- Deep Learning derived Input Function in dynamic PET

About the technology

With DLIF we present a non-invasive, automatically generated input-function for kinetic modeling in dynamic PET. Our deep learning models are trained end-to-end with dynamic PET images as input and ready-to-use input function as output. The model show very strong correlation and non-significant difference in influx (Ki) for myocardium and tumor derived with AIF and DLIF, respectively.

Value proposition

  • No surgery requirements
  • Compatible with any PET scanner and the most widely used tracers
  • Provides results that are highly accurate with less bias and variation than state of the art

Areas of application

  • Non-invasive input function for dynamic PET where blood sampling is not possible
  • Preclinical or clinical applications
  • Could be integrated into external software independent of PET scanner vendor
  • As a part of the PET scanner-software to deliver the DLIF together with the PET images

Resources and partners

  • The Norwegian Nuclear Medicine Consortium 180°N
  • Tromsø Research Foundation
  • Center for Research-based Innovation (SFI) Visual Intelligence
  • Proof of concept validated for small scale research in mice

Comparison to state of the art technology

Image: Comparison model of DLIF and existing state of the art technology.

Opportunities for Collaboration

We are currently looking for industry partners who finds interest in our technology. We are open for working together on the development of DLIF to accommodate your needs.

Collaboration partners

Universitetet i Tromsø
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TFS_FARGE_NOR-HOR
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Contact information

Foto: Marius Fiskum

Ingrid Beard country

Business Developer | Tech Transfer

ingrid@norinnova.no
Tel. 991 57 143

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