Zhang Tiande, Pang Haowen, Wu Yanan, Xu Jiaxuan, Liu Lingkai, Li Shang, Xia Shuyue, Chen Rongchang, Liang Zhenyu, Qi Shouliang
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Comput Methods Programs Biomed. 2025 Feb;259:108516. doi: 10.1016/j.cmpb.2024.108516. Epub 2024 Nov 14.
Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.
To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps.
BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD.
BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available.
慢性阻塞性肺疾病（COPD）在病因和临床表现方面具有高度异质性。呼气计算机断层扫描（CT）能够有效评估气体潴留，有助于疾病诊断。然而，出于对辐射暴露和成本的担忧，呼气CT并未常规开展。近期关于合成呼气CT的工作主要集中在成像特征上，而忽略了患者特异性肺功能。
为解决这些问题，我们开发了一种名为BreathVisionNet的新型模型，该模型纳入肺功能数据以指导从吸气CT合成呼气CT。引入了一种结合卷积神经网络和Transformer的架构，以利用COPD患者中不规则的表型分布。该模型通过将全局信息纳入编码器，能够更好地理解长距离和全局背景。边缘信息和多视图数据的利用进一步提高了合成CT的质量。通过使用合成的呼气CT和吸气CT可以估计参数响应映射（PRM），以量化正常、肺气肿和功能性小气道疾病（fSAD）的COPD表型，包括它们的百分比、空间分布和体素分布图。
BreathVisionNet在合成图像质量方面优于其他生成模型。它分别实现了平均绝对误差、归一化均方误差、结构相似性指数和峰值信噪比为78.207 HU、0.643、0.847和25.828 dB。比较预测的和真实的PRM，Dice系数可达0.732（肺气肿）和0.560（fSAD）。对于开发数据集（低辐射剂量CT扫描），真实和预测的fSAD百分比之间的差异均值为4.42；对于独立的外部验证数据集（常规剂量），差异均值为9.05，表明该模型具有很强的通用性。在体素分布图上训练的分类器在预测COPD存在时的准确率可达0.891。
BreathVisionNet能够从吸气CT准确合成呼气CT图像并预测其体素分布。估计的PRM有助于量化正常、肺气肿和fSAD的COPD表型。这种能力在仅有吸气CT图像可用时，为COPD的多样性提供了更多见解。
