An adversarial machine learning framework and biomechanical model‐guided approach for computing 3D lung tissue elasticity from end‐expiration 3DCT. Issue 2 (22nd December 2020)

• Record Type: Journal Article
• Authors: Santhanam, Anand P.; Stiehl, Brad; Lauria, Michael; Hasse, Katelyn; Barjaktarevic, Igor; Goldin, Jonathan; Low, Daniel A.
• Is Part Of: Medical physics. Volume 48:Issue 2(2021)
• Page Start: 667
• Page End: 675
• Publication Date: 2020-12-22
• Subjects: 4DCT -- biomechanical modeling -- deep learning -- lung elastography; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14252
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four‐dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain. Methods: In this paper, we present a machine learning‐based method that predicts the three‐dimensional (3D) lung tissue elasticity distribution for a given end‐expiration 3DCT. The method to predict the lung tissue elasticity from an end‐expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed five‐dimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end‐expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground‐truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end‐exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath‐hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2‐norm‐based direct comparison was employed between the estimated elasticity and the ground‐truth elasticity. In the second approach, we generated a synthetic four‐dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the ground‐truth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC). Results: The results show that a cGAN‐based machine learning approach was effective in computing the lung tissue elasticity given the end‐expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44 ± 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 ± 0.4 KPa. These results show that the cGAN‐generated elasticity correlates well with that of the underlying ground‐truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN‐generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity‐generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity‐generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity‐generated end inhalation CT and the ground‐truth end inhalation CT. Conclusion: The cGAN‐generated lung tissue elasticity given an end‐expiration CT image can be computed in near real time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end‐expiration CT image within clinically acceptable numerical accuracy.