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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction.
IEEE Transactions on Medical Imaging
|September 10, 2021
Summary
This study introduces a Bayesian framework to quantify model uncertainty in MRI reconstruction. It accurately measures pixelwise epistemic uncertainty, aiding radiologists in assessing reconstruction reliability.
Area of Science:
- Medical Imaging
- Machine Learning
- Bayesian Inference
Background:
- Deep learning in MRI reconstruction often prioritizes benchmark scores, neglecting model-immanent (epistemic) uncertainty.
- Systematic analysis of epistemic uncertainty in MRI reconstruction is less common.
Purpose of the Study:
- To introduce a Bayesian variational framework for quantifying epistemic uncertainty in undersampled MRI reconstruction.
- To develop a method that provides radiologists with a measure of reconstruction reliability.
Main Methods:
- A Bayesian variational framework was employed to solve the linear inverse problem of undersampled MRI reconstruction.
- The energy functional incorporated a data fidelity term and a learned parametric regularizer (Total Deep Variation - TDV).
- Epistemic uncertainty was estimated by sampling TDV parameters from a learned multivariate Gaussian distribution via stochastic optimal control.
Main Results:
- The proposed approach achieved competitive results in undersampled MRI reconstruction.
- Accurate quantification of pixelwise epistemic uncertainty was demonstrated.
- The quantified uncertainty provides a valuable tool for visualizing reconstruction reliability.
Conclusions:
- The Bayesian variational framework effectively quantifies epistemic uncertainty in MRI reconstruction.
- This method offers a reliable measure of reconstruction quality, enhancing diagnostic confidence for radiologists.
- The approach advances deep learning applications in medical imaging by addressing model uncertainty.
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