A Splatting Method to Generate DRRs for Deformed CT Volume
Z Zhong1*, Y Cai1, X Guo1, T Solberg2, W Mao2, (1) The University of Texas at Dallas, Richardson, TX,(2) UT Southwestern Medical Center, Dallas, TXSU-E-J-112 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall
Purpose: To develop an efficient algorithm for generating high-quality digitally reconstructed radiographs (DRRs) for regularly and irregularly sampled volumes based on a splatting method with dynamic elliptical Gaussian kernels, and to evaluate this method using ray tracing.
Methods: The traditional ray tracing method, which takes every intersected voxel into account, produces high quality DRRs but is very time consuming. Additionally, it is not suitable for handling irregularly-sampled volumes since it always requires image re-sampling, which leads to inaccuracy. We present a splatting approach to compute the 'footprint function', facilitating efficient perspective projection of elliptical Gaussian kernels at very low cost. This reported framework allows dealing with both regularly and irregularly sampled volumes effectively and efficiently. An XCAT digital phantom was used to generate 3D chest volumes at different respiratory phases, and CT projections are generated using ray tracing and the splatting method, respectively. Normalized cross correlation (NCC) is applied to evaluate the DRR similarity of two methods.
Results: Respiratory Phases one and four are used as volume datasets from the 4D XCAT digital phantom as they represent the lung at end inhale and end exhale, respectively. The dataset of Phase one is the basic regularly-sampled volume while the dataset of Phase four is deformed from that of Phase one, and re-sampled to a regularly sampled dataset for the ray tracing method. NCC between splatting and ray tracing DRRs are 0.9980 and 0.9977 for Phase one and Phase four, respectively. The calculation speed of the splatting method is 3 times faster than that of ray tracing.
Conclusions: Our splatting approach can generate high-quality DRRs efficiently, and is a good alternative for current DRR generation techniques for deformed volume datasets.