<p dir="ltr">Computed tomography (CT) imaging is widely used in industrial and medical appli- cations for non-destructive visualization of internal sample morphology. Traditional CT reconstruction methods use projection images from a single rotation axis with a predefined set of orientations. However, for objects containing dense materials like metal, the use of a single rotation axis may leave some regions of the object obscured by the metal, even though projections from other rotation axes (or poses) might contain complementary information that would better resolve these obscured regions. Additionally, for certain CT applications, it is also desirable to reduce data acquisition time with an adaptive orientation selection strategy. </p><p dir="ltr">In this thesis, we propose advanced algorithms to improve reconstruction quality and reduce data acquisition time by efficiently leveraging the complementary information from the different orientations and rotation axes of a single object.</p><p dir="ltr">In the first portion of this thesis, we propose Multi-pose Fusion, an algorithm for reducing CT reconstruction artifacts by integrating CT measurements from multiple poses of a single object. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug- and-play, as a framework for integrating projection data from different poses. We present experimental results using both synthetic and measured CT data, and demonstrate that the Multi-pose Fusion reconstruction method is effective in reducing artifacts and improving image quality.</p><p dir="ltr">In the second portion of the this thesis, we propose an adaptive orientation selection method for the application of neutron computed tomography (nCT), in which the information from previously acquired measurements is used to decide the next measurement orientation. Using simulated and experimental data, we demonstrate that our method produces high- quality reconstructions using significantly fewer total measurements than the conventional approach.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26511571 |
| Date | 07 August 2024 |
| Creators | Diyu Yang (18966412) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Multi-pose_Fusion_and_Adaptive_Orientation_Selection_for_X-ray_and_Neutron_Computed_Tomography/26511571 |
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