Image Quality Assessment of Deep Learning-Based Virtual Monoenergetic Images From Single-Energy CT Pulmonary Angiography
A measure of 40 keV DL-VME derived from SECT effectively enhances both vessel opacification and image quality in CT pulmonary angiography. The image quality advantage of DL-VME over SECT remains robust across variations in data acquisition and patient variables.
Authors
- John W. Garrett (ORCID: https://orcid.org/0000-0002-8152-736X)
- Thomas M. Grist (ORCID: https://orcid.org/0000-0003-3279-6931)
- Yijing Wu (ORCID: https://orcid.org/0000-0002-7669-6919)
- Prashant Nagpal (ORCID: https://orcid.org/0000-0003-2347-034X)
- Guang‐Hong Chen (ORCID: https://orcid.org/0000-0002-2263-1028)
- Brian F. Mullan (ORCID: https://orcid.org/0000-0003-1656-6335)
- Ke Li (ORCID: https://orcid.org/0000-0002-2672-2672)
- Ran Zhang (ORCID: https://orcid.org/0000-0001-7643-3935)
- Zhihua Qi (ORCID: https://orcid.org/0000-0002-3541-5391)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- University of Wisconsin–Madison (US)
- Henry Ford Health System (US)
Publication Details
- Journal
- Journal of Computer Assisted Tomography
- Published
- 2025-12-12
- DOI
- https://doi.org/10.1097/rct.0000000000001812
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00