Fast Chemical Exchange Saturation Transfer (CEST) Imaging with Variably-accelerated Sensitivity Encoding (vSENSE)
Yi Zhang1, Hye-Young Heo1, Dong-Hoon Lee1, Paul Bottomley1, and Jinyuan Zhou1,2

1Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, 2F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

CEST imaging has numerous applications, but its widespread clinical use is hampered by relatively long acquisition times. Here, a novel variably-accelerated sensitivity encoding (vSENSE) method is proposed that provides faster CEST acquisitions than conventional SENSE. The vSENSE approach undersamples k-space variably for images acquired at different saturation frequencies to maximize acquisition speed. vSENSE was validated in a phantom and in 8 patients with brain tumors studied at 3T. The vSENSE method provided a 4-fold acceleration, compared to conventional SENSE which permitted only a 2-fold acceleration, with both compared to a full k-space reconstruction.

Purpose

The routine clinical application of CEST MRI is limited by its relatively long scan times, since CEST typically requires images to be repeat acquisitions with different saturation frequencies. Despite recent advances in fast CEST acquisition methods (1-4), the only widespread option available on MRI scanners for accelerating CEST acquisitions, is currently parallel imaging, such as with SENSE (5). However, the acceleration factor offered by SENSE for CEST MRI rarely goes beyond two (6-8). Here, a novel acquisition and reconstruction method, variably-accelerated sensitivity encoding (vSENSE), is proposed for faster acquisition than conventional SENSE. The vSENSE method undersamples k-space variably for images acquired at different saturation frequencies to generate accurate sensitivity maps and maximize acquisition speed.

Methods

The vSENSE method was validated on a doped water phantom and 8 consented brain tumor patients studied on a 3T Philips MRI scanner with a 32-channel head coil. CEST contrast was achieved using 0.8s saturation duration and 2μT saturation power (saturation frequencies, 14 to -8ppm stepped at 0.5ppm, in addition to an unsaturated reference–S0; two-dimensional turbo-spin-echo, TSE, for image readout; FOV=212×185×4.4mm; acquisition resolution=2.2×2.2×4.4mm; acquisition matrix size=96×84x1; total duration=2.7min) (6). A 2D TSE “WASSR” (9) sequence was acquired separately for B0 inhomogeneity correction. The raw k-space CEST data and the vendor’s preset sensitivity reference scan, were saved for offline in-house reconstruction. Conventional T2-weighted, FLAIR, and T1-weighted anatomical MRI were also acquired from each patient.

First, standard reconstruction was performed with the following steps: (A1) Fourier transforming (FT) the k-space data from each channel; and (A2) combining images from all channels using the “maximized SNR” strategy (10).

Second, SENSE reconstruction was performed by: (B1) computing sensitivity maps from the vendor’s preset reference scan; (B2) undersampling by increasing the phase-encoding gradient step size by 4-fold in every dynamic CEST dataset, resulting in folded images; and (B3) reconstructing unfolded images using the SENSE method (5).

Third, vSENSE reconstruction was performed by: (C1) selecting one of the CEST dynamic images to retain the full k-space data (acceleration, R=1). Here the S0 dynamic was chosen. (C2) Reconstruct images from this fully acquired data set using FT, and fit the sensitivity maps with a weighted polynomial. (C3) Increase the phase-encoding gradient step size to variably undersample the other CEST images, with R=2 for the ±3.5ppm images and R=4 for the rest. (C4) Apply the estimated sensitivity maps to the variably undersampled datasets to reconstruct images.

Results and Discussion

Fig. 1 shows the conventional SENSE image (Fig. 1b, R=4) has a much greater difference (Fig. 1d) from the standard FT image (Fig. 1a, R=1), compared to the difference (Fig. 1e) with the vSENSE method (Fig. 1c, R=4). The vSENSE method self-estimates the sensitivity maps from the fully-acquired S0 dynamic, resulting in more accurate estimation of sensitivity maps than conventional SENSE with a separate reference sensitivity scan. This is because the vendor’s preset SENSE reference scan acquired at the beginning of each exam virtually always uses a different acquisition sequence, resolution and orientation vs. subsequent imaging scans.

Fig. 2 from a brain tumor patient, shows conventional SENSE (R=4) yielded substantially corrupted Amide Proton Transfer (11) weighted (APTw, part e) and raw (part g) images, compared to the standard FT results (blue arrows indicate tumor region, and red arrows indicate SENSE-reconstruction artifacts). In contrast, the vSENSE (R=4) method generated APTw (part f) and raw (part h) images, as well as z-spectra (part i) highly consistent with FT ones. Note that the sampling pattern can be adapted for other CEST imaging applications, such as creatine (12) or glucose(13, 14) imaging.

Conclusion

The vSENSE method variably undersamples saturated images at different frequencies, resulting in an overall acceleration factor of essentially four. In conjunction with the fully-sampled S0 dynamic, the vSENSE method guarantees accurate sensitivity maps and sufficient SNR for accurate APTw imaging. As implemented here, vSENSE doubled the speed and provided better results than conventional SENSE, which may prove key for advancing CEST to the clinic.

Acknowledgements

Funding Support: NIH Grant R01 EB007829, CA166171, EB009731, NS083435

References

1. Xu X, Lee JS, Jerschow A. Ultrafast scanning of exchangeable sites by NMR spectroscopy. Angewandte Chemie International Edition. 2013;52(32):8281-4.

2. Döpfert J, Zaiss M, Witte C, Schröder L. Ultrafast CEST imaging. J Magn Reson. 2014;243:47-53.

3. Varma G, Lenkinski R, Vinogradov E. Keyhole chemical exchange saturation transfer. Magn Reson Med. 2012;68(4):1228-33.

4. Zhang Y, Heo HY, Jiang S, Lee DH, Bottomley PA, Zhou J. Highly accelerated chemical exchange saturation transfer (CEST) measurements with linear algebraic modeling. Magn Reson Med. 2015. doi: 10.1002/mrm.25873.

5. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med. 1999;42(5):952-62.

6. Zhu H, Jones CK, van Zijl P, Barker PB, Zhou J. Fast 3D chemical exchange saturation transfer (CEST) imaging of the human brain. Magn Reson Med. 2010;64(3):638-44.

7. Zhou J, Zhu H, Lim M, Blair L, Quinones-Hinojosa A, Messina SA, Eberhart CG, Pomper MG, Laterra J, Barker PB. Three-dimensional amide proton transfer MR imaging of gliomas: Initial experience and comparison with gadolinium enhancement. J Magn Reson Imaging. 2013;38(5):1119-28.

8. Zhao X, Wen Z, Zhang G, Huang F, Lu S, Wang X, Hu S, Chen M, Zhou J. Three-dimensional turbo-spin-echo amide proton transfer MR imaging at 3-Tesla and its application to high-grade human brain tumors. Mol Imaging Biol. 2013;15(1):114-22.

9. Kim M, Gillen J, Landman BA, Zhou J, van Zijl P. Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments. Magn Reson Med. 2009;61(6):1441-50.

10. Roemer P, Edelstein W, Hayes C, Souza S, Mueller O. The NMR phased array. Magn Reson Med. 1990;16(2):192-225.

11. Zhou J, Payen J-F, Wilson DA, Traystman RJ, van Zijl PC. Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med. 2003;9(8):1085-90.

12. Haris M, Singh A, Cai K, Kogan F, McGarvey J, DeBrosse C, Zsido GA, Witschey WR, Koomalsingh K, Pilla JJ. A technique for in vivo mapping of myocardial creatine kinase metabolism. Nat Med. 2014;20(2):209-14.

13. Chan KW, McMahon MT, Kato Y, Liu G, Bulte JW, Bhujwalla ZM, Artemov D, van Zijl P. Natural D-glucose as a biodegradable MRI contrast agent for detecting cancer. Magn Reson Med. 2012;68(6):1764-73.

14. Walker-Samuel S, Ramasawmy R, Torrealdea F, Rega M, Rajkumar V, Johnson SP, Richardson S, Gonçalves M, Parkes HG, Årstad E. In vivo imaging of glucose uptake and metabolism in tumors. Nat Med. 2013;19(8):1067-72

Figures

Figure 1: Comparison of saturated images at 3.5ppm from a doped water phantom, reconstructed from standard FT (full k-space, R=1) (a), SENSE (R=4) (b) and vSENSE (R=4) (c). The difference of SENSE and vSENSE from FT are shown in (d) and (e), respectively.

Figure 2: Anatomical (a-c) and APT-weighted images (d-f) from a brain tumor patient. APT-weighted images from SENSE (e) and vSENSE (f) were calculated from raw images shown in (g) and (h), respectively. FT (blue) and vSENSE (red) z-spectra from the selected region (red circle in h) were compared in (i).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
1522