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Volume: 33 | Article ID: art00009
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Reducing motion artifact in sequential-scan dual-energy CT imaging by incorporating deformable registration within joint statistical image reconstruction
  DOI :  10.2352/ISSN.2470-1173.2021.15.COIMG-293  Published OnlineJanuary 2021
Abstract

Dual-energy computed tomography (DECT) has been widely used to reconstruct basis components. In previous studies, ou DECT algorithm has shown high accuracy in stopping power ratio (SPR) estimation of fixed objects for proton radiotherapy planning. However, patient movement between sequential data acquisitions may lead to severe motion artifacts in the component images. In order to reduce or eliminate the motion artifacts in clinical applications, we combine a deformable registration method with an accurate joint statistical iterative reconstruction algorithm, dual-energy alternating minimization (DEAM). Image registration is a process of geometrically aligning two or more images. We implement a multi-modality symmetric deformable registration method based on Advanced Normalization Tools (ANTs) to automatically align the scans we acquire for the same patient. The precalculated registration mapping and its inverse are then embedded into each iteration of the DEAM algorithm. The performance of warped DEAM is quantitatively assessed. Theoretically, the performance of warped DEAM on moved patients should be comparable to the performance of the original DEAM algorithm on fixed objects. The warped DEAM algorithm reduces motion artifacts while preserving the accuracy of the iterative joint statistical CT reconstruction algorithm, which enables us to reconstruct accurate results from sequentially scanned dual-energy patient data.

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Tao Ge, Rui Liao, David G. Politte, Maria Medrano, Jeffrey F. Williamson, Bruce R. Whiting, Tianyu Zhao, Joseph A. O’Sullivan, "Reducing motion artifact in sequential-scan dual-energy CT imaging by incorporating deformable registration within joint statistical image reconstructionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XIX,  2021,  pp 293-1 - 293-6,  https://doi.org/10.2352/ISSN.2470-1173.2021.15.COIMG-293

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