Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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S. Seyyedi, M. Wieczorek, F. Pfeiffer, T. Lasser
Incorporating a Noise Reduction Technique into X-ray Tensor Tomography
IEEE Transactions on Computational Imaging 4(1):137-146, 2018 (bib)

X-ray tensor tomography (XTT) is a novel imaging modality for the three-dimensional reconstruction of X-ray scattering tensors from dark-field images obtained in a grating interferometry setup. The two-dimensional dark-field images measured in XTT are degraded by noise effects, such as detector readout noise and insufficient photon statistics, and consequently, the three-dimensional volumes reconstructed from this data exhibit noise artifacts. In this paper, we investigate the best way to incorporate a denoising technique into the XTT reconstruction pipeline, i.e., the popular total variation (TV) denoising technique. We propose two different schemes of including denoising in the reconstruction process, one using a column block-parallel iterative scheme and one using a whole-system approach. In addition, we compare the results when using a simple denoising approach applied either before or after reconstruction. The effectiveness is evaluated qualitatively and quantitatively based on datasets from an industrial sample and a clinical sample. The results clearly demonstrate the superiority of including denoising in the reconstruction process, along with slight advantages of the whole-system approach.
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