Intrinsic Respiratory Gating for Simultaneous Multi-Mouse μCT Imaging to Assess Liver Tumors.

hepatocellular carcinoma high through put iterative reconstruction liver tumor mice respiratory gating small animal μCT

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2022
Historique:
received: 18 02 2022
accepted: 08 06 2022
entrez: 25 7 2022
pubmed: 26 7 2022
medline: 26 7 2022
Statut: epublish

Résumé

Small animal micro computed tomography (μCT) is an important tool in cancer research and is used to quantify liver and lung tumors. A type of cancer that is intensively investigated with μCT is hepatocellular carcinoma (HCC). μCT scans acquire projections from different angles of the gantry which rotates X-ray source and detector around the animal. Motion of the animal causes inconsistencies between the projections which lead to artifacts in the resulting image. This is problematic in HCC research, where respiratory motion affects the image quality by causing hypodense intensity at the liver edge and smearing out small structures such as tumors. Dealing with respiratory motion is particularly difficult in a high throughput setting when multiple mice are scanned together and projection removal by retrospective respiratory gating may compromise image quality and dose efficiency. In mice, inhalation anesthesia leads to a regular respiration with short gasps and long phases of negligible motion. Using this effect and an iterative reconstruction which can cope with missing angles, we discard the relatively few projections in which the gasping motion occurs. Moreover, since gated acquisition, i.e., acquiring multiple projections from a single gantry angle is not a requirement, this method can be applied to existing scans. We applied our method in a high throughput setting in which four mice with HCC tumors were scanned simultaneously in a multi-mouse bed. To establish a ground truth, we manually selected projections with visible respiratory motion. Our automated intrinsic breathing projection selection achieved an accordance of 97% with manual selection. We reconstructed volumetric images and demonstrated that our intrinsic gating method significantly reduces the hypodense depiction at the cranial liver edge and improves the detectability of small tumors. Furthermore, we show that projection removal in a four mice scan discards only 7.5% more projections than in a single-mouse setting, i.e., four mouse scanning does not substantially compromise dose efficiency or image quality. To the best of our knowledge, no comparable method that combines multi-mouse scans for high throughput, intrinsic respiratory gating, and an available iterative reconstruction has been described for liver tumor imaging before.

Identifiants

pubmed: 35872758
doi: 10.3389/fmed.2022.878966
pmc: PMC9299429
doi:

Types de publication

Journal Article

Langues

eng

Pagination

878966

Informations de copyright

Copyright © 2022 Thamm, Rosenhain, Leonardic, Höfter, Kiessling, Osl, Pöschinger and Gremse.

Déclaration de conflit d'intérêts

FG is the owner of Gremse-IT GmbH, a spin-out of the RWTH Aachen University, which commercializes software for biomedical image analysis. TP and FO are employees of Roche Diagnostics GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Mirko Thamm (M)

Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.
Gremse-IT GmbH, Aachen, Germany.

Stefanie Rosenhain (S)

Gremse-IT GmbH, Aachen, Germany.

Kevin Leonardic (K)

Gremse-IT GmbH, Aachen, Germany.

Andreas Höfter (A)

Gremse-IT GmbH, Aachen, Germany.

Fabian Kiessling (F)

Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.

Franz Osl (F)

Discovery Pharmacology, Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany.

Thomas Pöschinger (T)

Discovery Pharmacology, Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany.

Felix Gremse (F)

Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.
Gremse-IT GmbH, Aachen, Germany.

Classifications MeSH