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Shading Correction for Cone Beam CT in Radiation Therapy Via Sparse Sampling On Planning CT

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L Shi

L Shi1*, T Tsui2 , J Wei3 , L Zhu4 , (1) Georgia Institute of Technology, Atlanta, GA (2) Cancer Treatment Center of America, Newnan, GA, (3) Landauer Medical Physics , Newnan, GA, (4) Georgia Institute of Technology, Atlanta, GA


SU-K-FS4-16 (Sunday, July 30, 2017) 4:00 PM - 6:00 PM Room: Four Seasons 4

Purpose: The image quality of cone beam CT (CBCT) is limited by severe shading artifacts, hindering its quantitative applications in radiation therapy. We propose an image-domain shading correction method using planning CT (pCT) as prior information which is highly adaptive to clinical environment.

Methods: We propose to perform shading correction via sparse sampling on pCT. The method starts with a coarse mapping between the first-pass CBCT images obtained from the Varian TrueBeam system and the pCT. The CBCT images still contain severe shading artifacts after processed with scatter correction embedded in the Varian commercial software. The difference images between the mapped pCT and the CBCT are considered as shading errors, but only sparse shading samples are selected for correction using empirical constraints to avoid carrying over false information from pCT. A Fourier-Transform based technique, referred to as local filtration, is proposed to efficiently process the sparse data for effective shading correction. The performance of the proposed method is evaluated on one anthropomorphic pelvis phantom and 17 patients. Spatial non-uniformity (SNU) was used as quantitative metric to evaluate the results.

Results: On the phantom, the spatial non-uniformity difference between CBCT and pCT is reduced from 74 to 1 HU. The root of mean square difference of SNU between CBCT and pCT is reduced from 83 to 10 HU on the pelvis patients, and from 101 to 12 HU on the thorax patients. The sparse sampling scheme successfully removes shading errors without adding false structures even when the maximum registration error is as high as 8 mm.

Conclusion: We develop an effective shading correction algorithm for CBCT readily implementable on clinical data without modifications of current imaging hardware and protocol. The algorithm is directly applied on the output images from a commercial CBCT scanner with high computational efficiency and negligible memory burden.

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