Purpose: To develop a deep learning‐based Bayesian estimation for MRI reconstruction.Methods: We modeled the MRI reconstruction problem with Bayes’s theorem, following the recently proposed PixelCNN++ method. The image reconstruction from incomplete k‐space measurement was obtained by maximizing the posterior possibility. A generative network was utilized as the image prior, which was computationally tractable, and the k‐space data fidelity was enforced by using an equality constraint. The stochastic backpropagation was utilized to calculate the descent gradient in the process of maximum a posterior, and a projected subgradient method was used to impose the equality constraint. In contrast to the other deep learning reconstruction methods, the proposed one used the likelihood of prior as the training loss and the objective function in reconstruction to improve the image quality.Results: The proposed method showed an improved performance in preserving image details and reducing aliasing artifacts, compared with GRAPPA, ‐ESPRiT, model‐based deep learning architecture for inverse problems (MODL), and variational network (VN), last two were state‐of‐the‐art deep learning reconstruction methods. The proposed method generally achieved more than 3 dB peak signal‐to‐noise ratio improvement for compressed sensing and parallel imaging reconstructions compared with the other methods.Conclusions: The Bayesian estimation significantly improved the reconstruction performance, compared with the conventional ‐sparsity prior in compressed sensing reconstruction tasks. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.

Purpose: Recent development of ultra‐low‐field (ULF) MRI presents opportunities for low‐power, shielding‐free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large‐scale publicly available 3T brain data.Methods: A dual‐acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross‐scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T₁‐weighted and T₂‐weighted imaging were trained with 3D ULF image data sets synthesized from the high‐resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3‐mm acquisition resolution in healthy volunteers, young and old, as well as patients.Results: The proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5‐mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI.Conclusion: The proposed dual‐acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high‐field brain data. Such strategy can empower ULF MRI for low‐cost brain imaging, especially in point‐of‐care scenarios or/and in low‐income and mid‐income countries.