@article{Zdun2022joint,
Author = {L. Zdun, M. Boberg, and C. Brandt},
Title = {Joint multi-patch reconstruction: fast and improved results by stochastic optimization.},
Journal = {<em>International Journal on Magnetic Particle Imaging</em>.},
Year = {(2022).},
Volume = {<strong>8</strong>.},
Number = {(2),},
Pages = {1-8},
Note = {article, multi-patch, artifact, openaccess},
Doi = {10.18416/IJMPI.2022.2212002},
Url = {https://journal.iwmpi.org/index.php/iwmpi/article/view/477},
Abstract = {In order to measure larger volumes in magnetic particle imaging, it is necessary to divide the region of interest into several patches and measure those patches individually due to a limited size of the field of view. This procedure yields truncation artifacts at the patches boundaries during reconstruction. Applying a regularization which takes into account neighbourhood structures not only on one patch but across all patches can significantly reduce those artifacts. However, the current state-of-the-art reconstruction method using the Kaczmarz algorithm is limited to Tikhonov regularization. We thus propose to use the stochastic primal-dual hybrid gradient method to solve the multi-patch reconstruction task. Our experiments show that the quality of our reconstructions is significantly higher than those obtained by Tikhonov regularization and Kaczmarz method. Moreover, using our proposed method, a joint reconstruction considerably reduces the computational costs compared to multiple single-patch reconstructions. The algorithm proposed is thus competitive to the current state-of-the-art method not only regarding reconstruction quality but also concerning the computational effort.}
}

@COMMENT{Bibtex file generated on 2026-5-14 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/ibi/publications }