Accelerating MR measurement of liver steatosis using combined compressed sensing and parallel imaging: quantitative evaluation for clinical trials
Louis W Mann1, David M Higgins2, Carl N Peters1, Sophie Cassidy1, Ken K Hodson1, Anna Coombs1, Roy Taylor1, and Kieren Grant Hollingsworth1

1Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom, 2Philips Healthcare, Guildford, United Kingdom

Synopsis

We compared hepatic fat fractions quantified with accelerated magnetic resonance (MR) imaging reconstructed with combined compressed sensing and parallel imaging (CS-PI) with conventional acquisitions. Undersampled data at ratios of 2.6×, 2.9×, 3.8×, and 4.8× were prospectively acquired from eleven subjects with type 2 diabetes and a healthy control. Fat fraction maps were calculated using CS-PI and Bland-Altman analysis. Inter- and intrarater analysis was performed. The fat fractions from the accelerated acquisitions had tight 95% limits of with no bias. The fat fractions were acceptable up to a factor of 3.8×, shortening the breath-hold from 17.7s to 4.7s.

Introduction

Hepatic steatosis is an important global health problem, affecting 20-30% of adults in the Western Hemisphere and is central to the pathogenesis of type 2 diabetes1. Techniques such as the 3-point Dixon technique and IDEAL have proven to be sensitive non-invasive protocols for quantifying hepatic steatosis in interventional type 2 diabetes trials2. However, both chemical shift imaging and T2-corrected spectroscopic acquisitions typically require breath hold times of 18-29s, which are challenging for most patients, and impossible for some. Scan acceleration by undersampling data and reconstructing with combined compressed sensing and parallel imaging (CS-PI) may substantially reduce the required breath hold duration3. However, to date no study has quantitatively optimized the reconstruction parameters required for CS-PI, or evaluated quantitative fat fraction results from prospectively undersampled data. The aim of our study was to determine optimized reconstruction parameters and determine the limits of agreement of hepatic fat fraction quantified by accelerated MRI compared to conventional acquisitions. This will allow us to determine the maximum acceleration factor advisable and justify applying the technique prospectively in clinical research.

Methods

Eleven subjects with type 2 diabetes and 1 healthy subject were recruited. The MRI examination was performed on a 3T Philips Achieva using a 6 channel cardiac array, with the subject in a supine position. Fully-sampled and prospectively undersampled k-space data at acceleration factors of 2.6x, 2.9x, 3.8x, 4.8x (Figure 1) were acquired during consecutive expiration breath holds. A custom 3D spoiled gradient echo was used with variable density Poisson disk undersampling in the ky-kz encoding plane with a central fully-sampled region (20x20 encodes for the 2.6x acceleration, 20x14 encodes for higher accelerations). The matrix size was 221x160x16 yielding a voxel resolution of 1.90x1.90x7mm, and 5 unipolar gradient echoes per repetition time: repetition time (msec)/echo times (msec)/flip angle (deg)=7.0ms/0.91,2.16,3.42,4.67,5.92ms/3o, bandwidth 2144Hz/pixel. The fully sampled acquisition time was 17.7s and the duration of the single breath hold for the accelerated acquisitions was 6.8s, 6.1s, 4.7s and 3.7s respectively. Data using conventional PI (SENSE) at 2.6x acceleration were also collected.The 3D raw data were reconstructed with a compressed-sensing L1-ESPIRiT formulation with the Daubechies-4 wavelet used as the sparsifying transform4. For each dataset, the optimal weighting parameter was determined by comparing the reconstruction of each echo with the reconstruction of the fully-sampled data for several different values of the parameter using both the root mean square error (RMSE) and the structural similarity index (SSIM)5. Proton density fat fraction maps were calculated by a mixed fitting method6 described fully elsewhere7, and analysed by multi-slice region of interest analysis in ImageJ7. KGH and LWM independently marked ROIs to create an inter-rater analysis for fat fraction and LWM repeated all ROIs after a period of a week without the original ROIs to assess intra-rater variability. Comparisons were performed by Bland-Altman analysis. Image quality was qualitatively assessed7.

Results and Discussion

All subjects successfully completed the imaging protocol (Figure 2a-c). 10/11 T2D subjects showed steatosis (>5%). The number of slices with delineated ROIs per subject was 12 ± 2 (range 9-15). 143 ROIs were delineated in total and were available for statistical analysis. The optimum reconstruction parameter was found to be 0.3 for the 2.6x CS-PI (Figure 2d) and 0.5 for all other accelerations: these values were consistent across the patient cohort. The fat fractions for the fully sampled and undersampled reconstructions are given for each individual in Table 1. There was no significant bias of fat fraction for any of the accelerations and the 95% limits of agreement were tight and similar for the 2.6x, 2.9x and 3.8x CS-PI reconstructions (1.2%, 1.2% and 1.1%, Figure 2e and f), while they were higher for the 4.8x CS-PI reconstruction (1.5%). The inter-rater analysis of fat fraction showed negligible bias (0.2%) and a 95% limit of agreement of 0.8%, while the intra-rater analysis had a 95% limit of agreement of 0.7% with negligible bias (0.1%). Image quality was sufficient for ROI analysis for all scans except some 4.8x CS-PI images (Table 2). All conventional PI images were severely artefacted and unsuitable for analysis (Table 2).

Conclusion

Our study demonstrates that the measurement of liver fat fraction can be substantially accelerated by prospective undersampling and reconstruction by combined CS-PI, with good fidelity of the fat fractions compared to conventional full sampling. Optimal reconstruction parameters are fixed for a given protocol, avoiding the need for patient by patient calibration. As demand for measurement of liver fat fraction grows, the time efficiency of accurate quantification is likely to become of greater importance. The uncertainty in fat fraction caused by undersampling up to a factor of 3.8x was similar to that of the inter-rater uncertainty in the unmodified technique, with only a small degree of image distortion. The 95% limits of agreement for the 4.8x undersampling were substantially larger than either the repeatability or inter-observer limits of agreement. The image quality score at 4.8x showed marked degradation, with uncertainty over the delineation of a number of regions of interest using this scan alone (not shown7). From these data, we would recommend that 3.8x should be regarded as the practical limit of acceleration for this protocol and reconstruction method.

Acknowledgements

Medical Research Council New Investigator Research Grant (G110060)

References

[1] Taylor Diabetes Care 2013;36:1047, [2] Lim Diabetologia 2011;54:2506, [3] Sharma JMRI 2013;38:1267, [4] Uecker MRM 2014;71:990, [5] Wang IEEE TIP 2004;13:600, [6] Hernando MRM 2012;67:638,[7] Mann LW Radiology 2015:doi:10.1148/radiol.2015150320.

Figures

Figure 1: Sampling patterns in ky-kz space

Figure 2 : Fat fraction maps of a T2D subject showing (a) full, (b) 2.6x , (c) 3.8x acceleration. (d) Optimising the reconstruction parameter for 2.6x, (e) & (f) Bland-Altman plots comparing fat fractions for 2.6x and 3.8x against full sampling

Table 1 : Fat fractions for the 12 subjects from full sampling and CS-PI

Table 2 : Description and results of the qualitative grading scheme to assess image quality. Two assessors rated the quality of the water and fat fraction images for the fully sampled data, the CS-PI reconstructed data and data reconstructed with conventional parallel imaging (PI 2.6x). Data are given as mean (SD).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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