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Abstract #3809

Gradient Waveform Pre-emphasis using Deep Neural Network

Guangyu Dan1, Zheng Zhong1, Qi Liu1, and Jian Xu1
1United Imaging Healthcare North America, Houston, TX, United States

Synopsis

Keywords: System Imperfections, System Imperfections: Measurement & Correction, pre-emphasis, deep learning

Motivation: Gradient waveform fidelity is critical in MRI for accurate signal encoding, but system imperfections often lead to waveform deviations that compromise image quality.

Goal(s): To develop a deep learning-based method for accurate gradient waveform pre-emphasis that compensates for these system imperfections.

Approach: A bi-directional LSTM network iteratively learns the system’s gradient response, optimizing input waveforms for high-fidelity output. This was validated using 3D Cones sequences on phantoms and human brains.

Results: The output gradient waveform achieved near-perfect alignment with the ideal waveform. Phantom and human brain images demonstrated enhanced spatial uniformity and reduced artifacts, showcasing improved MRI image quality.

Impact: This method enhances MRI image quality by reducing artifacts and improving spatial accuracy through gradient waveform pre-emphasis using deep learning.

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Keywords