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

arXiv: https://arxiv.org/abs/1306.1392 · HTML: https://mlsanity.com/paper/1306.1392/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2013.

## Built on by (most cited first, 4 of 4)

- [A modular U-Net for automated segmentation of X-ray tomography images in composite materials](https://mlsanity.com/paper/2107.07468/) (2021)
- [Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney](https://mlsanity.com/paper/2108.02226/) (2021)
- [Automated segmentation of microtomography imaging of Egyptian mummies](https://mlsanity.com/paper/2105.06738/) (2021)
- [Why do we regularise in every iteration for imaging inverse problems?](https://mlsanity.com/paper/2411.00688/) (2024)

## Abstract (arXiv)

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.