Program Information
A Method for Characterizing and Validating Dynamic Lung Density Change During Quiet Respiration
T Dou1*, D Ruan2 , M Heinrich3 , D Low4 , (1) University of California, Los Angeles, Los Angeles, CA, (2) UCLA School of Medicine, Los Angeles, CA, (3) Institute of Medical Informatics, University of Lubeck, Lubeck, Schleswig-Holstein, (4) UCLA, Los Angeles, CA
Presentations
TH-CD-202-6 (Thursday, August 4, 2016) 10:00 AM - 12:00 PM Room: 202
Purpose: To obtain a functional relationship that calibrates the lung tissue density change under free breathing conditions through correlating Jacobian values to the Hounsfield units.
Methods: Free-breathing lung computed tomography images were acquired using a fast helical CT protocol, where 25 scans were acquired per patient. Using a state-of-the-art deformable registration algorithm, a set of the deformation vector fields (DVF) was generated to provide spatial mapping from the reference image geometry to the other free-breathing scans. These DVFs were used to generate Jacobian maps, which estimate voxelwise volume change. Subsequently, the set of 25 corresponding Jacobian and voxel intensity in Hounsfield units (HU) were collected and linear regression was performed based on the mass conservation relationship to correlate the volume change to density change. Based on the resulting fitting coefficients, the tissues were classified into parenchymal (Type I), vascular (Type II), and soft tissue (Type III) types. These coefficients modeled the voxelwise density variation during quiet breathing. The accuracy of the proposed method was assessed using mean absolute difference in HU between the CT scan intensities and the model predicted values. In addition, validation experiments employing a leave-five-out method were performed to evaluate the model accuracy.
Results: The computed mean model errors were 23.30±9.54 HU, 29.31±10.67 HU, and 35.56±20.56 HU, respectively, for regions I, II, and III, respectively. The cross validation experiments averaged over 100 trials had mean errors of 30.02 ± 1.67 HU over the entire lung. These mean values were comparable with the estimated CT image background noise.
Conclusion: The reported validation experiment statistics confirmed the lung density modeling during free breathing. The proposed technique was general and could be applied to a wide range of problem scenarios where accurate dynamic lung density information is needed.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by NIH R01 CA0096679.
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