Department of Mechanical Engineering & Centre for Bioengineering, University of Canterbury, New Zealand.
Intensive Care Unit, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, People's Republic of China.
Biomed Phys Eng Express. 2024 Nov 6;11(1). doi: 10.1088/2057-1976/ad8c47.
Creating multi-level digital-twin models for mechanical ventilation requires a detailed estimation of regional lung volume. An accurate generic map between 2D chest surface motion and 3D regional lung volume could provide improved regionalisation and clinically acceptable estimates localising lung damage. This work investigates the relationship between CT lung volumes and the forced vital capacity (FVC) a surrogate of tidal volume proven linked to 2D chest motion. In particular, a convolutional neural network (CNN) with U-Net architecture is employed to build a lung segmentation model using a benchmark CT scan dataset. An automated thresholding method is proposed for image morphology analysis to improve model performance. Finally, the trained model is applied to an independent CT dataset with FVC measurements for correlation analysis of CT lung volume projection to lung recruitment capacity. Model training results show a clear improvement of lung segmentation performance with the proposed automated thresholding method compared to a typically suggested fixed value selection, achieving accuracy greater than 95% for both training and independent validation sets. The correlation analysis for 160 patients shows a good correlation ofsquared value of 0.73 between the proposed 2D volume projection and the FVC value, which indicates a larger and denser projection of lung volume relative to a greater FVC value and lung recruitable capacity. The overall results thus validate the potential of using non-contact, non-invasive 2D measures to enable regionalising lung mechanics models to equivalent 3D models with a generic map based on the good correlation. The clinical impact of improved lung mechanics digital twins due to regionalising the lung mechanics and volume to specific lung regions could be very high in managing mechanical ventilation and diagnosing or locating lung injury or dysfunction based on regular monitoring instead of intermittent and invasive lung imaging modalities.
创建用于机械通气的多层数字孪生模型需要对区域性肺容积进行详细估计。二维胸部表面运动和三维区域性肺容积之间的准确通用映射可以提供更好的区域化和临床可接受的估计，从而定位肺损伤。这项工作研究了 CT 肺容积与用力肺活量（FVC）之间的关系，FVC 是与 2D 胸部运动相关的潮气量的替代指标。特别是，采用具有 U-Net 架构的卷积神经网络（CNN），使用基准 CT 扫描数据集构建肺分割模型。提出了一种自动阈值处理方法用于图像形态分析，以提高模型性能。最后，将训练好的模型应用于具有 FVC 测量值的独立 CT 数据集，以分析 CT 肺容积投影到肺复张能力的相关性。模型训练结果表明，与典型的固定值选择相比，所提出的自动阈值处理方法明显改善了肺分割性能，在训练和独立验证集上的准确率均大于 95%。对 160 名患者的相关性分析表明，所提出的 2D 体积投影与 FVC 值之间的平方值具有很好的相关性，这表明与较大的 FVC 值和肺可复张能力相比，肺容积的投影更大且更密集。因此，总体结果验证了使用非接触式、非侵入性的 2D 测量来实现基于良好相关性的等效 3D 模型的区域化肺力学模型的潜力。通过对肺力学和体积进行区域化，实现对特定肺区的肺力学数字孪生的改进，可能会对管理机械通气和基于常规监测而不是间歇性和侵入性的肺部成像方式来诊断或定位肺部损伤或功能障碍产生非常高的临床影响。
