Magnetic Resonance in Medicine· 2017Q1
Learning a variational network for reconstruction of accelerated MRI data
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- Q1SCImago
- 2017year
Short summary
A novel variational network reconstructs accelerated MRI data 193 ms faster than standard methods, preserving image quality and pathologies even for unseen data.
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Key points
- A variational network combines variational models and deep learning for accelerated MRI reconstruction.
- All network parameters, including prior models and data term weights, are learned offline.
- Reconstructions outperform standard algorithms on clinical knee MRI data, even with unseen pathologies.
- Reconstruction time is 193 ms per image on a single GPU, enabling clinical workflow integration.
AI-generated from the title and abstract; the full text is not read.
Abstract
PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. THEORY AND METHODS: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. RESULTS: The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. CONCLUSION: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
The authors' abstract, as published at the source. Magnetic Resonance in Medicine, 2017 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine