Meeting Banner
Abstract #1080

MultiNet CAIPIRINHA: accelerated 1H MRSI with 1-step neural network reconstruction based on augmented MRSI training data

Kimberly L Chan1 and Anke Henning2,3
1Advanced Imaging Research Center, The University of Texas Southwestern, Dallas, TX, United States, 2The University of Texas Southwestern, Dallas, TX, United States, 3Max Planck Institute for Biological Cybernetics, Tübingen, Germany


We have shown that MultiNet, a neural-network-based image reconstruction, can reconstruct variable-density k-space undersampling schemes to decrease MRSI acquisition times. This used a 4-step method where points are predicted by 4 successively-applied neural-networks off both acquired and previously predicted k-space points. Herein, a 1-step method where points are only predicted off acquired k-space points to reduce reconstruction error was explored. This method was trained using a new augmented MRSI training set and compared to the 4-step reconstruction of new CAIPIRINHA-based schemes and the original schemes. The new 1-step reconstruction method was found to increase SNR and improve metabolic maps.

This abstract and the presentation materials are available to members only; a login is required.

Join Here