Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy

Area of Science:

• Medical Physics
• Radiotherapy
• Image Analysis

Background:

• Accurate internal organ motion estimation is crucial for effective lung cancer radiotherapy.
• Current methods face challenges in precisely tracking internal organ movements during treatment fractions.

Purpose of the Study:

• To develop and validate an internal-external correlation model for internal motion estimation in lung cancer radiotherapy.
• To improve the accuracy of tumor and lung tracking during intra-fraction and inter-fraction periods.

Main Methods:

• Utilized a local topology preserved non-rigid point matching algorithm to obtain deformation vector fields (DVFs) from 4DCT images.
• Constructed a composite matrix combining internal and external DVFs, followed by principal component analysis (PCA) for motion correlation.
• Validated the model using a 4D NURBS-based cardiac-torso (NCAT) phantom and 4DCT data from five lung cancer patients.

Main Results:

• Achieved low center of mass errors for tumor tracking: 0.8(±0.5)mm/0.8(±0.4)mm (synthetic) and 1.3(±1.0)mm/1.2(±1.2)mm (patient) for intra-fraction/inter-fraction.
• Demonstrated low percent errors for lung contour tracking: 0.06(±0.02)/0.07(±0.03) (synthetic) and 0.06(±0.02)/0.06(±0.02) (patient) for intra-fraction/inter-fraction.
• The model showed high effectiveness and reliability in motion tracking for both tumors and lungs.

Conclusions:

• The developed internal-external correlation model provides accurate and reliable motion estimation for lung cancer radiotherapy.
• This approach enhances the precision of tumor and lung tracking, potentially improving treatment efficacy and patient outcomes.
• The validated model offers a significant advancement in real-time motion management during lung cancer radiation therapy.