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Abstract #2638

Multi-Contrast MR Imaging Acceleration: Transform 2D Low-Resolution into High-Resolution 3D Images with Auxiliary Contrast in 3D Acquisition

Zheng Zhang1, Zechen Zhou2, Lei Xiang2, Xinyu Song3, Xiaoer Wei3, and Yuehua Li3
1Subtle Medical Inc, Guildford, United Kingdom, 2Subtle Medical Inc, Menlo Park, CA, United States, 3Shanghai Sixth People Hospital, Shanghai, China

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, super-resolution

Motivation: High-resolution 3D MR imaging provides superior diagnostic quality compared to 2D scans, but its prolonged acquisition time limits its clinical adoption for all contrast weightings.

Goal(s): Develop a method that enhances the resolution of 2D FLAIR and T2 MR scans to similar levels as in 3D acquisitions.

Approach: Leveraging auxiliary high-resolution 3D images, we developed a model with a pre-trained masked autoencoder and residual convolutional decoder to enhance resolution for 2D MR scans.

Results: The developed method produces 3D images from 2D scans with similar quality comparable to 3D acquisitions, which can be 5- to 12-fold faster, enabling efficient multi-contrast MRI acceleration.

Impact: The developed approach allows multi-contrast brain low-resolution 2D scans with an auxiliary high-resolution 3D reference scan to produce multi-contrast high-resolution 3D images. Clinical evaluation on synthesized 3D brain images and extension to other applications may be worth further investigation.

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Keywords