PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities

TL;DR

PyHST2 is a high-speed, distributed tomographic reconstruction code capable of iterative methods and a-priori knowledge integration, optimized for large data flows at synchrotron facilities.

Contribution

The paper introduces PyHST2, a distributed code that combines high-speed processing with advanced iterative and a-priori reconstruction techniques for tomography.

Findings

Supports high data throughput of 10 terabytes per experiment.

Incorporates novel convex functional based on overlapping patches.

Provides methods for optimal parameter estimation without ground-truth data.

Abstract

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.