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Abstract #4252

Higher Fields, Deeper Insights: DL-based $$$B_{1}^{+}$$$ prediction at 10.5T

Kimon Hadjikiriakos1, Felix Frederik Zimmermann1, Felix Krüger1, Christoph Stefan Aigner1,2, Malgorzata Marjanska3, Simon Schmidt3, Young Woo Park3, Gregory J. Metzger3, and Sebastian Schmitter3,4
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 2Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany, 3Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, Minneapolis, MN, United States, 4Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

Synopsis

Keywords: High-Field MRI, High-Field MRI

Motivation: Deep learning (DL)-based channel-wise $$$B_{1}^{+}$$$-mapping at 10.5T can substantially reduce necessary $$$B_{1}^{+}$$$ calibration times, but the network's inner workings remain unclear.

Goal(s): Analyzing the impact of individual 80Rx head-coil elements to predict the 16Tx elements, using interpretability methods for DL-based $$$B_{1}^{+}$$$-mapping and gaining insights to the network’s decision-making.

Approach: Localizers and $$$B_{1}^{+}$$$-maps collected at 10.5T using a 16Tx/80Rx head-coil were supplied to a DL network to rapidly predict $$$B_{1}^{+}$$$-maps while evaluating its reliance on specific Rx-channels.

Results: Reducing Rx-channels from 80 to as few as 4 improves accuracy, suggesting redundancy; feature permutation maps further support redundancy in Rx-channels for DL-based $$$B_{1}^{+}$$$-mapping.

Impact: The study suggests that training a neural network to predict $$$B_{1}^{+}$$$-maps for a 16Tx/80Rx head coil at 10.5T might not require all coil elements, highlighting methods to identify redundant elements to optimize training speed and specific applications.

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Keywords