0223
Motion-resolved B1+ prediction using deep learning for real-time pTx pulse-design.
Alix Plumley1, Luke Watkins1, Kevin Murphy1, and Emre Kopanoglu1
1Cardiff University Brain Research Imaging Centre, Cardiff, United Kingdom
We present a deep learning approach to estimate motion-resolved parallel-transmit B1+ distributions using a system of conditional generative adversarial networks. The estimated maps can be used for real-time pulse re-design, eliminating motion-related flip-angle error.
Fig.4(A) Voxelwise magnitude correlations between B1initial (initial position) and B1gt (ground-truth displaced) [left] and B1predicted (network predicted displaced) and B1gt [right]. Small (top), medium (middle) and large (bottom) displacements are shown. Phase data not shown. (B) Magnitude (in a.u.) and phase (radians) B1-maps for the largest displacement. Left, middle and right: B1initial, B1gt and B1predicted, respectively. B1predicted quality did not depend heavily on displacement magnitude and remained high despite 5 network cascades for evaluation at R-20 A10mm.
Fig.3(A) Motion-induced flip-angle error for pTx pulses designed using B1initial (conventional method, pink) & B1predicted (proposed method, blue) at all evaluated positions & slices. Y-axes show nRMSE (% target flip-angle). nRMSE of all pulsespredicted is at/below the shaded region. Asterisks show number of network cascades required for evaluation. (B) Example profiles. Left: evaluation for pulseinitial without motion. Middle & right: evaluations at B1gt (displaced position) using pulseinitial & pulsepredicted, respectively. Colorscale shows flip-angle (target=70°).