Abstract #3968

Metabolite Map Estimation from Undersampled Spectroscopic Imaging Data Using N-Compartment Model

Itthi Chatnuntawech¹, Berkin Bilgic¹, Borjan Gagoski², Trina Kok¹, Audrey Peiwen Fan¹, Elfar Adalsteinsson¹,³

¹EECS, Massachusetts Institute of Technology, Cambridge, MA, United States; ²Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States; ³Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States

Specific physiological abnormalities could be detected by irregular change of metabolite concentration in specific brain regions. The combination between fully sampled spectroscopic imaging data and segmented structural image has been used to estimate metabolite value at each voxel. This abstract presents an N-compartment-model method with polynomial masks to obtain metabolite maps from undersampled spectroscopic imaging data. With the assumption that metabolite value within the same tissue type is slowly varying, the information of tissue boundaries is obtained from segmented structural image. Then, a regularized reconstruction with priors is formulated to reconstruct the metabolite maps. By acquiring only a subset of k-space samples, the acquisition process is sped up, while high reconstruction quality is retained via prior knowledge of tissue boundary and structure of data.