Using a Low Rank plus Sparse Reconstruction Approach to Accelerate 3D Dynamic bSSFP Hyperpolarized Carbon-13 MR Imaging
Eugene Milshteyn1, Cornelius von Morze1, Galen D Reed2, Hong Shang1, Peter J Shin1, Peder EZ Larson1, and Daniel B Vigneron1

1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2HeartVista, Menlo Park, CA, United States

Synopsis

Hyperpolarized 13C MR can provide unique imaging assessments of metabolism and perfusion in various disease conditions in vivo. High spatiotemporal resolution is needed to best characterize these processes. This project used a low rank plus sparse reconstruction with the bSSFP acquisition to achieve high isotropic resolution dynamic 3D imaging with multiple hyperpolarized substrates.

Purpose

The development of new hyperpolarization (HP) 13C methods has allowed unprecedented characterization of metabolism and perfusion changes in various diseases processes using MRI1,2. To characterize these temporal processes, dynamic hyperpolarized 13C imaging is desirable, although a trade-off between high temporal resolution and high spatial resolution is typically required3. The bSSFP sequence offers the highest SNR per unit time and can be readily combined with compressed sensing to achieve high spatial and temporal resolution dynamic imaging4. A low rank plus sparse (L+S) reconstruction approach for undersampled data has been previously demonstrated in 1H perfusion imaging5, whereby the low rank component describes the temporally and spatially correlated background, and the sparse component describes the remaining dynamic signal changes, both of which are typically seen in hyperpolarized 13C imaging. In this project, we utilized the L+S reconstruction to achieve 3D high resolution (1.5-2 mm isotropic; 0.003-0.008 cm3) 13C images of [13C,15N2]urea and [2-13C]pyruvate in vivo with 5 s temporal resolution.

Methods

Simulated reconstructions using the L+S method (total variation along the time dimension for the sparse component) were performed by retrospectively undersampling a previously acquired 3D high resolution dataset that was transformed into a 4D dataset by adding signal dynamics based on previously published in vivo hyperpolarized 13C studies2. A different undersampling variable-density pattern was used for each time point and structural similarity index (SSIM) was used to assess the reconstruction. The in vivo experiments were performed with a custom dynamic 3D bSSFP pulse sequence in normal Sprague-Dawley rats. For each of the 13 time points for [13C,15N2]urea imaging, ~192/480 phase encodes (80 x 40 x 12 matrix size) were acquired based on a simulated 60% undersampling pattern chosen for initial studies. A 1.6 ms sinc pulse (no slice select gradients) with a flip angle θ = 30°, after a θ = 15° preparatory pulse, was used with a TR/TE of 7.5/3.75 ms, leading to a scan time of 1.44 s per time point. A delay of 3.56 s was used between time points, leading to a temporal resolution of 5 s, and a total scan time of 65 s. [2-13C]pyruvate imaging was acquired with similar pulse and TR/TE parameters, but ~108/270 phase encodes (60 x 30 x 9 matrix size) were acquired, leading to a scan time 810 ms per time point, and a 4.19 s delay to achieve 5 s temporal resolution. Both compounds were imaged with a 12 x 6 x 1.8 cm3 field of view, leading to 1.5 mm isotropic resolution for [13C,15N2]urea and 2 mm isotropic for [2-13C]pyruvate. The 13C images of [2-13C]pyruvate and [13C,15N2]urea were each acquired independently starting at the beginning of injection (t = 0 s). The experiments were conducted on a 3T GE MR scanner. DNP experiments used a HyperSense polarizer, and 3 mL of 110 mM [13C,15N2]urea and 80 mM [2-13C]pyruvate was injected via tail vein catheters in two different animals.

Results and Discussion

The L+S reconstruction of retrospectively simulated data was in good agreement with the fully sampled data for various acceleration factors, with the resulting SSIMs being above 0.9. Figure 1 shows the L+S reconstructed and fully sampled images, as well as the SSIM map for a representative dynamic slice, for the 60% undersampling pattern used in in vivo studies. The resulting 3D dynamic images showed distribution of urea (Figure 2) and pyruvate (Figure 3) within the kidneys, aorta, and heart, with the resolution being high enough to visualize uptake in the renal cortex, medulla, and pelvis. The figures show the full 3D view of each compound at 20 s after start of injection, as well as the time course for a representative slice of both urea and pyruvate, with the SNR being high enough to detect both compounds in multiple time frames. The signal dynamics for each compound from the left kidney are also shown. While the RF pulse was not spectrally selective, any signal from metabolites formed from [2-13C]pyruvate can be considered relatively negligible compared to the signal level of [2-13C]pyruvate6.

Conclusion

The application of the L+S reconstruction to the bSSFP sequence allowed for 3D dynamic high resolution (0.003-0.008 cm3) imaging of HP 13C substrates. This approach also provided high temporal resolution and a large temporal window that showed in-flow and out-flow of the substrates in different anatomical structures. The 1.5-2 mm isotropic resolution achieved here is similar to that used for 1H imaging and application of this approach can be extended to future studies of cancer and other disease models, as well as ultimately for clinical imaging.

Acknowledgements

The authors would like to thank Dr. Jeremy Gordon, Dr. Peng Cao, Mark Van Criekinge, and Lucas Carvajal for all their help and funding from the NIH (P41EB013598).

References

1. Kurhanewicz, J. et al. Analysis of cancer metabolism by imaging hyperpolarized nuclei: prospects for translation to clinical research. Neoplasia 13, 81–97 (2011).

2. Von Morze, C. et al. Investigating tumor perfusion and metabolism using multiple hyperpolarized 13C compounds: HP001, pyruvate and urea. Magn. Reson. Imaging 30, 305–311 (2012).

3. Hu, S. et al. Compressed Sensing for Resolution Enhancement of Hyperpolarized 13C Flyback 3D-MRSI. J. Magn. Reson. 192, 258–264 (2008).

4. Scheffler, K. & Lehnhardt, S. Principles and applications of balanced SSFP techniques. Eur. Radiol. 13, 2409–2418 (2003).

5. Otazo, R., Candès, E. & Sodickson, D. K. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn. Reson. Med. 73, 1125–1136 (2015).

6. Schroeder, M. et al. Real-time assessment of Krebs cycle metabolism using hyperpolarized 13C magnetic resonance spectroscopy. FASEB J. 23, 2529–2538 (2009).

Figures

Figure 1: The reconstructed undersampled images and fully sampled images of a representative slice from an L+S reconstructed retrospective simulation of a 4D data set are shown, along with the SSIM map for each timepoint. A 60% undersampling pattern was used. The overall SSIM for the reconstruction was 0.95.

Figure 2: A full 3D view of the 5th timepoint from a [13C,15N2]urea image set can be seen on the top left, along with the time course for the representative slice outlined in black at the bottom. The dynamic signal curve for the left renal cortex can be seen on the top right.

Figure 3: A full 3D view of the 5th timepoint from a [2-13C]pyruvate image set can be seen on the top left, along with the time course for the representative slice outlined in black at the bottom. The dynamic signal curve for the left renal cortex can be seen on the top right.



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