Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study.

Autor:	Zhang L; Medical Physics Graduate Program, Duke University, Durham, NC, USA.; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA., Zhang Y; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA., Zhang Y; Medical Physics Graduate Program, Duke University, Durham, NC, USA.; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.; Department of Radiation Oncology, UT Southwestern Cancer Center, TX, USA., Harris WB; Medical Physics Graduate Program, Duke University, Durham, NC, USA.; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA., Yin FF; Medical Physics Graduate Program, Duke University, Durham, NC, USA.; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China., Cai J; Medical Physics Graduate Program, Duke University, Durham, NC, USA.; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China.; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China., Ren L; Medical Physics Graduate Program, Duke University, Durham, NC, USA.; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Jazyk:	angličtina
Zdroj:	Cancer translational medicine [Cancer Transl Med] 2017; Vol. 3 (6), pp. 185-193. Date of Electronic Publication: 2017 Dec 29.
Abstrakt:	Aim: During cancer radiotherapy treatment, on-board four-dimensional-cone beam computed tomography (4D-CBCT) provides important patient 4D volumetric information for tumor target verification. Reconstruction of 4D-CBCT images requires sorting of acquired projections into different respiratory phases. Traditional phase sorting methods are either based on external surrogates, which might miscorrelate with internal structures; or on 2D internal structures, which require specific organ presence or slow gantry rotations. The aim of this study is to investigate the feasibility of a 3D motion modeling-based method for markerless 4D-CBCT projection-phase sorting. Methods: Patient 4D-CT images acquired during simulation are used as prior images. Principal component analysis (PCA) is used to extract three major respiratory deformation patterns. On-board patient image volume is considered as a deformation of the prior CT at the end-expiration phase. Coefficients of the principal deformation patterns are solved for each on-board projection by matching it with the digitally reconstructed radiograph (DRR) of the deformed prior CT. The primary PCA coefficients are used for the projection-phase sorting. Results: PCA coefficients solved in nine digital phantoms (XCATs) showed the same pattern as the breathing motions in both the anteroposterior and superoinferior directions. The mean phase sorting differences were below 2% and percentages of phase difference < 10% were 100% for all the nine XCAT phantoms. Five lung cancer patient results showed mean phase difference ranging from 1.62% to 2.23%. The percentage of projections within 10% phase difference ranged from 98.4% to 100% and those within 5% phase difference ranged from 88.9% to 99.8%. Conclusion: The study demonstrated the feasibility of using PCA coefficients for 4D-CBCT projection-phase sorting. High sorting accuracy in both digital phantoms and patient cases was achieved. This method provides an accurate and robust tool for automatic 4D-CBCT projection sorting using 3D motion modeling without the need of external surrogate or internal markers. Competing Interests: Conflicts of interest There are no conflicts of interest.
Databáze:	MEDLINE
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