生成式翻译先验：基于跨模态图像翻译的贝叶斯成像
Generative Translation Priors: Bayesian Imaging with Cross-Modality Image Translation
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中文总结 AI 辅助
本研究提出生成式翻译先验（GTP）贝叶斯框架，将扩散式图像翻译模型转化为跨模态先验，推导两种离散化算法，在CT、PET重建任务中验证其可有效融入跨模态信息，欠采样下仍实现高保真重建。
中文摘要 AI 辅助
在成像算法中，利用共可用模态的图像为目标域重建提供信息的能力极具价值。本研究提出生成式翻译先验（Generative Translation Priors, GTP）——一种将基于扩散的图像到图像翻译模型转化为不适定成像逆问题的跨模态图像先验的贝叶斯框架。GTP通过似然引导融入目标域测量，将翻译过程导向期望的后验分布。该框架基于对所得后验动态的理论分析，揭示了似然引导引入的固有偏差；我们进一步表征此偏差并推导无需真实值的估计公式，使其可作为评估后验采样质量的实用指标。基于此分析，我们分别推导了基于梯度和近端似然引导的两种离散化GTP算法。我们在带磁共振侧信息的计算机断层扫描重建、带计算机断层扫描侧信息的正电子发射断层扫描重建上验证GTP，实验表明GTP可有效融入互补跨模态信息，即便在严重欠采样测量下也能实现高保真重建。
英文摘要
The ability to leverage images from co-available modalities to inform target-domain reconstruction is highly desirable in imaging algorithms. In this work, we introduce Generative Translation Priors (GTP)--a Bayesian framework that transforms diffusion-based image-to-image translation models into cross-modality image priors for ill-posed imaging inverse problems. GTP incorporates target-domain measurements through likelihood guidance, steering the translation process toward the desired posterior distribution. The framework is grounded in a theoretical analysis of the resulting posterior dynamics, which reveals an intrinsic bias introduced by likelihood guidance. We further characterize this bias and derive a ground-truth-free formulation for its estimation, enabling it to serve as a practical metric for assessing posterior sampling quality. Building on this analysis, we derive two discretized GTP algorithms based on gradient and proximal likelihood guidance, respectively. We validate GTP on computed tomography reconstruction with magnetic resonance side information, and on positron emission tomography reconstruction with computed tomography side information. Experiments demonstrate that GTP effectively incorporates complementary cross-modality information and achieves high-fidelity reconstruction even under severely undersampled measurements.
发表机构
- Johns Hopkins University（约翰斯·霍普金斯大学）
- Stanford University（斯坦福大学）
- University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。
