ArticleZeitschrift fur medizinische Physik2024
Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.
Article in Zeitschrift fur medizinische Physik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Cognitive Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome-Aetiology and Potential Treatments.International journal of molecular sciences · 2025Review
- Artificial intelligence in medical physics.Zeitschrift fur medizinische Physik · 2024Article
- A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke.Frontiers in neurology · 2024Review
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNeuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, the gold standard TSPO PET quantification method includes a 90 min scan and continuous arterial blood sampling, which is challenging to perform on a routine basis. In this work, we determine what information is required for a simplified quantification approach using a machine learning algorithm. MATERIALS AND
methodsWe analyzed data from 18 patients with ischemic stroke who received 0-90 min [
resultsWhen using all the input features, the algorithm predicted V
conclusionReliable TSPO PET quantification is achievable by using a single late PET frame divided by a late blood sample activity concentration.
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