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Abstract #2637

Dep2SMS: Simultaneous Multi-Slice Reconstruction via Deep Learning with Auxiliary Depth Camera Guidance

Mengdie Song1, Xiaohan Hao2, Fulang Qi1, Zheyu Guo1, Yuan Li1, and Bensheng Qiu1
1University of Science and Technology of China, Hefei, China, 2Fuqing Medical Technology Co., LTD, Hefei, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Simultaneous Multi-Slice (SMS) reconstruction achieves slice separation by single-band calibration data or coil sensitivity maps through pre-scanning, resulting in SMS inefficiency.

Goal(s): Achieve real-time and robust contour acquisition through the depth camera.
Proposed a SMS reconstruction method via deep learning with auxiliary depth camera guidance.

Approach: We obtain the contour by placing the depth camera, locating phantom, and fixed markers within the MRI. We propose a Mamba-based network based on the auxiliary depth camera guidance to achieve faithful SMS reconstruction.

Results: The contour captured by the depth camera demonstrates effectiveness with less time-consuming and more robust, and the proposed Dep2SMS achieves outstanding reconstruction.

Impact: We novelty introduce the depth camera into the MRI system to capture contour to assist SMS reconstruction.
We utilize the Mamba-based framework with intra-patch convolution and linear-complexity long-range attention for SMS reconstruction to capture fine structural and global texture features.

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Keywords