Adding historical tracking to diffusion-based medical image reconstruction eliminates the bias-hallucination tradeoff and guarantees convergence to...
This paper fixes a problem with using AI image generators to reconstruct medical scans from incomplete data. Previous methods lose track of what they've already tried, causing them to either ignore measurement constraints or hallucinate fake details. The solution adds memory to the optimization process and cleans up noise patterns so the AI generator works correctly.