The content discusses the challenges faced in fast MRI image reconstruction due to prolonged scan times and k-space undersampling artifacts. The author introduces a novel Physics-Informed Synthetic data learning framework called PISF, which enables generalized DL for multi-scenario MRI reconstruction through synthetic data training. By utilizing enhanced learning techniques, the PISF approach demonstrates remarkable generalizability across multiple vendors and imaging centers, reducing reliance on real-world MRI data by up to 96%. The method shows promising results in adapting to diverse patient populations and offers a cost-effective way to boost DL adoption in fast MRI applications.
The study evaluates the substitutability of PISF for conventional DL methods using realistic training data and showcases its performance in multi-vendor multi-center imaging scenarios. Additionally, the adaptability of PISF to patients with pathologies is examined through a reader study evaluating image quality criteria. The proposed framework proves effective in reconstructing high-quality images across various contrasts, anatomies, vendors, centers, and pathologies from fast sampled data.
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by Zi Wang,Xiao... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2307.13220.pdfDeeper Inquiries