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Gamma Source Location Learning from Synthetic Multi-pinhole Collimator Data

  • Peter A. von NiederhäusernEmail author
  • Carlo Seppi
  • Simon Pezold
  • Guillaume Nicolas
  • Spyridon Gkoumas
  • Stephan K. Haerle
  • Philippe C. Cattin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)

Abstract

Sentinel lymph node biopsy (SNB) is a surgical method to stage certain cancer types in a minimally invasive manner. However, the current sensing methods for SNB are limited in accuracy, as they are based on acoustic feedback radiation probes to detect tracer enriched sentinel lymph nodes. We present a deep neural network approach to learn the latent spatial activity distributions from a simulated gamma source on 2D activity images. Data processing can then be applied for multi-pinhole collimator optimization, lymph node visualization or surgical navigation to further support SNB. Using simulations of photon multi-pinhole collimator interaction, we generate labeled synthetic 2D activity images to train convolutional neural networks (CNN). These CNNs are then evaluated on synthetic as well as on real experimental data from a radioactive point-like source, collected by our own stationary small form factor multi-pinhole collimator. We achieve good results on synthetic data for the xy-component ensemble learners with a localization class accuracy of 0.97, while depth estimation achieves a localization class accuracy of 0.55. Accuracy on real experimental data is limited due to the small sample set and its variability, compared to the simulation.

Keywords

Sentinel lymph node biopsy Radioguided surgery Inverse problem Machine learning Convolutional neural network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peter A. von Niederhäusern
    • 1
    Email author
  • Carlo Seppi
    • 1
  • Simon Pezold
    • 1
  • Guillaume Nicolas
    • 2
  • Spyridon Gkoumas
    • 3
  • Stephan K. Haerle
    • 4
  • Philippe C. Cattin
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of BaselAllschwilSwitzerland
  2. 2.University Hospital of Basel, Radiology and Nuclear Medicine ClinicBaselSwitzerland
  3. 3.DECTRIS Ltd.Baden-DättwilSwitzerland
  4. 4.Center for Head and Neck Surgical Oncology and Reconstructive Surgery, Hirslanden ClinicLucerneSwitzerland

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