Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process, they do not provide any uncertainty information about the reconstructed image, which severely limits their use in practice, especially for safety-critical imaging applications. In this paper, we propose a learning-based image reconstruction framework that incorporates the observation model into the reconstruction task and that is capable of quantifying epistemic and aleatoric uncertainties, based on deep unrolling and Bayesian neural networks. We demonstrate the uncertainty characterization capability of the proposed framework on magnetic resonance imaging and computed tomography reconstruction problems. We investigate the characteristics of the epistemic and aleatoric uncertainty information provided by the proposed framework to motivate future research on utilizing uncertainty information to develop more accurate, robust, trustworthy, uncertainty-aware, learning-based image reconstruction and analysis methods for imaging problems. We show that the proposed framework can provide uncertainty information while achieving comparable reconstruction performance to state-of-the-art deep unrolling methods.

翻译：深层不动是正在形成的深层次基于学习的图像重建方法,它弥合了基于模型和纯粹基于深层次基于学习的图像重建方法之间的差距。虽然深层不动方法在成像问题方面达到最先进的性能,并允许将观测模型纳入重建进程,但它们没有提供关于重建图像的任何不确定信息,严重限制了其在实践中的使用,特别是安全关键成像应用。在本文件中,我们提议了一个基于学习的图像重建框架,将观察模型纳入重建任务,并能够量化基于深层无滚动和贝叶色神经网络的成象和感知不确定性。我们展示了拟议的磁共振成像框架的不确定性定性能力,并计算了成像重建问题。我们调查了拟议框架提供的成像和感知不确定性信息的特点,以激励今后研究利用不确定性信息,为成像问题开发更准确、可靠、可信、可靠、有不确定性、基于学习的图像重建和分析方法。我们表明,拟议的框架可以提供不确定性信息,同时实现可比较的重建性重建绩效和状态方法。