Real-time respiratory motion forecasting with online learning of recurrent neural networks for accurate targeting in externally guided radiotherapy.

Journal: Computer methods and programs in biomedicine
Published Date: May 20, 2025

Abstract

BACKGROUND AND OBJECTIVE: In lung radiotherapy, infrared cameras can track reflective objects on the chest to estimate tumor motion due to breathing. However, treatment system latencies hinder radiation beam precision. Real-time recurrent learning (RTRL), the conventional online learning approach for training recurrent neural networks (RNNs), is a potential solution that can learn patterns within non-stationary respiratory data but has high complexity. This research assesses the capabilities of resource-efficient online algorithms for RNNs-unbiased online recurrent optimization (UORO), sparse one-step approximation (SnAp-1), and decoupled neural interfaces (DNI)-to forecast respiratory motion during radiotherapy accurately.

Authors

  • Michel Pohl
    The University of Tokyo, Graduate School of Engineering, Department of Bioengineering, Tokyo, Japan. Electronic address: michel.pohl@centrale-marseille.fr.
  • Mitsuru Uesaka
    The University of Tokyo, Graduate School of Engineering, Department of Bioengineering, Tokyo, Japan; The University of Tokyo, Graduate School of Engineering, Department of Nuclear Engineering and Management, Tokyo, Japan.
  • Hiroyuki Takahashi
    Department of Pathology, Jikei University School of Medicine, Tokyo, Japan.
  • Kazuyuki Demachi
    The University of Tokyo, Graduate School of Engineering, Department of Nuclear Engineering and Management, Tokyo, Japan.
  • Ritu Bhusal Chhatkuli
    Research Center for Child Mental Development, Chiba University, Chiba, Japan.

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