A quantitative analysis of biomechanical lung model consistency using 5DCT datasets. Issue 11 (17th September 2020)

• Record Type: Journal Article
• Authors: Stiehl, Brad; Lauria, Michael; O'Connell, Dylan; Hasse, Katelyn; Barjaktarevic, Igor Z.; Lee, Percy; Low, Daniel A.; Santhanam, Anand P.
• Is Part Of: Medical physics. Volume 47:Issue 11(2020)
• Page Start: 5555
• Page End: 5567
• Publication Date: 2020-09-17
• Subjects: 5DCT -- biomechanical modeling -- lung elastography; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14323
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.