Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach.

PURPOSE

This article proposes an accurate and fast deformable registration method between end-exhale and end-inhale CT scans that can handle large lung deformations and accelerate the registration process.

METHODS

The density correction method is applied to reduce the density difference between two CT scans due to respiration and gravity. The lungs are globally aligned by affine registration and nonlinearly deformed by a demons algorithm using a combined gradient force and active cells. The use of combined gradient force allows a fast convergence in the lung regions with a weak gradient of the target image by taking into account the gradient of the source image. The use of active cells helps to accelerate the registration process and reduce the degree of deformation folding because it avoids unnecessary computation of the displacement for well-matched lung regions.

RESULTS

The proposed method was tested with end-exhale and end-inhale CT scans acquired from eight normal subjects. The performance of the proposed method was evaluated through comparisons of methods that use a target gradient force or a combined gradient force, as well as methods with and without active cells. The proposed method with combined gradient force led to significantly higher accuracy compared to the method with target gradient force. For the entire lung, the proposed method provided a mean landmark error of 2.8 +/- 1.5 mm. For the lower 30% part of the lungs, the Dice similarity coefficient and normalized cross correlation of the proposed method were higher than the original demon algorithm by 2.3% (p=0.0172) and 2.2% (p=0.0028), respectively. The proposed method with an active cell led to fewer voxels with negative Jacobian values and a 55% decrease of processing time compared to the method without an active cell.

CONCLUSIONS

The results show that the proposed method can accurately register lungs with large deformations and can considerably reduce the processing time. The proposed deformable registration technique can be used for quantitative assessments of air trapping in obstructive lung disease and for tumor motion tracking during the planning of radiotherapy treatments.