Macromolecule mapping of the brain using ultrashort‐TE acquisition and reference‐based metabolite removal. Issue 5 (3rd September 2017) - Lam, Fan
Abstract : Purpose: To develop a practical method for mapping macromolecule distribution in the brain using ultrashort‐TE MRSI data. Methods: An FID‐based chemical shift imaging acquisition without metabolite‐nulling pulses was used to acquire ultrashort‐TE MRSI data that capture the macromolecule signals with high signal‐to‐noise‐ratio (SNR) efficiency. To remove the metabolite signals from the ultrashort‐TE data, single voxel spectroscopy data were obtained to determine a set of high‐quality metabolite reference spectra. These spectra were then incorporated into a generalized series (GS) model to represent general metabolite spatiospectral distributions. A time‐segmented algorithm was developed to back‐extrapolate the GS model‐based metabolite distribution from truncated FIDs and remove it from the MRSI data. Numerical simulations and in vivo experiments have been performed to evaluate the proposed method. Results: Simulation results demonstrate accurate metabolite signal extrapolation by the proposed method given a high‐quality reference. For in vivo experiments, the proposed method is able to produce spatiospectral distributions of macromolecules in the brain with high SNR from data acquired in about 10 minutes. We further demonstrate that the high‐dimensional macromolecule spatiospectral distribution resides in a low‐dimensional subspace. This finding provides a new opportunity to use subspace models for quantification and accelerated macromolecule mapping. Robustness of the proposed method is also demonstrated using multiple data sets from the same and different subjects. Conclusion: The proposed method is able to obtain macromolecule distributions in the brain from ultrashort‐TE acquisitions. It can also be used for acquiring training data to determine a low‐dimensional subspace to represent the macromolecule signals for subspace‐based MRSI. Magn Reson Med 79:2460–2469, 2018. © 2017 International Society for Magnetic Resonance in Medicine.