Zhang Tiande, Pang Haowen, Wu Yanan, Xu Jiaxuan, Liang Zhenyu, Xia Shuyue, Jin Chenwang, Chen Rongchang, 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.
Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03322-0.
Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease with various phenotypes. Registered inspiratory and expiratory CT images can generate the parametric response map (PRM) that characterizes phenotypes' spatial distribution and proportions. However, increased radiation dosage, scan time, quality control, and patient cooperation requirements limit the utility of PRM. This study aims to synthesize a PRM using only inspiratory CT scans. First, a CycleGAN with perceptual loss and a multiscale discriminator (MPCycleGAN) is proposed and trained to synthesize registered expiratory CT images from inspiratory images. Next, a strategy named InspirationOnly is introduced, where synthesized images replace actual expiratory CT images. The image synthesizer outperformed state-of-the-art models, achieving a mean absolute error of 105.66 ± 36.64 HU, a peak signal-to-noise ratio of 21.43 ± 1.87 dB, and a structural similarity of 0.84 ± 0.02. The intraclass correlation coefficients of emphysema, fSAD, and normal proportions between the InspirationOnly and ground truth were 0.995, 0.829, and 0.914, respectively. The proposed MPCycleGAN enables the InspirationOnly strategy to yield PRM using only inspiratory CT. The estimated COPD phenotypes are consistent with those from dual-phase CT and correlated with the spirometry parameters. This offers a potential tool for characterizing phenotypes of COPD, particularly when expiratory CT images are unavailable.
慢性阻塞性肺疾病(COPD)是一种具有多种表型的高度异质性疾病。已配准的吸气和呼气CT图像可以生成参数响应图(PRM),该图可表征表型的空间分布和比例。然而,辐射剂量增加、扫描时间、质量控制以及对患者配合的要求限制了PRM的实用性。本研究旨在仅使用吸气CT扫描来合成PRM。首先,提出并训练了一种带有感知损失和多尺度鉴别器的循环生成对抗网络(MPCycleGAN),以从吸气图像合成已配准的呼气CT图像。接下来,引入了一种名为“InspirationOnly”的策略,即合成图像取代实际的呼气CT图像。该图像合成器的性能优于现有模型,平均绝对误差为105.66±36.64HU,峰值信噪比为21.43±1.87dB,结构相似性为0.84±0.02。“InspirationOnly”与真实情况之间的肺气肿、fSAD和正常比例的组内相关系数分别为0.995、0.829和0.914。所提出的MPCycleGAN使“InspirationOnly”策略能够仅使用吸气CT生成PRM。估计的COPD表型与双期CT的表型一致,并且与肺功能参数相关。这为表征COPD的表型提供了一种潜在工具,特别是在没有呼气CT图像的情况下。