Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy

Phys Med Biol. 2020 Dec 4;65(24):10.1088/1361-6560/ab9fcb. doi: 10.1088/1361-6560/ab9fcb.


Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds.

Keywords: CBCT; U-Net; adaptive radiotherapy; deep learning; proton therapy; scatter correction.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cone-Beam Computed Tomography / methods
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Neural Networks, Computer
  • Phantoms, Imaging
  • Proton Therapy*
  • Scattering, Radiation
  • Spiral Cone-Beam Computed Tomography*