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Abstract #2607

Pseudo real-time multi-parametric quantitative 3D MRI using Parallel non-Cartesian stDLNN for interventional procedures

Zhijun Wang1, Sifan Wu1, Wenlong Feng1, Zekang Ding1, Huajun She1, and Yiping P. Du1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Real-Time, Multi‐Contrast, Quantitative Imaging

Motivation: Pseudo real-time multi-parametric MRI can improve the localization of targets for intraoperative procedures, particularly in the surgery of deep brain stimulation (DBS).

Goal(s): To develop an online MRI technique capable of generating quantitative R2*, SWI, and QSM maps with reduced acquisition times, suitable for intraoperative procedures.

Approach: We employed a blipped multi-gradient-echo sequence with a stack-of-stars trajectory and modified stDLNN algorithm along with the Gadgetron platform to achieve pseudo real-time imaging.

Results: Three-dimensional quantitative R2*, SWI, and QSM mapping can be acquired in 11.2s, with online reconstruction and quantification completed in 4s, generating quantitative maps with good quality for intraoperative applications.

Impact: This work demonstrates the potential for pseudo real-time multi-parametric 3D MRI during surgeries, supporting procedures like DBS by providing quantitative maps with a 11.2s acquisition and 4s computation, which could enhance intraoperative decision-making and improve patient outcomes.

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