Brooks Frank J, Gunsten Sean P, Vasireddi Sunil K, Brody Steven L, Anastasio Mark A
Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois.
Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri.
Physiol Rep. 2019 Aug;7(16):e14208. doi: 10.14814/phy2.14208.
To date, there are very limited noninvasive, regional assays of in vivo lung microstructure near the alveolar level. It has been suggested that x-ray phase-contrast enhanced imaging reveals information about the air volume of the lung; however, the image texture information in these images remains underutilized. Projection images of in vivo mouse lungs were acquired via a tabletop, propagation-based, X-ray phase-contrast imaging system. Anesthetized mice were mechanically ventilated in an upright position. Consistent with previously published studies, a distinct image texture was observed uniquely within lung regions. Lung regions were automatically identified using supervised machine learning applied to summary measures of the image texture data. It was found that an unsupervised clustering within predefined lung regions colocates with expected differences in anatomy along the cranial-caudal axis in upright mice. It was also found that specifically selected inflation pressures-here, a purposeful surrogate of distinct states of mechanical expansion-can be predicted from the lung image texture alone, that the prediction model itself varies from apex to base and that prediction is accurate regardless of overlap with nonpulmonary structures such as the ribs, mediastinum, and heart. Cross-validation analysis indicated low inter-animal variation in the image texture classifications. Together, these results suggest that the image texture observed in a single X-ray phase-contrast-enhanced projection image could be used across a range of pressure states to study regional variations in regional lung function.
迄今为止，在肺泡水平附近对活体肺微观结构进行的非侵入性区域检测非常有限。有人提出，X射线相衬增强成像可揭示有关肺气体容积的信息；然而，这些图像中的图像纹理信息仍未得到充分利用。通过基于传播的桌面式X射线相衬成像系统获取了活体小鼠肺的投影图像。对麻醉的小鼠进行机械通气并使其处于直立位置。与先前发表的研究一致，在肺区域内独特地观察到了明显的图像纹理。使用应用于图像纹理数据汇总测量的监督机器学习自动识别肺区域。研究发现，在预定义的肺区域内进行的无监督聚类与直立小鼠沿头-尾轴的预期解剖差异相符。还发现，仅从肺图像纹理就可以预测特定选择的充气压力——这里是机械扩张不同状态的有意替代指标，预测模型本身从肺尖到肺底各不相同，并且无论与肋骨、纵隔和心脏等非肺结构有无重叠，预测都是准确的。交叉验证分析表明，图像纹理分类在动物之间的差异较小。总之，这些结果表明，在单个X射线相衬增强投影图像中观察到的图像纹理可用于一系列压力状态，以研究区域肺功能的区域差异。
