End-to-End Model-Based Deep Learning for Dual-Energy Computed Tomography Material Decomposition

TY - GEN

T1 - End-to-End Model-Based Deep Learning for Dual-Energy Computed Tomography Material Decomposition

AU - Wang, Jiandong

AU - Perelli, Alessandro

N1 - Publisher Copyright: © 2024 IEEE.

PY - 2024/8/22

Y1 - 2024/8/22

N2 - Dual energy X-ray Computed Tomography (DECT) enables to automatically decompose materials in clinical images without the manual segmentation using the dependency of the X-ray linear attenuation with energy. In this work we propose a deep learning procedure called End-to-End Material Decomposition (E2E-DEcomp) for quantitative material decomposition which directly convert the CT projection data into material images. The algorithm is based on incorporating the knowledge of the spectral model DECT system into the deep learning training loss and combining a data-learned prior in the material image domain. Furthermore, the training does not require any energy-based images in the dataset but rather only sinogram and material images. We show the effectiveness of the proposed direct E2E-DEcomp method on the AAPM spectral CT dataset compared with state of the art supervised deep learning networks.

AB - Dual energy X-ray Computed Tomography (DECT) enables to automatically decompose materials in clinical images without the manual segmentation using the dependency of the X-ray linear attenuation with energy. In this work we propose a deep learning procedure called End-to-End Material Decomposition (E2E-DEcomp) for quantitative material decomposition which directly convert the CT projection data into material images. The algorithm is based on incorporating the knowledge of the spectral model DECT system into the deep learning training loss and combining a data-learned prior in the material image domain. Furthermore, the training does not require any energy-based images in the dataset but rather only sinogram and material images. We show the effectiveness of the proposed direct E2E-DEcomp method on the AAPM spectral CT dataset compared with state of the art supervised deep learning networks.

KW - Dual-energy CT (DECT)

KW - Material Decomposition

KW - Deep Learning

KW - Optimization

KW - Deep learning

KW - Material decomposition

KW - Dual Energy Computed Tomography

UR - https://www.scopus.com/pages/publications/85203388145

U2 - 10.1109/ISBI56570.2024.10635736

DO - 10.1109/ISBI56570.2024.10635736

M3 - Conference contribution

SN - 9798350313345

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

PB - IEEE

T2 - 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Y2 - 27 May 2024 through 30 May 2024

ER -