CT metal artifact reduction with machine learning and photon-counting techniques

Authors
Gjesteby, Lars Arne
ORCID
Loading...
Thumbnail Image
Other Contributors
Wang, Ge, 1957-
De Man, Bruno
Xu, Xie George
Yan, Pingkun
Issue Date
2018-12
Keywords
Biomedical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
This dissertation builds upon work over the past four decades towards reducing CT metal artifacts. We present a novel hybrid approach to improve the acquired data fidelity with an energy-discriminating photon-counting detector (PCD) and perform advanced image-based correction with machine learning algorithms. A PCD measures the energy of every incident x-ray photon, whereas a traditional detector integrates signals received over the entire energy spectrum. By determining individual photon energies, the PCD avoids beam hardening and electronic noise, and also suppresses scatter. A major undertaking of this work has been to upgrade and characterize a benchtop CT system with a PCD for spectral imaging. In parallel, we have developed and optimized deep learning algorithms for mapping between metal-corrupted CT images and artifact-free counterparts. We demonstrate the unique capability of the benchtop system to test and refine algorithms for MAR with physical data. Our methods indicate that higher image quality can be achieved over state-of-the-art MAR methods in challenging cases. Additionally, we have designed and analyzed novel multi-modality imaging techniques for simultaneous acquisition of complementary datasets. These schemes demonstrate the feasibility to overcome single-modality artifacts with perfectly co-registered images.
Description
December 2018
School of Engineering
Department
Dept. of Biomedical Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.