One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
CoRR(2023)
摘要
Magnetic resonance imaging (MRI) is a widely used radiological modality
renowned for its radiation-free, comprehensive insights into the human body,
facilitating medical diagnoses. However, the drawback of prolonged scan times
hinders its accessibility. The k-space undersampling offers a solution, yet the
resultant artifacts necessitate meticulous removal during image reconstruction.
Although Deep Learning (DL) has proven effective for fast MRI image
reconstruction, its broader applicability across various imaging scenarios has
been constrained. Challenges include the high cost and privacy restrictions
associated with acquiring large-scale, diverse training data, coupled with the
inherent difficulty of addressing mismatches between training and target data
in existing DL methodologies. Here, we present a novel Physics-Informed
Synthetic data learning framework for Fast MRI, called PISF. PISF marks a
breakthrough by enabling generalized DL for multi-scenario MRI reconstruction
through a single trained model. Our approach separates the reconstruction of a
2D image into many 1D basic problems, commencing with 1D data synthesis to
facilitate generalization. We demonstrate that training DL models on synthetic
data, coupled with enhanced learning techniques, yields in vivo MRI
reconstructions comparable to or surpassing those of models trained on matched
realistic datasets, reducing the reliance on real-world MRI data by up to 96
Additionally, PISF exhibits remarkable generalizability across multiple vendors
and imaging centers. Its adaptability to diverse patient populations has been
validated through evaluations by ten experienced medical professionals. PISF
presents a feasible and cost-effective way to significantly boost the
widespread adoption of DL in various fast MRI applications.
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关键词
synthetic data,deep learning,mri,generalizable
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