Estimation and Removal of spurious echo Artifacts in single-voxel MRS using Sensitivity Encoding

Abstract

Purpose: In localized MR spectroscopy, spurious echo artifacts commonly occur when unsuppressed signal outside the volume-of-interest is excited and refocused. In the spectral domain, these signals often overlap with metabolite resonances and hinder accurate quantification. Since the artifacts originate from regions separate from the target MRS voxel, this work proposes that sensitivity encoding based on receive coil sensitivity profiles may be used to separate these signal contributions. Methods: Numerical simulations were performed to explore the effect of sensitivity encoded separation for unknown artifact regions. An imaging-based approach was developed to identify regions that may contribute to spurious echo artifacts, and tested for sensitivity-based unfolding of signal contribution on 6 datasets from 3 brain regions. Spectral data reconstructed using the proposed method ('ERASE') were compared to standard coil combination. Results: The method was able to fully separate metabolite and artifact signals if regions were known a priori. Mismatch between estimated and actual artifact locations reduced the efficiency of artifact removal. Water suppression imaging (WSI) was able to identify unsuppressed signal remote from the MRS voxel in all cases, and ERASE reconstruction (of up to 8 distinct locations) led to improvements in spectral quality and reduced fitting errors for the major metabolites compared to standard reconstruction, without significant degradation of spectral SNR. Conclusion: The ERASE reconstruction tool was demonstrated to reduce spurious echo artifacts in single voxel MRS. ERASE may be incorporated into standard MRS workflows to improve spectral quality when scanner hardware limitations or other factors result in out-of-voxel signal contamination.

Impact of gradient scheme and shimming on out-of-voxel echo artifacts in edited MRS

“…Most OOV echoes have a refocusing point that lies after, but during, the intended echo – these signals can be relatively reduced by apodization 34 (e.g., exponential line broadening). Estimation and removal of artifacts using sensitivity encoding (ERASE) 35 can remove OOV echoes if their spatial origin is known, using phased-array receive-coil sensitivity profiles to separate OOV echo signals from metabolite signals arising from within the voxel 35 . Given that OOV signals can often be identified by eye in both the time- and frequency-domain, there is reason to expect that deep learning techniques will be able to identify and remove spurious echoes.…”