Stiehl Brad, Lauria Michael, O'Connell Dylan, Hasse Katelyn, Barjaktarevic Igor Z, Lee Percy, Low Daniel A, Santhanam Anand P
Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.
Med Phys. 2020 Nov;47(11):5555-5567. doi: 10.1002/mp.14323. Epub 2020 Sep 17.
Lung biomechanical models are important for understanding and characterizing lung anatomy and physiology. A key parameter of biomechanical modeling is the underlying tissue elasticity distribution. While human lung elasticity estimations do not have ground truths, model consistency checks can and should be employed to gauge the stability of the estimation techniques. This work proposes such a consistency check using a set of 10 subjects.
We hypothesize that lung dynamics will be stable over a 2-3 min time period and that this stability can be employed to check biomechanical estimation stability. For this purpose, two sets of 12 fast helical free breathing computed tomography scans (FHFBCT) were acquired back-to-back for each of the subjects. A published breathing motion model [five-dimensional CT (5DCT)] was generated from each set. Both of the models were used to generate two biomechanical modeling input sets: (a) The lung geometry at the end-exhalation, and (b) the voxel displacement map that mapped the end-exhalation lung geometry with the end-inhalation lung geometry. Finite element biomechanical lung models were instantiated using the end-exhalation lung geometries. The models included voxel-specific lung tissue elasticity values and were optimized using a gradient search approach until the biomechanical model-generated displacement maps matched those of the 5DCT voxel displacement maps. Finally, the two elasticity distributions associated with each of the patient 5DCTs were quantitatively compared. Because the end-exhalation geometries differed slightly between the two scan datasets, the elasticity distributions were deformably mapped to one of the exhalation datasets.
For the 10 patients, on average, 90% of parenchymal voxels had <2 kPa Young's modulus difference between the two estimations, with a mean voxel difference of only 0.6 kPa. Similarly, 97% of the parenchymal voxels had <2 mm displacement difference between the two models with a mean difference of 0.48 mm. Furthermore, overlapping elasticity histograms for voxels between -600 and -900 HU (parenchymal tissues) showed that the histograms were consistent between the two estimations.
In this paper, we demonstrated that biomechanical lung models can be consistently estimated when using motion-model based imaging datasets, even though the models were created from scans acquired at different breaths.
肺生物力学模型对于理解和描述肺的解剖结构与生理功能至关重要。生物力学建模的一个关键参数是潜在的组织弹性分布。虽然人体肺弹性估计没有确凿的标准值，但模型一致性检查能够且应该用于评估估计技术的稳定性。这项工作提出了一种针对10名受试者的一致性检查方法。
我们假设肺动力学在2至3分钟的时间段内是稳定的，并且这种稳定性可用于检查生物力学估计的稳定性。为此，为每位受试者连续获取两组12次快速螺旋自由呼吸计算机断层扫描（FHFBCT）。从每组扫描中生成一个已发表的呼吸运动模型[五维CT（5DCT）]。这两个模型都用于生成两个生物力学建模输入集：（a）呼气末的肺几何形状，以及（b）将呼气末肺几何形状与吸气末肺几何形状映射的体素位移图。使用呼气末肺几何形状实例化有限元生物力学肺模型。这些模型包括体素特定的肺组织弹性值，并使用梯度搜索方法进行优化，直到生物力学模型生成的位移图与5DCT体素位移图相匹配。最后，对与每个患者5DCT相关的两个弹性分布进行定量比较。由于两个扫描数据集之间的呼气末几何形状略有不同，因此将弹性分布变形映射到其中一个呼气数据集上。
对于这10名患者，平均而言，90%的实质体素在两次估计之间的杨氏模量差异小于2kPa，平均体素差异仅为0.6kPa。同样，97%的实质体素在两个模型之间的位移差异小于2mm，平均差异为0.48mm。此外，-600至-900HU（实质组织）之间体素的弹性直方图重叠显示，两次估计之间的直方图是一致的。
在本文中，我们证明了即使模型是根据不同呼吸时获取的扫描创建的，但使用基于运动模型的成像数据集时，生物力学肺模型仍可得到一致的估计。
