Medical imaging aims to recover underlying tissue properties, using inexact (simplified/linearized) imaging models and often from inaccurate and incomplete measurements. Analytical reconstruction methods rely on hand-crafted regularization, sensitive to noise assumptions and parameter tuning. Among deep learning alternatives, plug-and-play (PnP) approaches learn regularization while incorporating imaging physics during inference, outperforming purely data-driven methods. The performance of all these approaches, however, still strongly depends on measurement quality and imaging model accuracy. In this work, we propose DenOiS, a framework that denoises both input observations and resulting solution in their respective domains. It consists of an observation refinement strategy that corrects degraded measurements while compensating for imaging model simplifications, and a diffusion-based PnP reconstruction approach that remains robust under missing measurements. DenOiS enables generalization to real data from training only in simulations, resulting in high-fidelity image reconstruction with noisy observations and inexact imaging models. We demonstrate this for speed-of-sound imaging as a challenging setting of quantitative ultrasound image reconstruction.

翻译：摘要：医学成像旨在利用不精确（简化/线性化）的成像模型，并通常基于不准确且不完整的测量数据，恢复底层组织特性。解析重建方法依赖于人为设计的正则化，对噪声假设和参数调优敏感。在深度学习方法中，即插即用（PnP）方法在推理过程中学习正则化并融入成像物理机制，其性能优于纯数据驱动方法。然而，所有这些方法的性能仍高度依赖于测量质量及成像模型的准确性。本研究提出DenOiS框架，该框架在各自域中对输入观测值与所得解进行降噪处理。该框架包含一项观测值细化策略，用于修正退化的测量数据并补偿成像模型简化带来的误差，以及一种基于扩散模型的PnP重建方法，该方法在测量数据缺失时仍保持鲁棒性。DenOiS能够仅通过仿真训练即可泛化至真实数据，实现基于含噪观测值与不精确成像模型的高保真图像重建。我们以定量超声图像重建中的挑战性场景——声速成像为例，验证了该方法的有效性。