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MO‐F‐BRA‐05: Real‐Time 3D Tumor Localization for Lung IGRT Using a Single X‐Ray Projection
Author(s) -
Chou C,
Frederick C,
Chang S,
Pizer S
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4735824
Subject(s) - image guided radiation therapy , projection (relational algebra) , medicine , nuclear medicine , medical imaging , computer science , radiology , algorithm
Purpose: To study the feasibility of a novel 2D/3D image registration method, called Projection Metric Learning for Shape Kernel Regression (PML‐SKR), in supporting on‐board x‐ray imaging systems to perform real‐time image‐guided radiation therapy in the lung. Methods: PML‐SKR works in two stages: planning and treatment. At planning stage, firstly it parameterizes the patient's respiratory deformation from the patient's treatment‐planning Respiratory‐Correlated CTs (RCCTs) by doing PCA analysis on the inter‐phase respiratory deformations. Secondly, it simulates a set of training projection images from a set of deformed CTs where their associated deformation parameters are sampled within 3 standard deviations of the parameter's values observed in the RCCTs. Finally, it learns a Riemannian distance metric on projection intensity for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave‐one‐out regression residual of the corresponding deformation parameter. At treatment stage, PML‐SKR interpolates the patient's 3D deformation parameters from the parameter's values in the training cases using the kernel regression with the learned distance metrics. Results: We tested PML‐SKR on the NST (Nanotube Stationary Tomosynthesis) x‐ray imaging system. In each test case, a DRR (dimension: 64×64) of an x‐ray source in the NST was simulated from a target CT for registration. The target CTs were deformed by normally distributed random samples of the first three deformation parameters. We generated 300 synthetic test cases from 3 lung datasets and measured the registration quality by the mTRE (mean Target Registration Error) over all cases and all voxels at tumor sites. With PML‐SKR's registrations, the average mTRE and its standard deviation are down from 10.89±4.44 to 0.67±0.46 mm using 125 training projection images. The computation time for each registration is 12.71±0.70 ms. Conclusion: The synthetic results have shown PML‐SKR's promise in supporting real‐time, accurate, and low‐dose lung IGRT. This work was partially supported by Siemens Medical Solutions.