Iodine K-edge imaging in photon counting CT through multiple two-basis decompositions and deep learning

Sara S. M. Tehrani; Karin Larsson; Fredrik Grönberg; Johannes Loberg; Hugo Linder; Mats U. Persson

doi:10.1117/12.3006727

1 April 2024 Iodine K-edge imaging in photon counting CT through multiple two-basis decompositions and deep learning

Sara S. M. Tehrani, Karin Larsson, Fredrik Grönberg, Johannes Loberg, Hugo Linder, Mats U. Persson

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252T (2024) https://doi.org/10.1117/12.3006727
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

One advantage of photon-counting CT compared to dual-energy CT is the possibility to perform K-edge imaging, where contrast agents such as iodine can be distinguished from other substances based on spectral characteristics. However, for iodine K-edge imaging in clinical CT, the three-basis decomposition problem is ill-conditioned due to the low K-edge energy of iodine, meaning that the decomposition is highly sensitive to both noise and miscalibrations. This makes robust three-basis decomposition difficult using standard techniques. In this simulation study we evaluate a novel method of performing K-edge imaging, which circumvents the challenging three-basis decomposition step by replacing it with multiple two-basis decompositions followed by a deep convolutional neural network to generate three basis images. Based on the XCAT phantom, we generated 1224 spectral phantom image slices of the neck, with iodine-filled blood vessels and calcifications, and simulated CT imaging in CatSim with a silicon-based detector model without quantum noise, i.e. in the high-dose limit. For each simulated slice, we used maximum likelihood to perform three two-basis decompositions, into PE-PVC, PE-iodine, and PVC-iodine, yielding six basis images in total. We then trained a U-Net to map these six input images to the ground-truth basis images, PE, PVC and iodine. Our results show that the proposed method can reproduce PE, PVC and iodine basis images with high accuracy, in the high-dose limit. This suggests that the proposed three-basis decomposition method may be a feasible way of performing K-edge CT imaging with iodine, with important potential implications for imaging of the carotid arteries.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sara S. M. Tehrani, Karin Larsson, Fredrik Grönberg, Johannes Loberg, Hugo Linder, and Mats U. Persson "Iodine K-edge imaging in photon counting CT through multiple two-basis decompositions and deep learning", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252T (1 April 2024); https://doi.org/10.1117/12.3006727

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