PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
This addresses the problem of processing massive tomography data efficiently for synchrotron researchers, but it is incremental as it builds on existing iterative methods with new implementations.
The authors developed PyHST2, a distributed code for high-speed tomographic reconstruction to handle large data flows (e.g., 10 terabytes per experiment) at synchrotron facilities, implementing iterative techniques with a-priori knowledge like total variation and a new convex functional to improve quality or reduce data volume.
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 third 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 and reach a given quality goal. The implemented a-priori knowledge techniques are based on the total variation penalisation and a new recently found convex functional which is based on overlapping patches. We give details of the different methods and their implementations while the code 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.