TY - JOUR
T1 - Material Decomposition in Photon-Counting Computed Tomography With Diffusion Models
T2 - Comparative Study and Hybridization With Variational Regularizers
AU - Vazia, Corentin
AU - Dassow, Thore
AU - Bousse, Alexandre
AU - Froment, Jacques
AU - Vedel, Beatrice
AU - Vermet, Franck
AU - Perelli, Alessandro
AU - Tasu, Jean Pierre
AU - Visvikis, Dimitris
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2026/1/6
Y1 - 2026/1/6
N2 - Photon-counting computed tomography (PCCT) has emerged as a promising imaging technique, enabling spectral imaging and material decomposition (MD). However, images typically suffer from a low signal-to-noise ratio (SNR) due to constraints such as low photon counts and sparse-view settings which provoke artifacts. To prevent this, variational methods minimize a data-fit function coupled with handcrafted regularizers that mimic a prior by enforcing image properties such as gradient sparsity. In the last few years, diffusion models (DMs) have become predominant in the field of generative models and have been used as a learned prior for image reconstruction. This work investigates the use of DMs as regularizers for MD tasks in PCCT, specifically using diffusion posterior sampling (DPS) guidance. Three DPS-based approaches–image-domain two-step DPS (im-TDPS), projection-domain two-step DPS (proj-TDPS), and one-step DPS (ODPS)–are evaluated. The first two methods achieve MD in two steps by performing reconstruction and MD separately. The last method, ODPS, samples the material images directly from the measurement data. The results indicate that ODPS achieves superior performance compared to im-TDPS and proj-TDPS, providing sharper, noise-free and crosstalk-free images. Furthermore, we introduce a novel hybrid method for scenarios involving materials absent from the training dataset. This method combines DM priors with standard variational handcrafted regularizers for the materials unknown to the DM. This hybrid method demonstrates improved MD quality compared to a standard variational method and does not require additional training of the DM neural network (NN).
AB - Photon-counting computed tomography (PCCT) has emerged as a promising imaging technique, enabling spectral imaging and material decomposition (MD). However, images typically suffer from a low signal-to-noise ratio (SNR) due to constraints such as low photon counts and sparse-view settings which provoke artifacts. To prevent this, variational methods minimize a data-fit function coupled with handcrafted regularizers that mimic a prior by enforcing image properties such as gradient sparsity. In the last few years, diffusion models (DMs) have become predominant in the field of generative models and have been used as a learned prior for image reconstruction. This work investigates the use of DMs as regularizers for MD tasks in PCCT, specifically using diffusion posterior sampling (DPS) guidance. Three DPS-based approaches–image-domain two-step DPS (im-TDPS), projection-domain two-step DPS (proj-TDPS), and one-step DPS (ODPS)–are evaluated. The first two methods achieve MD in two steps by performing reconstruction and MD separately. The last method, ODPS, samples the material images directly from the measurement data. The results indicate that ODPS achieves superior performance compared to im-TDPS and proj-TDPS, providing sharper, noise-free and crosstalk-free images. Furthermore, we introduce a novel hybrid method for scenarios involving materials absent from the training dataset. This method combines DM priors with standard variational handcrafted regularizers for the materials unknown to the DM. This hybrid method demonstrates improved MD quality compared to a standard variational method and does not require additional training of the DM neural network (NN).
KW - Diffusion Posterior Sampling
KW - Material Decomposition
KW - Photon-Counting Computed Tomography
UR - https://www.scopus.com/pages/publications/105027347247
U2 - 10.1109/TRPMS.2026.3651354
DO - 10.1109/TRPMS.2026.3651354
M3 - Article
AN - SCOPUS:105027347247
SN - 2469-7311
JO - IEEE Transactions on Radiation and Plasma Medical Sciences
JF - IEEE Transactions on Radiation and Plasma Medical Sciences
ER -