3 March 2012 Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction
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Abstract
Four dimensional computed tomography (4D-CT) is very important for treatment planning in thorax or abdomen area, e.g. for guiding radiation therapy planning. The respiratory motion makes the reconstruction problem illposed. Recently, compressed sensing theory was introduced. It uses sparsity as a prior to solve the problem and improves image quality considerably. However, the images at each phase are reconstructed individually. The correlations between neighboring phases are not considered in the reconstruction process. In this paper, we propose the spatial-temporal total variation regularization (STTVR) method which not only employs the sparsity in the spatial domain but also in the temporal domain. The algorithm is validated with XCAT thorax phantom. The Euclidean norm of the reconstructed image and ground truth is calculated for evaluation. The results indicate that our method improves the reconstruction quality by more than 50% compared to standard ART.
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Haibo Wu, Andreas Maier, Rebecca Fahrig, Joachim Hornegger, "Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133J (3 March 2012); doi: 10.1117/12.911162; https://doi.org/10.1117/12.911162
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