Details

Autor(en) / Beteiligte

• Lang, Lukas F.
• Neumayer, Sebastian
• Öktem, Ozan
• Schönlieb, Carola-Bibiane

Titel

Template-Based Image Reconstruction from Sparse Tomographic Data

Ist Teil von

• Applied mathematics & optimization, 2020-12, Vol.82 (3), p.1081-1109

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2020

Quelle

EBSCOhost Business Source Ultimate

Beschreibungen/Notizen

• We propose a variational regularisation approach for the problem of template-based image reconstruction from indirect, noisy measurements as given, for instance, in X-ray computed tomography. An image is reconstructed from such measurements by deforming a given template image. The image registration is directly incorporated into the variational regularisation approach in the form of a partial differential equation that models the registration as either mass- or intensity-preserving transport from the template to the unknown reconstruction. We provide theoretical results for the proposed variational regularisation for both cases. In particular, we prove existence of a minimiser, stability with respect to the data, and convergence for vanishing noise when either of the abovementioned equations is imposed and more general distance functions are used. Numerically, we solve the problem by extending existing Lagrangian methods and propose a multilevel approach that is applicable whenever a suitable downsampling procedure for the operator and the measured data can be provided. Finally, we demonstrate the performance of our method for template-based image reconstruction from highly undersampled and noisy Radon transform data. We compare results for mass- and intensity-preserving image registration, various regularisation functionals, and different distance functions. Our results show that very reasonable reconstructions can be obtained when only few measurements are available and demonstrate that the use of a normalised cross correlation-based distance is advantageous when the image intensities between the template and the unknown image differ substantially.