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Research project: Fast,large scale optimization algorithms for tomographic image reconstruction

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This project focuses on developing novel algorithms that are tailored to large-scale tomographic reconstruction. The algorithms will be based on the latest developments in the field of compressed sensing and randomised algorithms. This will be achieved by developing algorithms that run efficiently on modern high-performance computing infrastructures, where each computing node does not have fast access to the entire dataset at once and where communication between different nodes is relatively slow. The project is being carried out by Associate processor Thomas Blumensath and his PhD student Yushan Gao

Project Overview

Research group: μ-vis x-ray imaging centre

Tomography imaging techniques produce volumetric images of the three-dimensional structure of an object. These techniques are used in a wide range of applications such as routine medical diagnosis, non-destructive testing of manufactured components and the scientific study of biological cells and so on. X-ray radiation is one of the common modalities used for three-dimensional imaging and in this case, x-ray projection images are typically collected from the object at different orientations and these projections are then used to compute a volumetric representation of the object’s internal x-ray attenuation profile. Scientific and industrial tomographic imaging applications require the use of ever larger datasets as they increasingly use larger and higher resolution detectors and/or use increasing numbers of projections to scan the object with the required resolution. Furthermore, the need to discern ever-finer details within an object leads to an increase in the desired resolution of the reconstructed volume. If this is paired with the use of non-standard tomographic scanning trajectories, then the filtered back projection algorithm, which remains the primary workhorse for the tomographic reconstruction of large datasets, is no longer applicable. To address these challenges, this project will develop novel algorithms that can be parallel applied among distributed networks to reduce the reconstruction time and computation load for each node.

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