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PRECISION: A Physics-Constrained and Noise-Controlled Diffusion Model for Photon Counting Computed Tomography | IEEE Journals & Magazine | IEEE Xplore

PRECISION: A Physics-Constrained and Noise-Controlled Diffusion Model for Photon Counting Computed Tomography


Abstract:

Recently, the use of photon counting detectors in computed tomography (PCCT) has attracted extensive attention. It is highly desired to improve the quality of material ba...Show More

Abstract:

Recently, the use of photon counting detectors in computed tomography (PCCT) has attracted extensive attention. It is highly desired to improve the quality of material basis image and the quantitative accuracy of elemental composition, particularly when PCCT data is acquired at lower radiation dose levels. In this work, we develop a physics-constrained and noise-controlled diffusion model, PRECISION in short, to address the degraded quality of material basis images and inaccurate quantification of elemental composition mainly caused by imperfect noise model and/or hand-crafted regularization of material basis images, such as local smoothness and/or sparsity, leveraged in the existing direct material basis image reconstruction approaches. In stark contrast, PRECISION learns distribution-level regularization to describe the feature of ideal material basis images via training a noise-controlled spatial-spectral diffusion model. The optimal material basis images of each individual subject are sampled from this learned distribution under the constraint of the physical model of a given PCCT and the measured data obtained from the subject. PRECISION exhibits the potential to improve the quality of material basis images and the quantitative accuracy of elemental composition for PCCT.
Published in: IEEE Transactions on Medical Imaging ( Volume: 43, Issue: 10, October 2024)
Page(s): 3476 - 3489
Date of Publication: 08 August 2024

ISSN Information:

PubMed ID: 39115984

Funding Agency:


References

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