Superfast Scan of Focused X-Ray Luminescence Computed Tomography Imaging.

X-ray luminescence computed tomography optical imaging optical tomography scanning schemes x-ray imaging

Journal

IEEE access : practical innovations, open solutions
ISSN: 2169-3536
Titre abrégé: IEEE Access
Pays: United States
ID NLM: 101639462

Informations de publication

Date de publication:
2023
Historique:
medline: 1 1 2023
pubmed: 1 1 2023
entrez: 26 6 2024
Statut: ppublish

Résumé

X-ray luminescence computed tomography (XLCT) is a hybrid molecular imaging modality having the high spatial resolution of x-ray imaging and high measurement sensitivity of optical imaging. Narrow x-ray beam based XLCT imaging has shown promise for high spatial resolution imaging of luminescent targets in deep tissues, but the slow acquisition speed limits its applications. In this work, we have introduced a superfast XLCT scan scheme based on the photon counter detector and a fly-scanning method. The new scan scheme is compared with three other scan methods. We have also designed and built a single-pixel x-ray detector to detect object boundaries automatically. With the detector, we can perform the parallel beam CT imaging with the XLCT imaging simultaneously. We have built the prototype XLCT imaging system to verify the proposed scan scheme. A phantom embedded with a set of four side-by-side cylindrical targets was scanned. With the proposed superfast scan scheme, we have achieved 43 seconds per transverse scan, which is 28.6 times faster than before with slightly better XLCT image quality. The superfast scan allows us to perform 3D pencil beam XLCT imaging in the future.

Identifiants

pubmed: 38919730
doi: 10.1109/access.2023.3336615
pmc: PMC11198969
doi:

Types de publication

Journal Article

Langues

eng

Pagination

134183-134190

Auteurs

Yile Fang (Y)

Department of Bioengineering, University of California, Merced, Merced, CA 95343, USA.

Yibing Zhang (Y)

Department of Bioengineering, University of California, Merced, Merced, CA 95343, USA.

Michael C Lun (MC)

Department of Bioengineering, University of California, Merced, Merced, CA 95343, USA.

Changqing Li (C)

Department of Electrical Engineering, University of California, Merced, Merced, CA 95343, USA.

Classifications MeSH