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Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice.

Samuel Kuttner1,2,3, Luigi T Luppino2, Laurence Convert4

  • 1The PET Imaging Center, University Hospital of North Norway, Tromsø, Norway.

Frontiers in Nuclear Medicine
|October 9, 2024
PubMed
Summary
This summary is machine-generated.

A novel deep learning model (DLIF) non-invasively predicts input functions for dynamic PET imaging in mice. This method eliminates the need for invasive arterial blood sampling, enabling longitudinal studies in small animal research.

Keywords:
Patlak analysisarterial input function estimationdeep learningdynamic positron emission tomography (PET)glucose metabolismprediction modelsmall-animal PET 18F-FDG PET/CT

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Area of Science:

  • Nuclear medicine
  • Biomedical imaging
  • Machine learning in medical research

Background:

  • Dynamic positron emission tomography (PET) and kinetic modeling are crucial for small animal tracer development.
  • Accurate input function determination via arterial blood sampling is essential but invasive and terminal in mice, precluding longitudinal studies.

Purpose of the Study:

  • To develop and validate a non-invasive, deep-learning-based prediction model (DLIF) for estimating the input function directly from PET data.
  • To enable quantitative and longitudinal PET imaging studies in mice without arterial cannulation.

Main Methods:

  • A deep learning model (DLIF) was trained and cross-validated on 68 [18F]Fluorodeoxyglucose mouse PET scans using image-derived targets.
  • The trained DLIF model was evaluated on an external dataset of 8 mouse scans with continuous arterial blood sampling for input function measurement.

Main Results:

  • The DLIF model accurately predicted input functions comparable to image-derived targets.
  • Patlak modeling using DLIF-derived input functions showed strong correlation with results from image-derived input functions.
  • Slightly larger discrepancies were observed on the external dataset, potentially due to experimental setup variations.

Conclusions:

  • The non-invasive DLIF prediction method offers a viable alternative to arterial blood sampling for small animal [18F]FDG PET imaging.
  • Further validation could establish DLIF as a tool for fully quantitative and longitudinal PET studies in mice, overcoming surgical limitations.