Fast frequency–sweep spectroscopic imaging with an ultra-low flip angle
Junyu Guo1, Zoltan Patay1, and Wilbrun E. Reddick1

1St Jude Children's Research Hospital, Memphis, TN, United States

Synopsis

We present a novel, simple and fast MR spectroscopic imaging technique and show its conceptual validation with simulations and demonstrate proof-of-principle with phantom and human studies. First, compared to the conventional spectroscopic imaging in the time-domain, our method acquires data in the frequency domain, allowing flexible non-uniform sampling to speed up the acquisition. Second, using ultra-small RF pulses offers intrinsic water and fat suppression, greatly simplifying the scanning procedures. Third, this new technique has hundreds of times lower energy deposition than conventional MRI scans. We believe our method could allow spectroscopic imaging to play a larger role in clinical applications.

Purpose

MR spectroscopic imaging (MRSI) is an important tool in clinical applications. As the spectrum is measured in either the frequency or the time domain, MRS is divided into the frequency-resolved and the time-resolved techniques (1, 2, 3). The early frequency-resolved techniques have been supplanted by the time-resolved technique with faster acquisition and higher sensitivity. We introduce a fast frequency-resolved MRSI technique, termed phase-cycled spectroscopic imaging (PCSI). PCSI uses an ultra-low flip-angle steady state to achieve high acquisition efficiency and faster frequency sweeping by changing cycled RF phase and using flexible non-uniform sampling, and greatly reduces RF energy deposition. With its intrinsic water and fat suppression, performing PCSI more closely resembles routine clinical scans by eliminating outer volume suppression steps. We demonstrate its feasibility using simulations, phantom and human studies.

Method

PCSI is the first pulsed frequency-resolved MRSI method. Using a series of ultra-small RF pulses to excite a target frequency, PCSI acquires an image at that frequency. By sweeping through discrete frequency points using RF phase-cycling, PCSI acquires a series of images at a range of frequencies and generates a spectrum in each voxel. PCSI makes frequency sweeping more flexible, allowing non-uniform frequency sampling of selected frequencies to speed up acquisition.

We performed simulations with multiple small flip angles to validate its feasibility and investigate the importance of the choice of flip angle. In simulations, a spectrum was created with four peaks: water, Cho, Cr, and NAA, with magnitudes of 10000, 0.6, 0.8, and 1.2, respectively. The profiles of the transverse magnetization were computed for different flip angles (0.24, 1, 3 degree) using TR=2.4 ms, T1=1300 ms, T2=250 ms.

All imaging studies were performed on a Siemens 3T scanner. PCSI acquisition uses a modified bSSFP sequence with the cycled RF phase changing for each measurement. A single axial slice was selected, and the advanced shimming mode was used. The system frequency was decreased by 200 Hz for phantom, 190 Hz for human. The protocol was as follows: ms; ms; flip angle for phantom, for human; acquisition matrix 32×32; resolution 6.25 × 6.25×15 mm3; 23 averages; 143 measurements with non-uniform sampling; total time 4:28 minutes.

For comparison, single-voxel spectra were acquired using a single voxel spectroscopy (SVS) sequence with TE =135 ms, voxel size = 20×20×20 mm3, and the total time 4:24 minutes. Chemical shift imaging (CSI) data were acquired from a patient with TE=135 ms; voxel size 10×10×15 mm3; acquisition matrix 10×10; and total time 8:07 minutes.

Results

Figure 1a shows the original generated spectrum. Fig. 1b shows the simulated spectrum with an optimal a=0.24°. All metabolic peaks are observed (Fig.2b), with heights proportional to the original values. Fig. 1c shows metabolic peaks are barely detectable in the simulated spectrum with α=1°. Those peaks are not appreciable in Fig. 1d, with α=3°.

Figure 2a shows PCSI signals of the phantom at the center (ROI size: 18.75×18.75×15 mm3). “Zooming in” inset demonstrates three metabolite peaks on the real signal, which was converted to the spectrum (Fig. 2b). For comparison, Fig. 2c shows a single-voxel spectrum using SVS. PCSI and SVS spectra (Fig. 2b, 2c) were similar and had consistent peak positions.

Figure 3a shows a PCSI spectrum from a volunteer (ROI size: 18.75×18.75×15 mm3), in which three metabolite peaks can be identified and fitted. The corresponding SVS spectrum is shown in Fig. 3b, with inset showing ROI’s location similar to that in Fig. 3a. Both spectra show consistent peak positions and relative peak heights after alignment. Figure 4 shows metabolic parametric maps were generated for two repeated measurements to demonstrate a good robustness of PCSI. Two sets of maps were interpolated and overlaid on a T2-weighted image for comparison.

Figure 5 shows CSI parametric maps and PCSI maps from a patient with a multifocal anaplastic astrocytoma. There were four lesion foci (1-4) shown in one T2w image, in which lesion 1 corresponds to a surgical cavity and lesions 2-4 represent tumor foci. Fig. 5 shows the importance of full coverage of the whole slice due to the heterogeneity of tumors.

Conclusion

We demonstrated PCSI, a new flexible way, to perform MRSI in the frequency domain. PCSI uses a new frequency-sweep technique and a non-uniform sampling scheme that speed up acquisition. PCSI also offers intrinsic water and fat suppression, reducing operator dependence and greatly simplifying scanning procedures. Further, PCSI substantially reduce SAR using ultra-small RF pulses. PCSI paves the way to faster, simpler, and broader clinical applications of MRSI.

Acknowledgements

The authors wish to acknowledge the valuable contributions of Angela Edwards, MR Technician, for her efforts in acquiring and processing of the CSI data. We acknowledge the efforts of Sharon Naron for her editing assistance. We also thanks Dr. Samuel Brady for letting us borrow the phantom.

References

1 Arnold, J. T., Dharmatti, S. S. & Packard, M. E. Chemical Effects on Nuclear Induction Signals from Organic Compounds. J Chem Phys 19, 507-507, doi:Doi 10.1063/1.1748264 (1951).

2 Meyer, L. H., Saika, A. & Gutowsky, H. S. Electron Distribution in Molecules .

3. The Proton Magnetic Spectra of Simple Organic Groups. J Am Chem Soc 75, 4567-4573, doi:Doi 10.1021/Ja01114a053 (1953). 3 Ernst, R. R. & Anderson, W. A. Application of Fourier Transform Spectroscopy to Magnetic Resonance. Rev Sci Instrum 37, 93-+, doi:Doi 10.1063/1.1719961 (1966).

Figures

Figure 1. Simulation of a spectrum using the PCSI method. (a) The assumed metabolite signals of 1. NAA, 2. Cr, 3. Cho, and 4. water in log scale. (b) The simulated real signal with α=0.24°; inset shows the enlarged plot. (c) with α=1°. (d) with α=3°.

Figure 2. Spectra from a phantom. (a) PCSI signal with an enlarged inset; (b) the spectrum calculated from (a); (c) the spectrum from a SVS sequence. ROI shown on inset. The number of 1,2,3, and 4 represent NAA, Cr, Cho, and Water, respectively. Ampl, amplitude; Real, real part; Imag, imaginary.

Figure 3. Comparison of Spectrum from human volunteer. (a) PCSI spectrum from nine voxel (18.75×18.75×15 mm3); (c) the spectrum from commercial single voxel spectroscopy sequence; the voxel size is 20 x 20 x 20 mm3 ; the inset shows the location of ROI for both plots. Real represents real part of signal.

Figure 4. The normalized parametric maps of three metabolites (NAA, Cr, and Cho) and the parametric maps of the Cho/NAA ratio from two repeated measurements using PCSI. M1 represents the measurement one; M2 represents the measurement two.

Figure 5. Parametric maps of NAA, Cr, and Cho and of the Cho/NAA ratios from a patient with astrocytoma. Maps from CSI sequence are in the upper row. PCSI parametric maps are in the lower row. A T2-weighted image shows the lesion foci (1-4). The dashed squares were manually drawn for comparison.



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