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Abstract #0009

SMRI: Next-generation MRI simulation platform for training data generation in the era of AI

Qinqin Yang1, Haitao Huang1, Zejun Wu1, Haotian Yong1, Haoye Zheng1, Shuhui Cai1, Zhong Chen1, and Congbo Cai1
1Department of Electronic Science, Xiamen University, Xiamen, China

Synopsis

Keywords: AI/ML Software, AI/ML Software, AI training data generation

Motivation: Current MRI simulation remains significantly limited by speed, restricting the use of synthetic training samples in deep learning-based MRI methods.

Goal(s): To develop an ultra-fast, versatile, cross-platform, and user-friendly MRI simulation platform for training data generation.

Approach: The SMRI platform was developed to support rapid MRI data generation by accelerating Bloch simulations. It enhances data diversity through deep learning-based MRI modality transformation and includes a user-friendly interface for ease of use.

Results: The SMRI platform delivers a 10- to 100-fold improvement in simulation speed over existing software, enabling diverse downstream tasks such as motion correction and quantitative MRI.

Impact: The ultra-fast, cross-platform, and user-friendly SMRI platform was developed for deep learning training sample generation, providing available and sufficient datasets for various deep learning-based MRI tasks within an acceptable time.

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