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Abstract #2652

Uncertainty-aware Quantitative MRI Reconstruuction using conidtional Wasserstein GAN

Haozhong Sun1, Zhongsen Li1, Runyu Yang1, Ziming Xu1, Haokun Li1, Xiaoqi Lin1, and Huijun Chen1
1Tsinghua University, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Quantitative Imaging

Motivation: Recent advancements in deep learning hold great promise for expediting MRI scans, particularly in the time-consuming domain of quantitative MRI. However, the inherent "black-box" nature of deep learning models introduces an element of unknown risk when confronted with unseen data. This is a critical concern in qMRI, where the utmost reliability is imperative.

Goal(s): To develop a qMRI reconstruction framework which could quantifies model uncertainty for guiding clinical decisions.

Approach: We presented a conditional Wasserstein GAN for qMRI reconstruction, enabling uncertainty assessment through posterior sampling.

Results: The proposed method achieved comparable performance to the current method while offering valuable pixel-wise uncertainty maps.

Impact: The study offers clinicians and researchers a reliable qMRI reconstruction method with pixel-wise uncertainty assessment. This could spark further investigations into model reliability and potentially facilitate the practical application of deep learning-based qMRI methods.

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