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: T...
详细信息
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 deforma
Purpose: The accuracy and convergence behavior of a variant of the demons deformable registration algorithm were investigated for use in cone-beam CT (CBCT)-guided procedures of the head and neck. Online use of deform...
详细信息
Purpose: The accuracy and convergence behavior of a variant of the demons deformable registration algorithm were investigated for use in cone-beam CT (CBCT)-guided procedures of the head and neck. Online use of deformable registration for guidance of therapeutic procedures such as image-guided surgery or radiation therapy places trade-offs on accuracy and computational expense. This work describes a convergence criterion for demons registration developed to balance these demands;the accuracy of a multiscale demons implementation using this convergence criterion is quantified in CBCT images of the head and neck. Methods: Using an open-source "symmetric" demons registration algorithm, a convergence criterion based on the change in the deformation field between iterations was developed to advance among multiple levels of a multiscale image pyramid in a manner that optimized accuracy and computation time. The convergence criterion was optimized in cadaver studies involving CBCT images acquired using a surgical C-arm prototype modified for 3D intraoperative imaging. CBCT-to-CBCT registration was performed and accuracy was quantified in terms of the normalized cross-correlation (NCC) and target registration error (TRE). The accuracy and robustness of the algorithm were then tested in clinical CBCT images of ten patients undergoing radiation therapy of the head and neck. Results: The cadaver model allowed optimization of the convergence factor and initial measurements of registration accuracy: demons registration exhibited TRE = (0.8 +/- 0.3) mm and NCC = 0.99 in the cadaveric head compared to TRE = (2.6 +/- 1.0) mm and NCC = 0.93 with rigid registration. Similarly for the patient data, demons registration gave mean TRE = (1.6 +/- 0.9) mm compared to rigid registration TRE = (3.6 +/- 1.9) mm, suggesting registration accuracy at or near the voxel size of the patient images (1 x 1 x 2 mm(3)). The multiscale implementation based on optimal convergence criteria completed regis
暂无评论