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Image-guided radiation therapy (IGRT) is an important technological advancement that has significantly contributed to the accuracy of radiation oncology treatment plan delivery in the last decade. However, the current standard IGRT technique of linac-mounted kilovoltage (kV) cone-beam Computed Tomography (CBCT) has limited soft tissue contrast and is prone to image artifacts, which detract from its clinical utility. It is even worse in chest CBCT compared to other anatomic sites due to respiratory motion, which could lead to mistreatment. Therefore, it is highly desirable to improve CBCT image quality to the level of a planning CT scan. In this study, we propose a novel deep learning-based method, which integrates histogram matching (HM) into a cycle-consistent adversarial network (CycleGAN) framework called HM-CycleGAN, to learn a mapping between chest CBCT images and paired planning CT images obtained at simulation. Histogram matching is performed via an informative maximizing (MaxInfo) loss calculated between planning CT and the synthetic CT (sCT) derived by feeding CBCT into the HM-CycleGAN. The proposed algorithm was evaluated using 15 sets of patient chest CBCT data, each of which has 3-5 daily CBCTs. The planning/simulation CT was used as ground truth for sCTs derived from CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC were 63.2 HU, 30.2 dB, 0.96 over all CBCT fractions. The proposed method showed superior image quality, reduced noise, and artifact severity compared to the scatter correction method. Upon further improvement and clinical assessment, this method could further enhance accuracy of the current IGRT technique. The CBCT-based synthetic CT could be the critical component to achieve online adaptive radiation therapy.
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