Free-breathing 3D stack-of-radial MRI quantification of liver fat and R2* in adults with fatty liver disease

Quantification of the Proton Density Fat Fraction and Iron Content: A Comparative Study Between 3.0 T and 5.0 T MRI

BACKGROUND

Proton density fat fraction (PDFF) and R2* are noninvasive MRI biomarkers for quantifying fat and iron in abdominal organs. While 3.0 T MRI is widely used clinically, 5.0 T may offer improved accuracy and reliability. With the increasing availability of 5.0 T, assessing its feasibility and utility for quantifying PDFF and R2* in abdominal organs is needed.

PURPOSE

To determine the agreement of the PDFF and R2* in the liver, pancreas, kidney, and paraspinal muscle at 2 different field strengths.

STUDY TYPE

Prospective.

POPULATION

A total of 127 participants, including 60 healthy volunteers and 67 with metabolic dysfunction-associated steatotic liver disease (100 women and 27 men, 52 ± 9 years old).

FIELD STRENGTH/SEQUENCE

3.0 T and 5.0 T; Chemical shift-encoded multi-echo gradient echo sequence.

ASSESSMENT

PDFF and R2* values in the liver, pancreas, kidney, and paraspinal muscle were measured by three observers to evaluate the interobserver and interfield agreement using two 3.0 T scanners (scanner 1 and scanner 2) and one 5.0 T scanner.

STATISTICAL TESTS

Linear regression, Bland-Altman analyses, and intraclass correlation coefficients (ICCs). The p-value < 0.05 indicated statistically significant.

RESULTS

PDFF and R2* at 5.0 T were strongly correlated with those at two 3.0 T scanners for all organs (R2 = 0.905-0.998 for PDFF; 0.831-0.991 for R2*), indicating high interfield ICCs (0.892-0.993 for PDFF; 0.910-0.981 for R2*). Comparisons between 5.0 T and two 3.0 T scanners showed good interfield agreement for PDFF (mean bias, -0.57% to 0.03%) while a higher bias for R2* (mean bias, -16.53 to -2.64 s-1, 95% LoA, -34.28 to 1.21 s-1) at 5.0 T compared to the comparison between 3T scanners.

DATA CONCLUSION

5.0 T revealed high interfield agreement between the PDFF and R2* with 3.0 T, which could provide reliable quantification of fat and iron contents in abdominal organs.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Improved liver fat and R 2 * quantification at 0.55 T using locally low-rank denoising

PURPOSE

To improve liver proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ quantification at 0.55 T by systematically validating the acquisition parameter choices and investigating the performance of locally low-rank denoising methods.

METHODS

A Monte Carlo simulation was conducted to design a protocol for PDFF andR 2 * $$ {R}_2^{\ast } $$ mapping at 0.55 T. Using this proposed protocol, we investigated the performance of robust locally low-rank (RLLR) and random matrix theory (RMT) denoising. In a reference phantom, we assessed quantification accuracy (concordance correlation coefficient [ρ c $$ {\rho}_c $$ ] vs. reference values) and precision (using SD) across scan repetitions. We performed in vivo liver scans (11 subjects) and used regions of interest to compare means and SDs of PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements. Kruskal-Wallis and Wilcoxon signed-rank tests were performed (p < 0.05 considered significant).

RESULTS

In the phantom, RLLR and RMT denoising improved accuracy in PDFF andR 2 * $$ {R}_2^{\ast } $$ withρ c $$ {\rho}_c $$ >0.992 and improved precision with >67% decrease in SD across 50 scan repetitions versus conventional reconstruction (i.e., no denoising). For in vivo liver scans, the mean PDFF and meanR 2 * $$ {R}_2^{\ast } $$ were not significantly different between the three methods (conventional reconstruction; RLLR and RMT denoising). Without denoising, the SDs of PDFF andR 2 * $$ {R}_2^{\ast } $$ were 8.80% and 14.17 s-1. RLLR denoising significantly reduced the values to 1.79% and 5.31 s-1 (p < 0.001); RMT denoising significantly reduced the values to 2.00% and 4.81 s-1 (p < 0.001).

CONCLUSION

We validated an acquisition protocol for improved PDFF andR 2 * $$ {R}_2^{\ast } $$ quantification at 0.55 T. Both RLLR and RMT denoising improved the accuracy and precision of PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements.

Simultaneous 3D T 1 $$ {\mathrm{T}}_1 $$ , T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction mapping with respiratory-motion correction for comprehensive liver tissue characterization at 0.55 T

PURPOSE

To develop a framework for simultaneous three-dimensional (3D) mapping ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction in the liver at 0.55 T.

METHODS

The proposed sequence acquires four interleaved 3D volumes with a two-echo Dixon readout.T 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ are encoded into each volume via preparation modules, and dictionary matching allows simultaneous estimation ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , andM 0 $$ {M}_0 $$ for water and fat separately. 2D image navigators permit respiratory binning, and motion fields from nonrigid registration between bins are used in a nonrigid respiratory-motion-corrected reconstruction, enabling 100% scan efficiency from a free-breathing acquisition. The integrated nature of the framework ensures the resulting maps are always co-registered.

RESULTS

T 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction measurements in phantoms correlated strongly (adjustedr 2 > 0 . 98 $$ {r}^2>0.98 $$ ) with reference measurements. Mean liver tissue parameter values in 10 healthy volunteers were427 ± 22 $$ 427\pm 22 $$ ,47 . 7 ± 3 . 3   ms $$ 47.7\pm 3.3\;\mathrm{ms} $$ , and7 ± 2 % $$ 7\pm 2\% $$ forT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction, giving biases of71 $$ 71 $$ ,- 30 . 0   ms $$ -30.0\;\mathrm{ms} $$ , and- 5 $$ -5 $$ percentage points, respectively, when compared to conventional methods.

CONCLUSION

A novel sequence for comprehensive characterization of liver tissue at 0.55 T was developed. The sequence provides co-registered 3DT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction maps with full coverage of the liver, from a single nine-and-a-half-minute free-breathing scan. Further development is needed to achieve accurate proton-density fat fraction (PDFF) estimation in vivo.

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) andR 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, andR 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 andR 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 forR 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 forR 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 andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations.

Free-breathing MRI techniques for fat and R2* quantification in the liver

OBJECTIVE

To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities.

MATERIALS AND METHODS

This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification.

RESULTS

Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity.

DISCUSSION

As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.

Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction

PURPOSE

To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

METHODS

Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF),R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM in in vivo study.

RESULTS

Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$  = 0.31 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.05,R 2 $$ {R}^2 $$  = 0.999) and Cartesian without motion (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$  = 0.32× gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.04,R 2 $$ {R}^2 $$  = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$  = 0.00261 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.524,R 2 $$ {R}^2 $$  = 0.977) compared to motion-averaged reconstruction (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.0021 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.572,R 2 $$ {R}^2 $$ = 0.723) in ROI-based linear regression analysis betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM.

CONCLUSION

Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Single-spoke binning: Reducing motion artifacts in abdominal radial stack-of-stars imaging

PURPOSE

To increase the effectiveness of respiratory gating in radial stack-of-stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes.

METHODS

Free induction decay (FID) navigators were integrated into a three-dimensional gradient echo radial stack-of-stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single-spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke-angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free-breathing volunteers were reconstructed after motion-gating using either method. Image sharpness was quantitatively compared using image gradient entropies.

RESULTS

The proposed method resulted in sharper images of the motion phantom and free-breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion-gating led to a decrease of streaking artifacts in motion-gated four-dimensional reconstructions. To consistently estimate respiratory signals from the FID-navigator data, specific types of gradient spoiler waveforms were required.

CONCLUSION

SSB allowed high-resolution motion-corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion-gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion-gated radial stack-of-stars MRI in a broader domain of clinical applications.

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.

Pregnancies complicated by gestational diabetes and fetal growth restriction: an analysis of maternal and fetal body composition using magnetic resonance imaging

INTRODUCTION

Maternal body composition may influence fetal body composition.

OBJECTIVE

The objective of this pilot study was to investigate the relationship between maternal and fetal body composition.

METHODS

Three pregnant women cohorts were studied: healthy, gestational diabetes (GDM), and fetal growth restriction (FGR). Maternal body composition (visceral adipose tissue volume (VAT), subcutaneous adipose tissue volume (SAT), pancreatic and hepatic proton-density fat fraction (PDFF) and fetal body composition (abdominal SAT and hepatic PDFF) were measured using MRI between 30 to 36 weeks gestation.

RESULTS

Compared to healthy and FGR fetuses, GDM fetuses had greater hepatic PDFF (5.2 [4.2, 5.5]% vs. 3.2 [3, 3.3]% vs. 1.9 [1.4, 3.7]%, p = 0.004). Fetal hepatic PDFF was associated with maternal SAT (r = 0.47, p = 0.02), VAT (r = 0.62, p = 0.002), and pancreatic PDFF (r = 0.54, p = 0.008). When controlling for maternal SAT, GDM increased fetal hepatic PDFF by 0.9 ([0.51, 1.3], p = 0.001).

CONCLUSION

In this study, maternal SAT, VAT, and GDM status were positively associated with fetal hepatic PDFF.

Liver iron quantification in children and young adults: comparison of a volumetric multi-echo 3-D Dixon sequence with conventional 2-D T2* relaxometry

BACKGROUND

Magnetic resonance imaging (MRI)-based liver iron quantification is the standard of care to guide chelation therapy in children at risk of hemochromatosis. T2* relaxometry is the most widely used technique but requires third-party software for post-processing. Vendor-provided three-dimensional (3-D) multi-echo Dixon techniques are now available that allow inline/automated post-processing.

OBJECTIVE

The purpose of our study was to evaluate the diagnostic accuracy of a volumetric multi-echo Dixon technique using conventional T2* relaxometry as the reference standard in a pediatric and young adult population.

MATERIALS AND METHODS

In this retrospective study, we queried the radiology information system to identify all MRIs performed for liver iron quantification from July 2015 to January 2020. All patients had undergone T2* relaxometry on a 1.5-tesla (T) scanner for liver iron concentration (LIC) estimation. In addition, a 3-D multi-echo Dixon was performed using Siemens Healthineers LiverLab (Erlangen, Germany). Two readers independently estimated liver R2* and T2* on the multi-echo Dixon by drawing free-hand regions of interest on the scanner-generated R2* and T2* maps. Conventional T2*-relaxometry-based LIC was the reference standard. We estimated interobserver agreement by concordance correlation coefficient (CCC). We used Bland-Altman analysis and Pearson correlation coefficient (r) to compare LIC by the two methods.

RESULTS

Fifty-four MRIs on 38 patients (22 females) were available for analysis. Mean patient age was 11.8 years (standard deviation [SD] 5.3 years). Reference standard LIC ranged 1.1-21.1 (median 6.8) mg/g dry weight of liver. The concordance between readers for T2* estimation using 3-D multi-echo Dixon was substantial (CCC 0.99, confidence interval 0.99-1.00). Bland-Altman plot showed that all observations were clustered around the zero bias line if the LIC average was ≤8 mg/g, and r was very strong (reader 1 r=0.93, reader 2 r=0.92, both P-values <0.001). With increasing LIC, there was a pattern of poor agreement on the Bland-Altman plot, with observations crossing the lower limits of agreement, and r was very weak (reader 1 r=0.05, P-value 0.84; reader 2 r=0.17, P-value 0.44).

CONCLUSION

Vendor-based 3-D multi-echo Dixon allows for excellent interobserver correlation in liver T2* estimation. LIC estimated by this method has a very strong correlation with conventional T2* relaxometry if liver iron overload is mild-moderate (LIC ≤8 mg/g).