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A multicentre and multi-national evaluation of the accuracy of quantitative Lu-177 SPECT/CT imaging performed within the MRTDosimetry project

Abstract:
Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1186/s40658-021-00397-0
Publication website:
https://opus.bibliothek.uni-augsburg.de/opus4/files/109729/109729.pdf

Authors

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Role:
Author
ORCID:
0000-0001-9552-7750
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Role:
Author
ORCID:
0000-0002-0008-1598
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Role:
Author
ORCID:
0000-0002-2510-1321


Publisher:
SpringerOpen
Journal:
EJNMMI Physics More from this journal
Volume:
8
Issue:
1
Pages:
55-55
Article number:
55
Publication date:
2021-07-23
DOI:
EISSN:
2197-7364
ISSN:
2197-7364


Language:
English
Keywords:
Pubs id:
1188031
Local pid:
pubs:1188031
Source identifiers:
W3186573596
Deposit date:
2026-03-25
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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