This paper reframes medical imaging as a dynamic signal rather than a static picture. Using the Universal Resonance Model (URM), it interprets imaging features—such as perfusion, enhancement patterns, and variability—not only as markers of abnormal tissue, but as spatial indicators of reduced stability and delayed recovery. Imaging is presented as a map of where biological systems are most fragile, where perturbations are amplified, and where failure is most likely to emerge next. By shifting focus from lesions and size to landscapes and susceptibility, the paper argues that imaging can become a tool for understanding trajectories, not just states—helping to identify vulnerability before collapse rather than damage after it.
Anita Domargård (Wed,) studied this question.