Towards In Loco X-ray Computed Tomography

Computed tomography (CT) is a non-invasive imaging technique that allows to reveal the inner structure of an object by combining a series of projection images that were acquired from dierent directions. CT nowadays has a broad range of applications, including those in medicine, preclinical research, nondestructive testing, materials science, etc. One common feature of the tomographic setups used in most applications is the requirement to put an object into a scanner. The rst major disadvantage of such a requirement is the constraint imposed on the size of the object that can be scanned. The second one is the need to move the object which might be dicult or might cause undesirable changes in the object. A possibility to perform in loco, i. e. on site, tomography will open up numerous applications for tomography in nondestructive testing, security, medicine, archaeology and veterinary, allowing to scan objects that are too large, heavy, fragile or dangerous to put into existing scanners. A mobile tomographic device with the X-ray source and the detector mounted on separate robotized platforms will allow to overcome the limitations of the conventional CT setups and provide a means of performing in loco tomography. The current achievements and promising results in the development of mobile robots, X-ray sources and detectors make the appearance of a mobile robotized tomographic device technically feasible in the coming years. However, the acquisition and the reconstruction of the datasets using mobile tomographic devices are likely to present a number of diculties for the conventional algorithms. Firstly, circular or helical source-detector trajectories, used nowadays in the majority of tomographic setups, might be unavailable or impractical due to the obstacles in the scanning scene, constituting the trajectory selection problem. Secondly, the conguration of the scanning scene might render certain projection directions unavailable, resulting in a limited angular range. Next, repositioning of the source and the detector might be time-consuming, leading to a possibility to only acquire a limited number of projections in a reasonable time. Furthermore, it might be impossible to acquire projections of the complete object from certain directions, thus requiring to deal with projection truncation. Finally, accurate determination of the position and the orientation of the source and the detector might be challenging, resulting in the need for projection alignment. In this thesis, three techniques are proposed that contribute towards the development of acquisition and reconstruction algorithms for mobile tomographic devices capable of in loco tomography. Only three of the mentioned issues are addressed and the proposed techniques are not supposed to be the complete solutions, but we hope to have contributed to the solutions yet to be found. While the variable distance approach and the dynamic angle selection algorithm aim at the improvement of the acquisition, making the rst steps towards the trajectory selection, the multiresolution Discrete Algebraic Reconstruction Technique (multiresolution DART, MDART) algorithm is a reconstruction algorithm that can handle the datasets with a small number of projections acquired from a limited angular range, signicantly reducing the related artefacts and producing accurate reconstructions.