Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set.
Magn Reson Med
; 92(1): 289-302, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38282254
ABSTRACT
PURPOSE:
To estimate pixel-wise predictive uncertainty for deep learning-based MR image reconstruction and to examine the impact of domain shifts and architecture robustness.METHODS:
Uncertainty prediction could provide a measure for robustness of deep learning (DL)-based MR image reconstruction from undersampled data. DL methods bear the risk of inducing reconstruction errors like in-painting of unrealistic structures or missing pathologies. These errors may be obscured by visual realism of DL reconstruction and thus remain undiscovered. Furthermore, most methods are task-agnostic and not well calibrated to domain shifts. We propose a strategy that estimates aleatoric (data) and epistemic (model) uncertainty, which entails training a deep ensemble (epistemic) with nonnegative log-likelihood (aleatoric) loss in addition to the conventional applied losses terms. The proposed procedure can be paired with any DL reconstruction, enabling investigations of their predictive uncertainties on a pixel level. Five different architectures were investigated on the fastMRI database. The impact on the examined uncertainty of in-distributional and out-of-distributional data with changes to undersampling pattern, imaging contrast, imaging orientation, anatomy, and pathology were explored.RESULTS:
Predictive uncertainty could be captured and showed good correlation to normalized mean squared error. Uncertainty was primarily focused along the aliased anatomies and on hyperintense and hypointense regions. The proposed uncertainty measure was able to detect disease prevalence shifts. Distinct predictive uncertainty patterns were observed for changing network architectures.CONCLUSION:
The proposed approach enables aleatoric and epistemic uncertainty prediction for DL-based MR reconstruction with an interpretable examination on a pixel level.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Imageamento por Ressonância Magnética
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Aprendizado Profundo
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Magn Reson Med
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Alemanha