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Abstract #3236

Attention-Based Inverted Minimum Intensity Projection-Guided GAN for 7T-Like SWI Generation from 3T SWI

Wei Tang1, Rencheng Zheng1, Ying-Hua Chu2, Chengyan Wang3, and He Wang1
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, Shanghai, China, 3Human Phenome Institute, Fudan University, Shanghai, China

Synopsis

Keywords: Other AI/ML, Blood vessels, Susceptibility weighted imaging, ultra-high-field, generative adversarial network, minimum intensity projection, spatial cross-attention

Motivation: 7T SWI provides a higher signal-to-noise ratio, greater venous detail, and better diagnostic capability but its application is limited by physiologic considerations and high costs.

Goal(s): Our goal was to generate realistic 7T-like SWI from routine 3T SWI to improve the image quality and diagnostic capability.

Approach: We employed an attention-based generative adversarial network guided by inverted minimum intensity projection information.

Results: The proposed method achieved the best qualitative and quantitative results compared to existing competitive methods.

Impact: This study was an early exploration of artificial intelligence methods in the field of cross-field-strength SWI image generation. It provided insights for related research on enhancing the image quality and diagnostic capability of low-field MR images.

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Keywords