Chengdu Computer Application Institute Chinese Academy of Sciences, China; University of the Chinese Academy of Sciences, China.
Radiophysical Technology Center, Cancer Center, West China Hospital, Sichuan University, China.
Comput Med Imaging Graph. 2024 Apr;113:102344. doi: 10.1016/j.compmedimag.2024.102344. Epub 2024 Feb 2.
Cone Beam Computed Tomography (CBCT) plays a crucial role in Image-Guided Radiation Therapy (IGRT), providing essential assurance of accuracy in radiation treatment by monitoring changes in anatomical structures during the treatment process. However, CBCT images often face interference from scatter noise and artifacts, posing a significant challenge when relying solely on CBCT for precise dose calculation and accurate tissue localization. There is an urgent need to enhance the quality of CBCT images, enabling a more practical application in IGRT. This study introduces EGDiff, a novel framework based on the diffusion model, designed to address the challenges posed by scatter noise and artifacts in CBCT images. In our approach, we employ a forward diffusion process by adding Gaussian noise to CT images, followed by a reverse denoising process using ResUNet with an attention mechanism to predict noise intensity, ultimately synthesizing CBCT-to-CT images. Additionally, we design an energy-guided function to retain domain-independent features and discard domain-specific features during the denoising process, enhancing the effectiveness of CBCT-CT generation. We conduct numerous experiments on the thorax dataset and pancreas dataset. The results demonstrate that EGDiff performs better on the thoracic tumor dataset with SSIM of 0.850, MAE of 26.87 HU, PSNR of 19.83 dB, and NCC of 0.874. EGDiff outperforms SoTA CBCT-to-CT synthesis methods on the pancreas dataset with SSIM of 0.754, MAE of 32.19 HU, PSNR of 19.35 dB, and NCC of 0.846. By improving the accuracy and reliability of CBCT images, EGDiff can enhance the precision of radiation therapy, minimize radiation exposure to healthy tissues, and ultimately contribute to more effective and personalized cancer treatment strategies.
锥形束计算机断层扫描（CBCT）在图像引导放射治疗（IGRT）中发挥着至关重要的作用，通过监测治疗过程中解剖结构的变化，为放射治疗的准确性提供了重要保障。然而，CBCT 图像常常受到散射噪声和伪影的干扰，这使得仅依靠 CBCT 进行精确的剂量计算和准确的组织定位变得极具挑战性。因此，迫切需要提高 CBCT 图像的质量，使其在 IGRT 中得到更实际的应用。本研究介绍了一种基于扩散模型的新型框架 EGDiff，旨在解决 CBCT 图像中散射噪声和伪影带来的挑战。在我们的方法中，我们通过向 CT 图像添加高斯噪声来进行正向扩散过程，然后使用具有注意力机制的 ResUNet 进行反向去噪过程，以预测噪声强度，最终合成 CBCT-to-CT 图像。此外，我们设计了一种能量引导函数，在去噪过程中保留域独立特征并丢弃域特定特征，从而提高 CBCT-CT 生成的有效性。我们在胸部数据集和胰腺数据集上进行了大量实验。结果表明，EGDiff 在胸部肿瘤数据集上的表现更好，其 SSIM 为 0.850，MAE 为 26.87 HU，PSNR 为 19.83 dB，NCC 为 0.874。在胰腺数据集上，EGDiff 优于最先进的 CBCT-to-CT 合成方法，其 SSIM 为 0.754，MAE 为 32.19 HU，PSNR 为 19.35 dB，NCC 为 0.846。通过提高 CBCT 图像的准确性和可靠性，EGDiff 可以提高放射治疗的精度，最大限度地减少对健康组织的辐射暴露，最终有助于制定更有效和个性化的癌症治疗策略。
