A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN‐based image synthesis

Abstract Background Due to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR). Purpose This study developed a deep‐learning‐based approach to generate high‐quality synthetic kilovoltage computed tomography (skVCT) from MVCT and meet clinical dose requirements. Methods Data from 41 head and neck cancer patients were collected; 25 (2995 slices) were used for training, and 16 (1898 slices) for testing. A cycle generative adversarial network (cycleGAN) based on attention gate and residual blocks was used to generate MVCT‐based skVCT. For the 16 patients, kVCT‐based plans were transferred to skVCT images and electron density profile‐corrected MVCT images to recalculate the dose. The quantitative indices and clinically relevant dosimetric metrics, including the mean absolute error (MAE), structural similarity index measure (SSIM), peak signal‐to‐noise ratio (PSNR), gamma passing rates, and dose‐volume‐histogram (DVH) parameters ( D max , D mean , D min ), were used to assess the skVCT images. Results The MAE, PSNR, and SSIM of MVCT were 109.6 ± 12.3 HU, 27.5 ± 1.1 dB, and 91.9% ± 1.7%, respectively, while those of skVCT were 60.6 ± 9.0 HU, 34.0 ± 1.9 dB, and 96.5% ± 1.1%. The image quality and contrast were enhanced, and the noise was reduced. The gamma passing rates improved from 98.31% ± 1.11% to 99.71% ± 0.20% (2 mm/2%) and 99.77% ± 0.18% to 99.98% ± 0.02% (3 mm/3%). No significant differences ( p > 0.05) were observed in DVH parameters between kVCT and skVCT. Conclusion With training on a small data set (2995 slices), the model successfully generated skVCT with improved image quality, and the dose calculation accuracy was similar to that of MVCT. MVCT‐based skVCT can increase treatment accuracy and offer the possibility of implementing adaptive radiotherapy.