ArticleConference proceedings. International Conference on Image Formation in X-Ray Computed Tomography2024
CT Material Decomposition using Spectral Diffusion Posterior Sampling.
Article in Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling.IEEE transactions on bio-medical engineering · 2025Article
- Joint Estimation of Anatomy and Implants in X-ray CT using a Mixed Prior Model.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Joint Reconstruction and Scatter Estimation in Cone-beam CT using Diffusion Posterior Sampling.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.Journal of medical imaging (Bellingham, Wash.) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
Identifiers
39301204PMC11412325What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.