Reconstruction of the enlarged field-of-view by interior tomography with detector offset

Ohsung Oh; Daeseung Kim; Seung Wook Lee

doi:10.1117/12.3027952

4 October 2024 Reconstruction of the enlarged field-of-view by interior tomography with detector offset

Ohsung Oh, Daeseung Kim, Seung Wook Lee

Proceedings Volume 13152, Developments in X-Ray Tomography XV; 131520B (2024) https://doi.org/10.1117/12.3027952
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

We explore interior tomography, a technique facilitating the observation of a region-of-interest (ROI) in computerized tomography (CT) through a strategically adjusted detector offset. By modifying the offset, we extend the field-of-view (FOV), consequently enlarging the ROI. Our innovative approach involves offsetting the detector to cover asymmetric regions during data acquisition, overcoming challenges faced by conventional reconstruction algorithms dealing with truncated projection data in interior tomography. To address these issues, we employ a deep learning (DL) network for interior tomography with a detector offset, comparing its performance with other reconstruction methods. Our DL network leverages the weighted filtered back projection (FBP) as input and incorporates the ROI map as additional information, enabling flexible ROI image acquisition within a single network. Trained on abdominal CT projection data, our network exhibits superior performance compared to existing methods. This methodology holds promise for advancing system fusion and miniaturization, particularly in omni-tomography, as it efficiently eliminates noise and artifacts in a shorter time.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ohsung Oh, Daeseung Kim, and Seung Wook Lee "Reconstruction of the enlarged field-of-view by interior tomography with detector offset", Proc. SPIE 13152, Developments in X-Ray Tomography XV, 131520B (4 October 2024); https://doi.org/10.1117/12.3027952

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KEYWORDS

Sensors

Computed tomography

Deep learning

Image restoration

Education and training

Tomography

Tunable filters

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