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Effect of 4D-CT Image Artifacts On the 3D Lung Registration Accuracy: A Parametric Study Using a GPU-Accelerated Multi-Resolution Multi-Level Optical Flow

A Santhanam

A Santhanam1*, T Dou2, Y Min3, S Meeks4, P Kupelian5, (1) UCLA, Los Angeles, CA, (2) ,,,(3) UCLA, Los Angeles, CA, (4) M.D. Anderson Cancer Ctr Orlando, Orlando, FL, (5) University of California, Los Angeles, Los Angeles, California

SU-E-J-73 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: To perform a parametric study of the effect of registration parameters on 4D-CT image registration accuracy.

Methods and Materials: A GPU based 4D-CT image registration that registers the 4D lung anatomy using a multi-level multi-contrast optical flow was used for this study. A set of 14 4D-CT datasets was employed for this study. The multi-level lung anatomy was segmented into the surface contour, blood vessels and parenchyma regions using OsiriX. The registration started at the lowest resolution of a 3D volume. Within each resolution level, the volumes were registered using optical flow. The motion field was first computed for surface contour pairs in the lowest resolution. At this stage, all the voxels except those on the surface contour (the lowest level of anatomical representation) were not included. GPU based Thin-Plate Splines was applied to the motion field so that voxels surrounding the surface contour had an initial displacement motion, which was closer to the actual value. The motion field was iteratively updated until the highest (original) resolution of the volume was processed.

Results: The GPU implementation provided a speed-up of >50x as compared to the CPU implementation. The registration accuracy varied non-linearly with the kernel size. For both kernel size and smoothness factor, a non-linear correlation was observed towards the registration accuracy with an optimal value being 5 cu.mm and 200, respectively. The accuracy improved with the number of resolution and contrast levels with 4 and 3, respectively, providing optimal registration accuracy. Finally, usage of the first 3 anatomical level representations provided the optimum registration accuracy as opposed to 4 or more levels.

Conclusion: A parametric 4D image registration analyses showed its relationship to the registration accuracy to be non-linear. A patient breathing and CT-scanner specific study will quantitatively relate the registration errors on treatment planning and delivery.

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