Inverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT
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 mov...
| Authors | Lukas Förner, Melina Wördehoff, Julian Steffens et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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