Conditional Diffusion Model for Versatile Temporal Inpainting in 4D Cerebral CT Perfusion Imaging

Cerebral CT Perfusion (CTP) sequence imaging is a widely used modality for stroke assessment. While high temporal resolution of CT scans is crucial for accurate diagnosis, it correlates to increased radiation exposure. A promising solution is to generate synthetic CT scans to artificially enhance the temporal resolution of the sequence. We present a versatile CTP sequence inpainting model based on a conditional diffusion model, which can inpaint temporal gaps with synthetic scan to a fine 1-second interval, agnostic to both the duration of the gap and the sequence length. We achieve this by incorporating a carefully engineered conditioning scheme that exploits the intrinsic patterns of time-concentration dynamics. Our approach is much more flexible and clinically relevant compared to existing interpolation methods that either (1) lack such perfusion-specific guidances or (2) require all the known scans in the sequence, thereby imposing constraints on the length and acquisition interval. Such flexibility allows our model to be effectively applied to other tasks, such as repairing sequences with significant motion artifacts. Our model can generate accurate and realistic CT scans to inpaint gaps as wide as 8 seconds while achieving both perceptual quality and diagnostic information comparable to the ground-truth 1-second resolution sequence. Extensive experiments demonstrate the superiority of our model over prior arts in numerous metrics and clinical applicability. Our code is available at https://github.com/baejustin/CTP_Inpainting_Diffusion .