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High resolution 3D EPSI Spectroscopic Imaging compared with Spectral Localization by Imaging (SLIM)
Sean Edmund Ellis1,2, Peter Adany1, Phil Lee1,2,3, and In-Young Choi1,2,3,4

1Hoglund Brain Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States, 2Department of Bioengineering, University of Kansas, Lawrence, KS, United States, 3Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, United States, 4Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States

Synopsis

Quantitative measurements of metabolites in important structures in the human brain are challenging to acquire using conventional spectroscopic imaging methods. In this study, we compared regional metabolite concentrations obtained from high resolution 3D EPSI MRSI data using two different methods: spatial averaging inside regions of interest using the MIDAS software, and Spectral Localization by Imaging (SLIM)-based MRS. Deep brain structures were studied and compared using the two methods. The quantitative outcomes of 3D EPSI and SLIM were comparable and SLIM could provide metabolite concentrations using shorter scan times by reducing the required number of voxel acquisitions.

Introduction

High resolution 3D EPSI offers to overcome resolution barriers in spectroscopic imaging of the brain. However, the acquisition time of EPSI is very long, in the range of 20 minutes per scan. The theory of Spectral Localization by Imaging (SLIM) provides a basis for reconstruction of the spectra of anatomical compartments in under-sampled conditions, by taking advantage of high resolution prior anatomical segmentation information from MRI. SLIM could thus utilize fewer k-space acquisitions to achieve performance comparable with region-of-interest (ROI) averaging of 3D EPSI. The purpose of this study was to evaluate MRSI reconstructions acquired by 3D EPSI, using both full k-space ROI averaging and SLIM with reduced k-space sizes.

Background and Theory

Echo planar spectroscopic imaging (EPSI) can be used to acquire full 3D k-space data with coverage of the whole brain. However, the scan time of EPSI is very long, due to the large number of phase encoding acquisitions. Spectral Localization by Imaging (SLIM) provides a framework to incorporate high resolution prior information from MRI, enabling the reconstruction of chemical shift spectra of distinct anatomical compartments [1]. The SLIM problem is expressed by a matrix equation s = G*c, where s is a vector of phase encoded signals, G is a geometry matrix derived from the compartment information and phase encoding vectors, and c is a vector of the compartment coefficients. Solving the system of SLIM requires a number of phase encodes larger than the number of compartments, and generally a greater number of phase encodes improves the resilience of SLIM reconstruction in the presence of compartmental inhomogeneities. Considering the large k-space coverage offered by EPSI, we aimed to investigate the performance of SLIM reconstruction with different k-space sizes including localized reconstructions from significantly reduced k-space.

Methods

Four healthy control subjects were consented according to institutional review board approved protocols. Scans were performed on a 3 T scanner (Skyra, Siemens) with a 16 channel head receiver coil. 1H MRSI was acquired using a 3D EPSI sequence [2] (TE/TR=3980/200000 ms). Parcellation of gray and white matter was obtained from MPRAGE images using FreeSurfer [3] to provide regional anatomical compartments for MIDAS and SLIM. EPSI reconstruction was performed by Metabolite Imaging and Data Analysis Software (MIDAS). Nine deep gray and white matter structures were selected and processed through MIDAS ROI averaging and SLIM reconstructions. Metabolite estimation and spectral fitting was processed by LCModel [4].

Results and Discussion

The acquisition time for 3D EPSI is around 20 minutes, making clinical application challenging. The SLIM theory promises to allow signal localization of compartments using much fewer k-space acquisitions, by taking advantage of prior information from high resolution MRI to reconstruct the spectra of anatomical compartments. The results of this study showed that localization of anatomical compartment spectra using SLIM could be retrieved from identically shaped structures used in MIDAS, with greatly reduced number of phase encoding acquisitions. This shows promise of using advanced reconstruction by SLIM and related techniques to improve the performance of 3D MRSI applications for quantitative spectroscopy of the human brain.

Acknowledgements

The Hoglund Brain Imaging Center is supported by the NIH (S10RR029577) and the Hoglund Family Foundation.

References

1. Hu X, et al., SLIM: spectral localization by imaging. Magn Reson Med, 8:314-322, 1988

2. Maudsley A.A., et al., Comprehensive processing, display, and analysis for in vivo MR spectroscopic imaging. NMR Biomed. 19:492-503, 2006

3. Dale, A.M., et al., Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179-194, 1999

4. Provencher S.W., et al., Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 30:672–679, 1993

Figures

Figure 1. Example parcellation results from MRI. Anatomical masks were obtained for the whole brain, and used for anatomical object-based reconstructions using the MIDAS software and SLIM.

Figure 2. Example results using the MIDAS software for ROI spectral averaging analysis.

Figure 3. Results of SLIM reconstruction with multiple sizes of k-space used in the reconstruction. Results showed excellent agreement with full sized 3D MRSI processing, demonstrating the potential for accurate spectral localization with significantly reduced k-space size.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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