CT-Guided and Sparsity-Constrained Multi-Material Decomposition for Dual Energy CBCT
Q Wang1*, H Xie1, T Wang1, J Roper1, X Tang2, J Bradley1, T Liu1, X Yang1, (1) Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, (2) Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA
Presentations
TH-F-201-3 (Thursday, 7/14/2022) 2:00 PM - 3:00 PM [Eastern Time (GMT-4)]
Room 201
Purpose: Dual-energy CBCT (DE-CBCT) can not only overcome the traditional issues of CBCT but also develop promising dual energy applications, such as material decomposition. This study aims to propose a CT-guided and sparsity-constrained multi-material decomposition method for DE-CBCT.
Methods: A constrained optimization model with multi-objectives is established to improve the image quality and quantitative accuracy of both DE-CBCT and material composition images. First, we employ a locally linear constraint to mathematically describe the structural similarities between CT and DE-CBCT or basis material images. Thus, CT can effectively work as a guide by introducing its superior image quality to both DE-CBCT and material composition images. In addition, to regulate the decomposition process of multi-materials, we employ low-rank approximation with trace norm truncation to limit the number of material bases and incorporate a sum-to-one constraint to bound the material volume fractions. In addition to considering the decomposed material images as piece-wise constant, we employ Mumford-Shah regularization in the spatial domain for edge preservation and piecewise smoothness. Further, we develop an iterative algorithm according to the alternating direction method of multipliers and implement it with parallel acceleration techniques to support potential real-time adaptive treatment planning.
Results: The proposed method can consistently preserve CT values, and effectively reduce noise, especially for low-energy scenarios (standard deviation: from 0.06 to 0.02 for Teflon, from 0.05 to 0.03 for 50% bone, and from 0.03 to 0.02 for solid water). Both phantom and patient studies demonstrate the superiority of the proposed method in image-quality enhancement, decomposition-accuracy improvement, noise suppression, and artifact reduction.
Conclusion: The proposed iterative multi-material decomposition method with multi-domain constraints can effectively improve the quality of DE-CBCT images and accuracy of material decomposition maps, which has a great potential to benefit dose verification, online contouring, and synthesis of monoenergetic images during cancer radiotherapy.
Keywords
Cone-beam CT, Dual-energy Imaging, Optimization
Taxonomy
IM- Cone Beam CT: Dual-energy and spectral
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