发表机构
University Hospital Augsburg; Technical University of Munich; Bavarian Center for Cancer Research (BZKF); University of Augsburg（奥格斯堡大学医院; 慕尼黑工业大学; 巴伐利亚癌症研究中心; 奥格斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对纵向光谱CT提取病变动态参数的挑战，提出逆贝叶斯框架，将光谱特征演变分解为三个分量，通过对NSCLC CT数据验证，能恢复不同动态模式，确立了逆动态推理方法，从静态特征提取迈向病变行为机制表征。
AI 中文摘要
纵向医学成像可捕捉病变的时间演变，但提取控制这种演变的潜在动态参数仍具有挑战性。我们提出了一个逆贝叶斯框架，用于从纵向光谱CT推断病变动态。我们将光谱特征（$x$）的演变分解为三个分量：$\frac{dx_i}{dt}=A_ix_i + B\cdot n + C\cdot\Delta x_{\text{sat}}$。其中$A_i$捕捉内在动态，$B$捕捉局部环境肿瘤负担，$C$捕捉环境/卫星状态变化。我们在来自转移灶的光子计数NSCLC CT数据上展示了该框架，恢复了不同的动态模式。合成验证证实了参数恢复，交叉耦合分析验证了我们的方法在存在非零耦合时能检测到它。这项工作将逆动态推理确立为从纵向成像中提取可解释参数的一种有原则的方法，从静态特征提取迈向病变行为的机制表征。代码和数据可在指定网址获取。
英文摘要
Longitudinal medical imaging captures temporal evolution of lesions, yet extracting the underlying dynamical parameters governing this evolution remains challenging. We propose an inverse Bayesian framework for inferring lesion dynamics from longitudinal spectral CT. We decompose spectral feature ($x$) evolution into three components: \begin{equation*} \frac{dx_i}{dt} = A_i x_i + B \cdot n + C \cdot Δx_{\text{sat}} \end{equation*} where $A_i$ captures intrinsic dynamics (lesion-autonomous evolution), $B$ captures local environment tumour burden (organ tumour burden through satellite count coupling), and $C$ captures environment/satellite state change (i.e., whether surrounding lesions move similarly or not). We demonstrate the framework on photon-counting NSCLC CT data from metastases, recovering distinct dynamical regimes: lung lesions exhibit significant satellite count coupling ($B=-0.34$, $p<0.05$) suggesting competitive dynamics, while liver lesions show synergistic satellite behaviour coupling ($C\approx+1.0$, $p<0.05$). Synthetic validation confirms parameter recovery, and cross-coupling analysis validates that our method detects non-zero coupling when present. This work establishes inverse dynamical inference as a principled methodology for extracting interpretable parameters from longitudinal imaging, moving beyond static feature extraction toward mechanistic characterisation of lesion behaviour. The code and data are available at: https://github.com/lukasf98/inverse-bayesian-inference
