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SU-E-J-26: A Novel Technique for Markerless Self-Sorted 4D-CBCT Using Patient Motion Modeling: A Feasibility Study.

Publication ,  Conference
Zhang, L; Zhang, Y; Harris, W; Yin, F; Ren, L
Published in: Med Phys
June 2015

PURPOSE: To develop an automatic markerless 4D-CBCT projection sorting technique by using a patient respiratory motion model extracted from the planning 4D-CT images. METHODS: Each phase of onboard 4D-CBCT is considered as a deformation of one phase of the prior planning 4D-CT. The deformation field map (DFM) is represented as a linear combination of three major deformation patterns extracted from the planning 4D-CT using principle component analysis (PCA). The coefficients of the PCA deformation patterns are solved by matching the digitally reconstructed radiograph (DRR) of the deformed volume to the onboard projection acquired. The PCA coefficients are solved for each single projection, and are used for phase sorting. Projections at the peaks of the Z direction coefficient are sorted as phase 1 and other projections are assigned into 10 phase bins by dividing phases equally between peaks. The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the proposed technique. Three scenarios were simulated, with different tumor motion amplitude (3cm to 2cm), tumor spatial shift (8mm SI), and tumor body motion phase shift (2 phases) from prior to on-board images. Projections were simulated over 180 degree scan-angle for the 4D-XCAT. The percentage of accurately binned projections across entire dataset was calculated to represent the phase sorting accuracy. RESULTS: With a changed tumor motion amplitude from 3cm to 2cm, markerless phase sorting accuracy was 100%. With a tumor phase shift of 2 phases w.r.t. body motion, the phase sorting accuracy was 100%. With a tumor spatial shift of 8mm in SI direction, phase sorting accuracy was 86.1%. CONCLUSION: The XCAT phantom simulation results demonstrated that it is feasible to use prior knowledge and motion modeling technique to achieve markerless 4D-CBCT phase sorting. National Institutes of Health Grant No. R01-CA184173 Varian Medical System.

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Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

June 2015

Volume

42

Issue

6

Start / End Page

3269

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Zhang, L., Zhang, Y., Harris, W., Yin, F., & Ren, L. (2015). SU-E-J-26: A Novel Technique for Markerless Self-Sorted 4D-CBCT Using Patient Motion Modeling: A Feasibility Study. In Med Phys (Vol. 42, p. 3269). United States. https://doi.org/10.1118/1.4924113
Zhang, L., Y. Zhang, W. Harris, F. Yin, and L. Ren. “SU-E-J-26: A Novel Technique for Markerless Self-Sorted 4D-CBCT Using Patient Motion Modeling: A Feasibility Study.” In Med Phys, 42:3269, 2015. https://doi.org/10.1118/1.4924113.
Zhang, L., et al. “SU-E-J-26: A Novel Technique for Markerless Self-Sorted 4D-CBCT Using Patient Motion Modeling: A Feasibility Study.Med Phys, vol. 42, no. 6, 2015, p. 3269. Pubmed, doi:10.1118/1.4924113.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

June 2015

Volume

42

Issue

6

Start / End Page

3269

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences