Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registration-free method for predicting local tissue expansion. We evaluated model performance across varying degrees of disease severity and compared its performance with two image-to-image translation frameworks - UNet and Pix2Pix. Our model achieved an overall PSNR of 18.95 decibels, SSIM of 0.840, and Spearman's correlation of 0.61 at a high spatial resolution of 1 mm3.

翻译：肺部局部组织扩张通常通过登记在多个肺部量中获得的计算断层扫描(CT)而产生,然而,获得多重扫描导致辐射剂量、时间和成本增加,在许多情况下可能无法,从而限制基于登记的生物机能的适用性。我们提议采用基因对抗学习方法,直接从单一CT扫描中估算当地组织扩张情况。拟议框架经过培训,对SPIROMIC组群2500个课题进行了评价。经过培训,该框架可用作预测地方组织扩张情况的一种不登记的方法。我们评估了不同程度疾病严重程度的模型性能,并将其与两个图像到图像翻译框架(UNet和Pix2Pix)进行了比较。我们的模式实现了18.95个图象贝、0.840的SSIM和1毫米高空间分辨率的Spearman0.61的关联性总体PSISNR,共达18.95个分贝贝尔、0.840的SSIM和0.61个高空间分辨率为1毫米的Spearman。