DeepPTx: parallel transmission MRI using deep learning

Applications

• Generate full parallel transmit-like high-fidelity images using single transmit systems

Technology Overview

Parallel transmit (pTx) is a powerful technique used to overcome the challenges of ultra-high field MRI while achieving the corresponding increase in signal-to-noise ratio (SNR). However, conventional pTx requires careful pulse design and tedious calibration thereby hindering clinical adoption. Researchers at the University of Minnesota have developed a novel deep-learning framework, deepPTx, which trains a deep neural network to directly predict pTx-like images from images obtained with single-channel transmit (sTx) systems. This method substantially enhances image quality relative to sTx approaches and does not require either pTx hardware or specialized pTx expertise in pulse design.

Phase of Development

Desired Partnerships

• License
• Sponsored research
• Co-development

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Researchers

• Xiaoping Wu, PhD Assistant Professor, Department of Radiology
• Kamil Ugurbil, PhD Professor, Department of Radiology