CT Material Decomposition using Spectral Diffusion Posterior Sampling
Xiao Jiang Grace J. Gang J. Webster Stayman
In this work, we introduce a new deep learning approach based on diffusion
posterior sampling (DPS) to perform material decomposition from spectral CT
measurements. This approach combines sophisticated prior knowledge from
unsupervised training with a rigorous physical model of the measurements. A
faster and more stable variant is proposed that uses a jumpstarted process to
reduce the number of time steps required in the reverse process and a gradient
approximation to reduce the computational cost. Performance is investigated for
two spectral CT systems: dual-kVp and dual-layer detector CT. On both systems,
DPS achieves high Structure Similarity Index Metric Measure(SSIM) with only 10%
of iterations as used in the model-based material decomposition(MBMD).
Jumpstarted DPS (JSDPS) further reduces computational time by over 85% and
achieves the highest accuracy, the lowest uncertainty, and the lowest
computational costs compared to classic DPS and MBMD. The results demonstrate
the potential of JSDPS for providing relatively fast and accurate material
decomposition based on spectral CT data.