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ORIGINAL ARTICLE
Year : 2017  |  Volume : 3  |  Issue : 6  |  Page : 185-193

Markerless four-dimensional-cone beam computed tomography projection-phase sorting using prior knowledge and patient motion modeling: A feasibility study


1 Medical Physics Graduate Program, Duke University, Durham, NC; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
2 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
3 Medical Physics Graduate Program, Duke University, Durham, NC; Department of Radiation Oncology, Duke University Medical Center, Durham, NC; Department of Radiation Oncology, UT Southwestern Cancer Center, TX, USA
4 Medical Physics Graduate Program, Duke University; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
5 Medical Physics Graduate Program, Duke University; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
6 Medical Physics Graduate Program, Duke University, Durham, NC, USA; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

Correspondence Address:
Lei Ren
Department of Radiation Oncology, Duke University Medical Center, PO Box. 3295, Durham, NC 27710
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ctm.ctm_38_17

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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.


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