Abstract #2020

In vivo Cerebellum MRSI reconstruction by domain-transform manifold learning

Neha Koonjoo^1,2,3, Adam Berrington⁴, Bo Zhu^2,3,5, Uzay E Emir^6,7, and Matthew S Rosen^2,3,5

¹Department of Radiology, A.A Martinos Center for Biomedical Imaging / MGH, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Department of Physics, Harvard University, Cambridge, MA, United States, ⁴Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom, ⁵Radiology, A.A Martinos Center for Biomedical Imaging / MGH, Charlestown, MA, United States, ⁶School of Health Sciences, Purdue University, West Lafayette, IN, United States, ⁷Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States

The recent advances of machine learning in MRSI have mainly been focused on predicting metabolite concentrations and denoising the metabolite-only spectra. Here, we present a deep neural network based on the AUTOMAP formalism to reconstruct metabolic cycle FIDs into the spectral domain. A density matrix formalism was used to generate up/down fields of ¹H FIDs of 27 metabolites. B₀ inhomogeneity was also included in the simulations. Non water-suppressed up/down field FIDs were fed to the trained network and the proposed reconstruction strategy was validated on simulated FIDs at different noise levels and on an in vivo cerebellum dataset at 3T.

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