Assessment of the axial resolution of a compact gamma camera with coded aperture collimator.

Axial resolution Coded aperture Compact gamma camera Image reconstruction Intraoperative imaging Radioguided surgery Timepix3

Journal

EJNMMI physics
ISSN: 2197-7364
Titre abrégé: EJNMMI Phys
Pays: Germany
ID NLM: 101658952

Informations de publication

Date de publication:
21 Mar 2024
Historique:
received: 04 09 2023
accepted: 06 03 2024
medline: 21 3 2024
pubmed: 21 3 2024
entrez: 21 3 2024
Statut: epublish

Résumé

Handheld gamma cameras with coded aperture collimators are under investigation for intraoperative imaging in nuclear medicine. Coded apertures are a promising collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolution and computational performance of two reconstruction methods. An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holes of 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12-100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likelihood expectation maximization algorithm (3D-MLEM). While taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively. Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial resolution but was computationally much slower than MURA Decoding, whose reconstruction time is compatible with real-time imaging.

Identifiants

pubmed: 38509411
doi: 10.1186/s40658-024-00631-5
pii: 10.1186/s40658-024-00631-5
doi:

Types de publication

Journal Article

Langues

eng

Pagination

30

Subventions

Organisme : Zentrales Innovationsprogramm Mittelstand (ZIM)
ID : KK5044701BS0

Informations de copyright

© 2024. The Author(s).

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Auteurs

Tobias Meißner (T)

Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. publications@ibt.kit.edu.
Mannheim Institute for Intelligent Systems in Medicine (MIISM), Heidelberg University, Mannheim, Germany. publications@ibt.kit.edu.

Laura Antonia Cerbone (LA)

Scuola Superiore Meridionale, Naples, Italy.
INFN Sezione di Napoli, Istituto Nazionale di Fisica Nucleare, Naples, Italy.

Paolo Russo (P)

INFN Sezione di Napoli, Istituto Nazionale di Fisica Nucleare, Naples, Italy.
Dipartimento di Fisica "Ettore Pancini", Universitá di Napoli Federico II, Naples, Italy.

Werner Nahm (W)

Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

Jürgen Hesser (J)

Mannheim Institute for Intelligent Systems in Medicine (MIISM), Heidelberg University, Mannheim, Germany.
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
Central Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany.
CZS Heidelberg Center for Model-Based AI, Heidelberg University, Heidelberg, Germany.

Classifications MeSH