicting results in literature as regards the relationship between pancreas iron and glucose dysregulation. METHODS: We retrospectively evaluated pancreatic and hepatic R2*, fat fraction (FF), liver iron concentration (LIC), and glucose metabolism in 105 patients with iron overload obtained with a multi-echo gradient echo R2* technique and assessed the correlation between pancreatic R2* and FF to glucose dysregulation. RESULTS: There were no significant differences in pancreatic R2*, liver R2*, and FF in patients with iron overload and glucose dysregulation compared to those with normoglycemia (p = 0.435, p = 0.674, and p = 0.976), whereas pancreatic FF was significantly higher, 23.5% vs 16.7% respectively (p = 0.011). Pancreatic FF and R2* demonstrated an area under the curve of 0.666 and 0.571 for discriminating glucose dysregulation. Pancreatic FF of 26.2% yielded specificity and sensitivity of 80% and 45% for prediction of glucose dysregulation. Pancreatic R2* weakly correlated with pancreatic FF, r = 0.388 (p < 0.001), and liver R2*, r = 0.201 (p = 0.033), and showed no correlation with hepatic FF r = -0.013 (p = 0.892) or LIC categories (p = 0.493). CONCLUSION: Pancreatic FF but not pancreatic R2* was associated with glucose dysregulation in patients with iron overload. Prior studies reporting correlation of pancreatic R2* to glucose dysregulation likely relate from inadequate MRI technique or analysis employed, which unlike our study did not perform simultaneous measurements of fat and iron essential to avoid their confounding effects during quantitative analysis. KEY POINTS: • Pancreatic fat fraction, unlike iron, is associated with glucose dysregulation in iron overload. • Simultaneous measurement of pancreatic iron and fat content with MRI is essential to avoid confounding effects of one another during quantitative analysis. • Pancreatic fat fraction could be utilized to predict glucose dysregulation in iron overload states.

Improved accuracy of apparent diffusion coefficient quantification using a fully automatic noise bias compensation method: Preliminary evaluation in prostate diffusion weighted imaging.

Noise in diffusion magnetic resonance imaging can introduce bias in apparent diffusion coefficient (ADC) quantification. Previous studies proposed methods that are site-specific techniques as research tools with limited availability and typically require manual intervention, not completely ready to use in the clinical environment. The purpose of this study was to develop a fully automatic computational method to correct noise bias in ADC quantification and perform a preliminary evaluation in the clinical prostate diffusion weighted imaging (DWI). Using a pseudo replica approach for the noise map calculation as well as a direct mapping and a stepwise Chebychev polynomial modelling approach for the ADC fitting, a fully automatic noise-bias-compensated ADC calculation method was proposed and implemented both on the scanner and offline. The proposed method was validated in a computer simulation and a standardized diffusion phantom with ground-truth values. Two in vivo studies were performed to evaluate the proposed method in the clinical environment. The first in vivo study performed acquisitions using a clinically routine prostate DWI protocol on 29 subjects to evaluate the consistency between simulated and empirical results. In the second in vivo study, prostate ADC values of 14 subjects were compared between data acquired with external coils only and reconstructed with the proposed method vs. acquired with external combined with endorectal coils and reconstructed with the conventional method. In statistical analyses, p < 0.05 was regarded as significantly different. In the computer simulation, the proposed method showed smaller error percentage than the other methods and was significantly different (p < 2.2 × 10-16). With low signal-to-noise ratio (SNR), the conventional method underestimated ADC values compared to the ground truth values of the diffusion phantom, while the results of the proposed method were more consistent with the ground truth values. Statistical analyses showed no significant differences between measured and simulated results in the first in vivo study (p = 0.5618). Data from the second in vivo study showed that agreement between ADC measured with external coils only and combined coils was improved for the proposed method (mean bias: 0.04 × 10-3 mm2/s, 95% confidence interval (CI) = [-0.01, 0.09] × 10-3 mm2/s, p = 0.187), compared to the conventional method (mean bias: -0.12 × 10-3 mm2/s, 95% CI = [-0.17, -0.06] × 10-3 mm2/s, p < 0.0001). The proposed method compensates noise bias in low-SNR diffusion-weighted acquisitions and results show improved ADC quantification accuracy in the prostate. This method may be suitable for both clinical imaging and research utilizing ADC quantification.

Full utilization of conjugate symmetry: combining virtual conjugate coil reconstruction with partial Fourier imaging for g-factor reduction in accelerated MRI.

PURPOSE: In this study we propose a method to combine the parallel virtual conjugate coil (VCC) reconstruction with partial Fourier (PF) acquisition to improve reconstruction conditioning and reduce noise amplification in accelerated MRI where PF is used. METHODS: Accelerated measurements are reconstructed in k-space by GRAPPA, with a VCC reconstruction kernel trained and applied in the central, symmetrically sampled part of k-space, while standard reconstruction is performed on the asymmetrically sampled periphery. The two reconstructed regions are merged to form a full reconstructed dataset, followed by PF reconstruction. The method is tested in vivo using T1-weighted spin-echo and T2*-weighted gradient-echo echo planar imaging (EPI) sequences, using both in-plane and simultaneous multislice (SMS) acceleration, at 1.5T and 3T field strengths. Noise amplification is estimated with theoretical calculations and pseudo-multiple-replica computations, for different PF factors, using zero-filling, homodyne, and projection onto convex sets (POCS) PF reconstruction. RESULTS: Depending on the PF algorithm and the inherent benefit of VCC reconstruction without PF, approximately 35% to 80%, 15% to 60%, and 5% to 30% of that intrinsic SNR gain can be retained for PF factors 7/8, 6/8, and 5/8, respectively, by including the VCC signals in the reconstruction. Compared with VCC-reconstructed acquisitions of higher acceleration, without PF, but having the same net acceleration, the combined method can provide a higher SNR if the inherent benefit of VCC is low or moderate. CONCLUSION: The proposed technique enables the partial application of VCC reconstruction to measurements with PF using either in-plane or SMS acceleration, and therefore can reduce the noise amplification of such acquisitions.

Prospective Evaluation of an R2* Method for Assessing Liver Iron Concentration (LIC) Against FerriScan: Derivation of the Calibration Curve and Characterization of the Nature and Source of Uncertainty in the Relationship.

BACKGROUND: FerriScan is the method-of-choice for noninvasive liver iron concentration (LIC) quantification. However, it has a number of drawbacks including cost and expediency. PURPOSE/HYPOTHESIS: To characterize an R2*-based MRI technique that may potentially be used as an alternative to FerriScan. This was accomplished through the derivation of a calibration curve that characterized the relationship between FerriScan-derived LIC and R2*. The nature and source of uncertainty in this curve were investigated. It was hypothesized that the source of uncertainty is heterogeneity of LIC across the liver. STUDY TYPE: Prospective. SUBJECTS: In all, 125 patients (69 women, 56 men) undergoing chelation treatment for iron overload prospectively underwent FerriScan and R2* MRI during the same exam. FIELD STRENGTH/SEQUENCE: Pulse sequences included 2D multislice spin-echo pulse for FerriScan, and a prototype 3D 6-echo gradient echo acquisition for R2* mapping at 1.5T. ASSESSMENT: A linear calibration curve was derived from the relationship between FerriScan-derived LIC estimates and R2* through least-squares fitting. STATISTICAL TESTS: The nature of the uncertainty in the curve was characterized through tests of normality and homoscedasticity. The source of uncertainty was tested by comparing the magnitude of LIC variation over the FerriScan ROI to the observed uncertainty in the R2*-derived LIC estimates. RESULTS: A linear relationship between logarithmically transformed FerriScan-derived LIC and R2* (log{FerriScan-derived LIC} = 1.029 log{R2*} - 3.822) was confirmed. Uncertainty was random, with a behaviour that was normal and homoscedastic. The source of uncertainty was confirmed as iron heterogeneity across the liver. The nontransformed calibration curve was: FerriScan-derived LIC = 0.0266⋅R2*, with a constant coefficient-of-variation of 0.32. DATA CONCLUSION: FerriScan and R2* techniques were found to provide equivalent quantification of LIC in this study. Any difference in accuracy or precision was at a level lower than the uncertainty caused by variation in LIC over the liver. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1467-1474.

Accuracy of Automated Liver Contouring, Fat Fraction, and R2* Measurement on Gradient Multiecho Magnetic Resonance Images.

OBJECTIVE: This study aimed to evaluate the performance of an automated workflow of volumetric liver proton density fat fraction (PDFFvol) and R2* quantification with automated inline liver volume (LV) segmentation. METHODS: Dual-echo and multiecho Dixon magnetic resonance images were evaluated in 74 consecutive patients (group A, PDFF < 10%; B, PDFF ≥ 10%; C, R2* ≥ 100 s; D, post-hemihepatectomy). The values of PDFFvol and R2*vol measurements across the LV were generated on multiecho images in an automated fashion based on inline liver segmentation on dual-echo images. Similar measurements were performed manually. RESULTS: Using the inline algorithm, the mis-segmented LV was highest in group D (80%). There were no significant differences between automated and manual measurements of PDFFvol. Automated R2*vol was significantly lower than manual R2*vol in group A (P = 0.004). CONCLUSIONS: Inline LV segmentation performed well in patients without and with hepatic steatosis. In cases with iron overload and post-hemihepatectomy, extrahepatic areas were erroneously included to a greater extent, with a tendency toward overestimation of PDFFvol.

Controlling the object phase for g-factor reduction in phase-Constrained parallel MRI using spatially selective RF pulses.

PURPOSE: Parallel imaging generally entails a reduction in the signal-to-noise ratio of the final image. Phase-constrained methods aim to improve reconstruction quality by using symmetry properties of k-space. Noise amplification in phase-constrained reconstruction depends heavily on the object background phase. The purpose of this work is to present a new approach of using tailored radiofrequency pulses to optimize the object phase distribution in order to maximize the benefit of phase-constrained reconstruction, and to minimize the noise amplification. METHODS: Intrinsic object phase and coil sensitivity profiles are measured in a prescan. Optimal phase distribution is computed to maximize signal-to-noise ratio in the given setup. Tailored radiofrequency pulses are designed to introduce the optimal phase map in the following accelerated acquisitions, subsequently reconstructed by phase-constrained methods. The potential of the method is demonstrated in vivo with in-plane accelerated (8x) and simultaneous multislice (3x) acquisitions. RESULTS: Mean g-factors are reduced by up to a factor of 2 compared with conventional techniques when an appropriate phase-constrained reconstruction is applied to phase-optimized acquisitions, enhancing the signal-to-noise ratio of the final images and the visibility of small details. CONCLUSIONS: Combining phase-constrained reconstruction and phase optimization by tailored radiofrequency pulses can provide notable improvement in the signal-to-noise ratio and reconstruction quality of accelerated MRI. Magn Reson Med 79:2113-2125, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Feasibility of a three-step magnetic resonance imaging approach for the assessment of hepatic steatosis in an asymptomatic study population.

OBJECTIVES: To determine the feasibility of a multi-step magnetic resonance imaging (MRI) approach for comprehensive assessment of hepatic steatosis defined as liver fat content of ≥5 % in an asymptomatic population. METHODS: The study was approved by the institutional review board and written informed consent of all participants was obtained. Participants of a population-based study cohort underwent a three-step 3-T MRI-based assessment of liver fat. A dual-echo Dixon sequence was performed to identify subjects with hepatic steatosis, followed by a multi-echo Dixon sequence with proton density fat fraction estimation. Finally, single-voxel T2-corrected multi-echo spectroscopy was performed. RESULTS: A total of 215 participants completed the MRI protocol (56.3 % male, average age 57.2 ± 9.4 years). The prevalence of hepatic steatosis was 55 %. Mean liver proton density fat fraction was 9.2 ± 8.5 % by multi-echo Dixon and 9.3 ± 8.6 % by multi-echo spectroscopy (p = 0.51). Dual-echo Dixon overestimated liver fat fraction by 1.4 ± 2.0 % (p < 0.0001). All measurements showed excellent correlations (r ≥ 0.9, p < 0.001). Dual-echo Dixon was highly sensitive for the detection of hepatic steatosis (sensitivity 0.97, NPV 0.96) with good specificity and PPV (0.75 and 0.81, respectively). CONCLUSIONS: A multi-step MRI approach may enable rapid and accurate identification of subjects with hepatic steatosis in an asymptomatic population. KEY POINTS: • Dual-echo Dixon can rapidly and reliably exclude hepatic steatosis without complex post-processing. • Multi-echo Dixon and multi-echo spectroscopy yield similar results regarding hepatic fat quantification. • Each sequence can be performed in one breath-hold. • These sequences can be implemented in routine abdominal MRI protocols. • Thus hepatic fat can be evaluated without relevant increase in scan time.

Quantification of hepatic steatosis with a multistep adaptive fitting MRI approach: prospective validation against MR spectroscopy.

OBJECTIVE. The purpose of this study is to prospectively compare hybrid and complex chemical shift-based MRI fat quantification methods against MR spectroscopy (MRS) for the measurement of hepatic steatosis. SUBJECTS AND METHODS. Forty-two subjects (18 men and 24 women; mean ± SD age, 52.8 ± 14 years) were prospectively enrolled and imaged at 3 T with a chemical shift-based MRI sequence and a single-voxel MRS sequence, each in one breath-hold. Proton density fat fraction and rate constant (R2*) using both single- and dual-R2* hybrid fitting methods, as well as proton density fat fraction and R2* maps using a complex fitting method, were generated. A single radiologist colocalized volumes of interest on the proton density fat fraction and R2* maps according to the spectroscopy measurement voxel. Agreement among the three MRI methods and the MRS proton density fat fraction values was assessed using linear regression, intraclass correlation coefficient (ICC), and Bland-Altman analysis. RESULTS. Correlation between the MRI and MRS measures of proton density fat fraction was excellent. Linear regression coefficients ranged from 0.98 to 1.01, and intercepts ranged from -1.12% to 0.49%. Agreement measured by ICC was also excellent (0.99 for all three methods). Bland-Altman analysis showed excellent agreement, with mean differences of -1.0% to 0.6% (SD, 1.3-1.6%). CONCLUSION. The described MRI-based liver proton density fat fraction measures are clinically feasible and accurate. The validation of proton density fat fraction quantification methods is an important step toward wide availability and acceptance of the MRI-based measurement of proton density fat fraction as an accurate and generalizable biomarker.

Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging.

PURPOSE: The purpose of this study was to develop a multi-step adaptive fitting approach for liver proton density fat fraction (PDFF) and R(2)* quantification, and to perform an initial validation on a broadly available hardware platform. THEORY AND METHODS: The proposed method uses a multi-echo three-dimensional gradient echo acquisition, with initial guesses for the fat and water signal fractions based on a Dixon decomposition of two selected echoes. Based on magnitude signal equations with a multi-peak fat spectral model, a multi-step nonlinear fitting procedure is then performed to adaptively update the fat and water signal fractions and R(2)* values. The proposed method was validated using numeric phantoms as ground truth, followed by preliminary clinical validation of PDFF calculations against spectroscopy in 30 patients. RESULTS: The results of the proposed method agreed well with the ground truth of numerical phantoms, and were relatively insensitive to changes in field strength, field homogeneity, monopolar/bipolar readout, signal to noise ratio, and echo time selections. The in vivo patient study showed excellent consistency between the PDFF values measured with the proposed approach compared with spectroscopy. CONCLUSION: This multi-step adaptive fitting approach performed well in both simulated and initial clinical evaluation, and shows potential in the quantification of hepatic steatosis.

T2-weighted 3D fMRI using S2-SSFP at 7 tesla.

In this study, the sensitivity of the S(2)-steady-state free precession (SSFP) signal for functional MRI at 7 T was investigated. In order to achieve the necessary temporal resolution, a three-dimensional acquisition scheme with acceleration along two spatial axes was employed. Activation maps based on S(2)-steady-state free precession data showed similar spatial localization of activation and sensitivity as spin-echo echo-planar imaging (SE-EPI), but data can be acquired with substantially lower power deposition. The functional sensitivity estimated by the average z-values was not significantly different for SE-EPI compared to the S(2)-signal but was slightly lower for the S(2)-signal (6.74 +/- 0.32 for the TR = 15 ms protocol and 7.51 +/- 0.78 for the TR = 27 ms protocol) compared to SE-EPI (7.49 +/- 1.44 and 8.05 +/- 1.67) using the same activated voxels, respectively. The relative signal changes in these voxels upon activation were slightly lower for SE-EPI (2.37% +/- 0.18%) compared to the TR = 15 ms S(2)-SSFP protocol (2.75% +/- 0.53%) and significantly lower than the TR = 27 ms protocol (5.38% +/- 1.28%), in line with simulations results. The large relative signal change for the long TR SSFP protocol can be explained by contributions from multiple coherence pathways and the low intrinsic intensity of the S(2) signal. In conclusion, whole-brain T(2)-weighted functional MRI with negligible image distortion at 7 T is feasible using the S(2)-SSFP sequence and partially parallel imaging.

General formulation for quantitative G-factor calculation in GRAPPA reconstructions.

In this work a theoretical description for practical quantitative estimation of the noise enhancement in generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstructions, equivalent to the geometry (g)-factor in sensitivity encoding for fast MRI (SENSE) reconstructions, is described. The GRAPPA g-factor is derived directly from the GRAPPA reconstruction weights. The procedure presented here also allows the calculation of quantitative g-factor maps for both the uncombined and combined accelerated GRAPPA images. This enables, for example, a fast comparison between the performances of various GRAPPA reconstruction kernels or SENSE reconstructions. The applicability of this approach is validated on phantom studies and demonstrated using in vivo images for 1D and 2D parallel imaging.

Fast method for 1D non-cartesian parallel imaging using GRAPPA.

MRI with non-Cartesian sampling schemes can offer inherent advantages. Radial acquisitions are known to be very robust, even in the case of vast undersampling. This is also true for 1D non-Cartesian MRI, in which the center of k-space is oversampled or at least sampled at the Nyquist rate. There are two main reasons for the more relaxed foldover artifact behavior: First, due to the oversampling of the center, high-energy foldover artifacts originating from the center of k-space are avoided. Second, due to the non-equidistant sampling of k-space, the corresponding field of view (FOV) is no longer well defined. As a result, foldover artifacts are blurred over a broad range and appear less severe. The more relaxed foldover artifact behavior and the densely sampled central k-space make trajectories of this type an ideal complement to autocalibrated parallel MRI (pMRI) techniques, such as generalized autocalibrating partially parallel acquisitions (GRAPPA). Although pMRI can benefit from non-Cartesian trajectories, this combination has not yet entered routine clinical use. One of the main reasons for this is the need for long reconstruction times due to the complex calculations necessary for non-Cartesian pMRI. In this work it is shown that one can significantly reduce the complexity of the calculations by exploiting a few specific properties of k-space-based pMRI.

Direct parallel image reconstructions for spiral trajectories using GRAPPA.

The use of spiral trajectories is an efficient way to cover a desired k-space partition in magnetic resonance imaging (MRI). Compared to conventional Cartesian k-space sampling, it allows faster acquisitions and results in a slight reduction of the high gradient demand in fast dynamic scans, such as in functional MRI (fMRI). However, spiral images are more susceptible to off-resonance effects that cause blurring artifacts and distortions of the point-spread function (PSF), and thereby degrade the image quality. Since off-resonance effects scale with the readout duration, the respective artifacts can be reduced by shortening the readout trajectory. Multishot experiments represent one approach to reduce these artifacts in spiral imaging, but result in longer scan times and potentially increased flow and motion artifacts. Parallel imaging methods are another promising approach to improve image quality through an increase in the acquisition speed. However, non-Cartesian parallel image reconstructions are known to be computationally time-consuming, which is prohibitive for clinical applications. In this study a new and fast approach for parallel image reconstructions for spiral imaging based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) methodology is presented. With this approach the computational burden is reduced such that it becomes comparable to that needed in accelerated Cartesian procedures. The respective spiral images with two- to eightfold acceleration clearly benefit from the advantages of parallel imaging, such as enabling parallel MRI single-shot spiral imaging with the off-resonance behavior of multishot acquisitions.