面向特征的深度学习框架：基于多任务定制感知损失的肺部锥形束CT（CBCT）增强
Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss
AI总结：
本文提出一种面向特征的深度学习框架，通过多任务定制特征到特征感知损失函数，将低质量肺部CBCT转换为高质量类CT图像，在伪影抑制和解剖细节保留上优于现有方法。
摘要：
锥形束计算机断层扫描（CBCT）在图像引导放射治疗（IGRT）期间被常规采集，用于为癌症治疗提供更新的患者解剖信息。然而，CBCT图像常常受到因欠采样投影和低剂量曝光引起的条纹伪影和噪声的影响，导致清晰度低和信息丢失。尽管近年来基于深度学习的CBCT增强方法在抑制伪影方面显示出有前景的结果，但它们在保留解剖细节方面性能有限，因为传统的像素到像素损失函数无法描述详细的解剖结构。为了解决这一问题，我们提出了一种新颖的面向特征的深度学习框架，该框架通过多任务定制的特征到特征感知损失函数，将低质量CBCT图像转换为高质量类CT成像。该框架包含两个主要组成部分：一个多任务学习特征选择网络（MTFS-Net），用于定制感知损失函数；以及一个由特征到特征感知损失引导的CBCT到CT转换网络，该网络使用了先进的生成模型，如U-Net、GAN和CycleGAN。我们的实验表明，所提出的框架能够为肺部生成合成CT（sCT）图像，这些图像与CT图像实现了高度相似性，平均SSIM指数为0.9869，平均PSNR指数为39.9621。sCT图像还实现了视觉上令人满意的性能，具有有效的伪影抑制、噪声降低和独特的解剖细节保留。我们的实验结果表明，所提出的框架在肺部CBCT增强方面优于最先进的模型。该框架在从CBCT生成适合各种临床应用的高质量解剖成像方面具有巨大前景。
英文摘要：
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.
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