A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT Radiotherapy Planning
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Volumetric Modulated Arc Therapy (VMAT) revolutionizes cancer treatment by
precisely delivering radiation while sparing healthy tissues. Fluence maps
generation, crucial in VMAT planning, traditionally involves complex and
iterative, and thus time consuming processes. These fluence maps are
subsequently leveraged for leaf-sequence. The deep-learning approach presented
in this article aims to expedite this by directly predicting fluence maps from
patient data. We developed a 3D network which we trained in a supervised way
using a combination of L1 and L2 losses, and RT plans generated by Eclipse and
from the REQUITE dataset, taking the RT dose map as input and the fluence maps
computed from the corresponding RT plans as target. Our network predicts
jointly the 180 fluence maps corresponding to the 180 control points (CP) of
single arc VMAT plans. In order to help the network, we pre-process the input
dose by computing the projections of the 3D dose map to the beam's eye view
(BEV) of the 180 CPs, in the same coordinate system as the fluence maps. We
generated over 2000 VMAT plans using Eclipse to scale up the dataset size.
Additionally, we evaluated various network architectures and analyzed the
impact of increasing the dataset size. We are measuring the performance in the
2D fluence maps domain using image metrics (PSNR, SSIM), as well as in the 3D
dose domain using the dose-volume histogram (DVH) on a validation dataset. The
network inference, which does not include the data loading and processing, is
less than 20ms. Using our proposed 3D network architecture as well as
increasing the dataset size using Eclipse improved the fluence map
reconstruction performance by approximately 8 dB in PSNR compared to a U-Net
architecture trained on the original REQUITE dataset. The resulting DVHs are
very close to the one of the input target dose.
