Feasibility of CycleGAN enhanced low dose CBCT imaging for prostate radiotherapy dose calculation

Author:

Chan YORCID,Li M,Parodi K,Belka C,Landry GORCID,Kurz C

Abstract

Abstract Daily cone beam computed tomography (CBCT) imaging during the course of fractionated radiotherapy treatment can enable online adaptive radiotherapy but also expose patients to a non-negligible amount of radiation dose. This work investigates the feasibility of low dose CBCT imaging capable of enabling accurate prostate radiotherapy dose calculation with only 25% projections by overcoming under-sampling artifacts and correcting CT numbers by employing cycle-consistent generative adversarial networks (cycleGAN). Uncorrected CBCTs of 41 prostate cancer patients, acquired with ∼350 projections (CBCTorg), were retrospectively under-sampled to 25% dose images (CBCTLD) with only ∼90 projections and reconstructed using Feldkamp–Davis–Kress. We adapted a cycleGAN including shape loss to translate CBCTLD into planning CT (pCT) equivalent images (CBCTLD_GAN). An alternative cycleGAN with a generator residual connection was implemented to improve anatomical fidelity (CBCTLD_ResGAN). Unpaired 4-fold cross-validation (33 patients) was performed to allow using the median of 4 models as output. Deformable image registration was used to generate virtual CTs (vCT) for Hounsfield units (HU) accuracy evaluation on 8 additional test patients. Volumetric modulated arc therapy plans were optimized on vCT, and recalculated on CBCTLD_GAN and CBCTLD_ResGAN to determine dose calculation accuracy. CBCTLD_GAN, CBCTLD_ResGAN and CBCTorg were registered to pCT and residual shifts were analyzed. Bladder and rectum were manually contoured on CBCTLD_GAN, CBCTLD_ResGAN and CBCTorg and compared in terms of Dice similarity coefficient (DSC), average and 95th percentile Hausdorff distance (HDavg, HD95). The mean absolute error decreased from 126 HU for CBCTLD to 55 HU for CBCTLD_GAN and 44 HU for CBCTLD_ResGAN. For PTV, the median differences of D 98%, D 50% and D 2% comparing both CBCTLD_GAN to vCT were 0.3%, 0.3%, 0.3%, and comparing CBCTLD_ResGAN to vCT were 0.4%, 0.3% and 0.4%. Dose accuracy was high with both 2% dose difference pass rates of 99% (10% dose threshold). Compared to the CBCTorg-to-pCT registration, the majority of mean absolute differences of rigid transformation parameters were less than 0.20 mm/0.20°. For bladder and rectum, the DSC were 0.88 and 0.77 for CBCTLD_GAN and 0.92 and 0.87 for CBCTLD_ResGAN compared to CBCTorg, and HDavg were 1.34 mm and 1.93 mm for CBCTLD_GAN, and 0.90 mm and 1.05 mm for CBCTLD_ResGAN. The computational time was ∼2 s per patient. This study investigated the feasibility of adapting two cycleGAN models to simultaneously remove under-sampling artifacts and correct image intensities of 25% dose CBCT images. High accuracy on dose calculation, HU and patient alignment were achieved. CBCTLD_ResGAN achieved better anatomical fidelity.

Funder

Research Training Group GRK 2274

Deutsche Forschungsgemeinschaft

Publisher

IOP Publishing

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

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1. Artificial intelligence for treatment delivery: image-guided radiotherapy;Strahlentherapie und Onkologie;2024-08-13

2. Cone-beam computed tomography noise reduction method based on U-Net with convolutional block attention module in proton therapy;Nuclear Science and Techniques;2024-07

3. Generative AI Revolution;Advances in Medical Technologies and Clinical Practice;2024-06-14

4. Low Dose CBCT Denoising Using a 3D U-Net;2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2024-04-14

5. Minimum imaging dose for deep learning-based pelvic synthetic computed tomography generation from cone beam images;Physics and Imaging in Radiation Oncology;2024-04

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