Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT

Area of Science:

• Medical Imaging
• Radiation Oncology
• Image Reconstruction

Background:

• Image-guided radiation therapy (IGRT) uses kV imaging for patient positioning and treatment verification.
• Limited gantry speed in IGRT leads to long acquisition times and motion artifacts in thoracic 4D cone-beam CT (CBCT) scans.
• Artifacts in gated 4D CBCT reconstructions hinder accurate respiratory motion estimation.

Purpose of the Study:

• To develop a method for reliable respiratory motion estimation in the presence of severe artifacts.
• To enable motion-compensated image reconstruction for high-quality, respiratory-correlated 4D CBCT volumes.

Main Methods:

• A novel motion estimation model explicitly addresses image artifacts, unlike standard registration methods.
• The artifact model generates realistic streak artifacts to estimate and compensate for motion vector field errors.
• The algorithm was evaluated using simulated and clinical patient data with both cyclic and 3D-3D registration approaches.

Main Results:

• The model-based motion estimation method demonstrated insensitivity to artifacts caused by angular undersampling in gated 4D reconstructions.
• Accurate motion estimation and effective correction were achieved with the motion-compensated image reconstruction algorithm.
• Significant reduction in motion artifacts for standard 3D reconstructions with minimal introduction of new artifacts.

Conclusions:

• The artifact model enables accurate estimation and compensation of patient motion, even with low-quality initial reconstructions.
• Combining the artifact model with a cyclic registration algorithm results in high spatial and temporal resolution, insensitive to sparse-view artifacts.