Paper
3 March 2012 Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction
Haibo Wu, Andreas Maier, Rebecca Fahrig, Joachim Hornegger
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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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haibo Wu, Andreas Maier, Rebecca Fahrig, and 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); https://doi.org/10.1117/12.911162
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Cited by 24 scholarly publications.
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KEYWORDS
Compressed sensing

Image quality

Image quality standards

Reconstruction algorithms

CT reconstruction

Computed tomography

3D modeling

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