PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
By Alessandro Mirone, Emmanuelle Gouillart, Emmanuel Brun, Paul Tafforeau, Jérôme Kieffer. arXiv.org, 2013
456 citations recorded by the source corpus.
We present the PyHST2 code which is in service at ESRF for phase-contrast and absorption tomography. This code has been engineered to sustain the high data flow typical of the 3rd generation synchrotron facilities (10 terabytes per experiment) by adopting a distributed and pipelined architecture. The code implements, beside a default filtered backprojection reconstruction, iterative reconstruction techniques with a priori knowledge. These latter are used to improve the reconstruction quality or in order to reduce the required data volume or the deposited dose to the sample and reach a given quality goal. The implemented a priori knowledge techniques are based on the total variation penalization and a new recently found convex functional which is based on overlapping patches. We give details of the different methods and discuss how they are implemented in the PyHST2 code, which is distributed under free license. We provide methods for estimating, in the absence of ground-truth data, the optimal parameters values for a priori techniques.
Subfields: Biomedical Engineering, Radiation, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/17adf0ee4fa8c3aa1caa6bc402856e75b9a4dce6
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In Biomedical Engineering
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Citations in the corpus
- TomoPy: a framework for the analysis of synchrotron tomographic data (2014, methodology): “There are numerous methods suitable for this task, each having different strengths and weaknesses depending on the applied data and timing constraints (Mirone et al., 2014; Rivers, 2012).”
- Heterogeneous Damage in Li-Ion Batteries: Experimental Analysis and Theoretical Modeling (2019, methodology): “The 2D phase mapping retrieved from the angular projections was then used as input for a tomographic reconstruction via the filtered back projection (FBP) algorithm method ( Mirone et al., 2014 ).”