Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient
Lei Zhang1,2, Yawei Zhang2, You Zhang1,2,3
1Medical Physics Graduate Program, Duke University, Durham, NC, USA.
This study introduces a novel 3D motion modeling method for accurate markerless respiratory phase sorting in four-dimensional-cone beam computed tomography (4D-CBCT). The technique achieves high sorting accuracy, improving tumor target verification in cancer radiotherapy.
Area of Science:
- Medical Imaging
- Radiotherapy Physics
- Computational Anatomy
Background:
- Four-dimensional-cone beam computed tomography (4D-CBCT) is crucial for radiotherapy tumor targeting, requiring accurate sorting of projections into respiratory phases.
- Current phase sorting methods using external surrogates or 2D internal structures have limitations in accuracy and applicability.
- Markerless and robust phase sorting is needed for reliable 4D-CBCT reconstruction.
Purpose of the Study:
- To investigate the feasibility of a 3D motion modeling approach for markerless projection-phase sorting in 4D-CBCT.
- To develop and validate a method utilizing principal component analysis (PCA) for respiratory motion characterization.
- To assess the accuracy of the proposed method in digital phantoms and patient data.
Main Methods:
- Utilized patient 4D-CT images as prior information for 3D motion modeling.
- Applied Principal Component Analysis (PCA) to extract dominant respiratory deformation patterns.
- Solved PCA coefficients for each projection by matching with digitally reconstructed radiographs (DRRs) of deformed prior CTs.
- Employed PCA coefficients for projection-phase sorting.
Main Results:
- PCA-based sorting accurately reflected breathing motion patterns in anteroposterior and superoinferior directions in digital phantoms (XCATs).
- Achieved mean phase sorting differences below 2% with 100% of projections within 10% phase difference in XCATs.
- Patient data demonstrated mean phase differences from 1.62% to 2.23%, with 98.4%-100% of projections within 10% phase difference.
Conclusions:
- The study successfully demonstrated the feasibility of using PCA coefficients for accurate 4D-CBCT projection-phase sorting.
- The 3D motion modeling method provides a robust and automatic solution for 4D-CBCT reconstruction.
- This markerless approach enhances tumor target verification accuracy in radiotherapy without external surrogates or internal markers.
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