Multi-level digital-twin models of pulmonary mechanics: correlation analysis of 3D CT lung volume and 2D Chest motion.

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Biomedical Physics & Engineering Express Pub Date : 2024-11-06 DOI:10.1088/2057-1976/ad8c47
Cong Zhou, J Geoffrey Chase, Yuhong Chen
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Abstract

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 ofRsquared 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.

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肺力学的多级数字孪生模型:三维 CT 肺容积与二维胸廓运动的相关性分析。
创建用于机械通气的多级数字孪生模型需要对区域肺容积进行详细估算。在二维胸腔表面运动和三维区域肺容积之间绘制精确的通用图,可改善区域化和临床上可接受的肺损伤定位估算。这项工作研究了 CT 肺容量与强制生命容量(FVC)之间的关系,强制生命容量是潮气量的替代物,与二维胸部运动相关。特别是,采用 U-Net 架构的卷积神经网络 (CNN) 利用基准 CT 扫描数据集建立了肺部分割模型。为提高模型性能,提出了一种用于图像形态分析的自动阈值化方法。最后,将训练好的模型应用于带有 FVC 测量值的独立 CT 数据集,以进行 CT 肺体积投影与肺募集容量的相关性分析。模型训练结果表明,与通常建议的固定值选择相比,所提出的自动阈值方法明显提高了肺分割性能,训练集和独立验证集的准确率均超过 95%。对 160 名患者进行的相关性分析表明,建议的二维肺容积投影与 FVC 值之间的相关性为 0.73,这表明相对于更大的 FVC 值和肺可募集容量,肺容积投影更大、更密集。因此,总体结果验证了使用非接触、非侵入性二维测量方法的潜力,可根据良好的相关性将肺力学模型区域化为具有通用图的等效三维模型。将肺力学和肺容量区域化到特定的肺部区域,从而改进了肺力学数字双胞胎,这对管理机械通气、基于定期监测而非间歇性和侵入性肺成像模式诊断或定位肺损伤或功能障碍具有非常大的临床影响。
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来源期刊
Biomedical Physics & Engineering Express
Biomedical Physics & Engineering Express RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.80
自引率
0.00%
发文量
153
期刊介绍: BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.
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