ArticleMagnetic resonance in medicine2022
A probabilistic Bayesian approach to recover
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation.Magnetic resonance in medicine · 2025Article
- Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.Magnetic resonance in medicine · 2023Article
- R-Mixup: Riemannian Mixup for Biological Networks.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
purposeUndersampling is used to reduce the scan time for high-resolution three-dimensional magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover THEORY: Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients.
methodsUndersampling takes place in the y-z plane of k-space according to the Poisson-disk pattern. Retrospective undersampling is performed to evaluate the performances of different reconstruction approaches, prospective undersampling is performed to demonstrate the feasibility of undersampling in practice.
resultsThe proposed AMP with parameter estimation (AMP-PE) approach successfully recovers
conclusionAMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.