Miederer, Isabelle; Shi, Kuangyu; Wendler, Thomas (2023). Machine learning methods for tracer kinetic modelling. Nuklearmedizin, 62(6), pp. 370-378. Thieme 10.1055/a-2179-5818
Full text not available from this repository. (Request a copy)Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation in clinical routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies while reducing processing time. Moreover, it can help improving kinetic modelling data used in downstream tasks such as tumor detection. In this review, we introduce the basics of tracer kinetic modelling and present a literature review of original works and conference papers using machine learning methods in this field.
Item Type: |
Journal Article (Review Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine |
UniBE Contributor: |
Shi, Kuangyu |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2567-6407 |
Publisher: |
Thieme |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
12 Oct 2023 12:53 |
Last Modified: |
25 Mar 2024 09:37 |
Publisher DOI: |
10.1055/a-2179-5818 |
PubMed ID: |
37820696 |
URI: |
https://boris.unibe.ch/id/eprint/187126 |
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