r-UNet: Leaf Position Reconstruction in Upstream Radiotherapy Verification

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Abstract

Monolithic Active Pixel Sensor (MAPS) devices are an effective tool for upstream verification of Intensity Modulated Radiotherapy (IMRT) treatments. It is crucial to measure with high precision the positions of the multi leaf collimators (MLC) used to shape the beam in real time, in order to enhance the quality and safety of treatments. This work describes r-UNet, a deep learning based solution for leaf position reconstruction. The model is used to analyse the high-resolution images produced by a Lassena MAPS device in order to automatically determine the leaf positions. Image segmentation and leaf position estimation are performed simultaneously in a multi-task setting. r-UNet obtained an average Dice coefficient of 0.96 ± 0.03 for the reconstructed image masks in the held-out test set; whilst the mean squared error (MSE) resulting from the estimation of the MLC positions is 0.003 mm, with a resolution ranging between 45 and 53 μm for leaf extensions between 1 and 35 mm. On unseen leaf positions, r-UNet yielded a single-leaf resolution between 54 and 88 μm depending on the leaf extension, and an average MSE of 0.07 mm. These results were obtained using single frames of data collected at 34 frames per second.
Original languageEnglish
JournalIEEE Transactions on Radiation and Plasma Medical Sciences
Early online date15 May 2020
DOIs
Publication statusE-pub ahead of print - 15 May 2020

Keywords

  • radiotherapy
  • Multi Leaf Collimator (MLC)
  • Monolithic Active Pixel Sensors (MAPS)
  • deep learning
  • position reconstruction
  • image segmentation

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