Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in low clarity and information loss. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details since conventional pixel-to-pixel loss functions are incapable of describing detailed anatomy. To address this issue, we propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging via a multi-task customized feature-to-feature perceptual loss function. The framework comprises two main components: a multi-task learning feature-selection network(MTFS-Net) for customizing the perceptual loss function; and a CBCT-to-CT translation network guided by feature-to-feature perceptual loss, which uses advanced generative models such as U-Net, GAN and CycleGAN. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9869 and an average PSNR index of 39.9621. The sCT images also achieved visually pleasing performance with effective artifacts suppression, noise reduction, and distinctive anatomical details preservation. Our experiment results indicate that the proposed framework outperforms the state-of-the-art models for pulmonary CBCT enhancement. This framework holds great promise for generating high-quality anatomical imaging from CBCT that is suitable for various clinical applications.

翻译：锥束计算机断层扫描（CBCT）在图像引导放射治疗（IGRT）中常规采集，为癌症治疗提供更新的患者解剖信息。然而，由于欠采样投影和低剂量曝光导致的条状伪影和噪声，CBCT图像常存在清晰度不足和信息损失的问题。尽管近期基于深度学习的CBCT增强方法在抑制伪影方面取得了显著成果，但由于传统逐像素损失函数无法描述精细解剖结构，其在保留解剖细节方面性能有限。为解决该问题，我们提出了一种新颖的面向特征深度学习框架，通过多任务定制化特征级感知损失函数将低质量CBCT图像转化为高质量类CT影像。该框架包含两大核心组件：用于定制感知损失函数的多任务学习特征选择网络（MTFS-Net）；以及由特征级感知损失引导的CBCT-to-CT转换网络，该网络采用U-Net、GAN和CycleGAN等先进生成模型。实验表明，所提框架生成的肺部合成CT（sCT）图像与CT图像具有高度相似性，平均结构相似性指数（SSIM）达0.9869，平均峰值信噪比（PSNR）达39.9621。sCT图像在有效抑制伪影、降低噪声的同时，实现了卓越的视觉表现与解剖细节保留。实验结果表明，所提框架在肺部CBCT增强任务中优于现有最优模型，为基于CBCT生成适用于多种临床场景的高质量解剖成像开辟了新途径。