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Cone-beam computed tomography noise reduction method based on U-Net with convolutional block attention module in proton therapy

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摘要: Cone-beam computed tomography (CBCT) is mostly used for position verification during the treatment pro#2;
cess. However, severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation
therapy re-planning for proton therapy. In this study, an improved U-Net neural network named CBAM-U-Net
was proposed for CBCT noise reduction in proton therapy, which is a CBCT denoised U-Net network with con#2;
volutional block attention modules. The datasets contained 20 groups of head and neck images. The CT images
were registered to CBCT images as ground truth. The original CBCT denoised U-Net network, sCTU-Net, was
trained for model performance comparison. The synthetic CT(SCT) images generated by CBAM-U-Net and the
original sCTU-Net are called CBAM-SCT and U-Net-SCT images, respectively. The HU accuracies of the CT,
CBCT, and SCT images were compared using four metrics: mean absolute error (MAE), root mean square error
(RMSE), peak signal-to-noise ratio (PSNR), and structure similarity index measure (SSIM). The mean values of
the MAE, RMSE, PSNR, and SSIM of CBAM-SCT images were 23.80 HU, 64.63 HU, 52.27 dB, and 0.9919,
respectively, which were superior to those of the U-Net-SCT images. To evaluate dosimetric accuracy, the range
accuracy was compared for a single-energy proton beam. The γ-index pass rates of a 4 cm × 4 cm scanned
field and simple plan were calculated to compare the effects of the noise reduction capabilities of the original
U-Net and CBAM-U-Net on the dose calculation results. CBAM-U-Net reduced noise more effectively than
sCTU-Net, particularly in high-density tissues. We proposed a CBAM-U-Net model for CBCT noise reduction
in proton therapy. Owing to the excellent noise reduction capabilities of CBAM-U-Net, the proposed model
provided relatively explicit information regarding patient tissues. Moreover, it can be used in dose calculation
and adaptive treatment planning in the future.

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[V1] 2024-05-30 23:03:36 ChinaXiv:202405.00332V1 下载全文
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