Optimization and Clinical Evaluation of a Prior Knowledge-based 4D Cone Beam CT Estimation Technique for Lung Radiotherapy

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2018

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Abstract

Purpose: 4D cone-beam CT (CBCT) provides 4D localization and monitoring of moving targets for inter/intra-fraction target verification in lung radiotherapy. CBCT reconstruction with Feldkamp-Davis-Kress (FDK) algorithm requires retrospectively sorted full-angle (360° for half fan scan/ 180° plus n angle for full fan scan) cone-beam projections, leading to long acquisition time, high imaging dose and limited mechanical clearance. A prior knowledge-based 4D-CBCT estimation technique was developed to do fast and low-dose target verification by estimating 4D-CBCT images using limited-angle on-board kV or MV projections and information from planning CT images. The purposes of this thesis are to (1) Optimize the image acquisition parameters of this technique in reconstructing 4D-CBCT images using limited-angle kV projections; and (2) Evaluate the clinical efficacy of this technique through patient studies.

Methods: A digital anthropomorphic phantom (XCAT) and real patient 4D-CT images were used to optimize and evaluate the prior knowledge-based 4D-CBCT estimation technique. To optimize the imaging acquisition schemes, phantom studies were conducted to simulate eight different treatment scenarios. The image acquisition schemes were optimized by minimizing the scanning angle/time required for accurate image estimation. With the minimum scanning angle/time determined, the effect of scanning direction and imaging frame rate on estimation accuracy was also tested. To clinically evaluate this technique through patient studies, we employed patient data with multiple 4D-CT scans. For each patient, one 4D-CT scan was considered as planning CT images and another as on-board ground truth 4D images. Digital reconstructed radiographs (DRRs) were generated from the second 4D-CT scan to simulate on-board 4D-CBCT projections in a limited angle. Each phase of the 4D-CBCT was generated by deforming the prior CT volume based on Deformation Field Maps solved by motion modeling and free-form deformation in the data fidelity constraint. Patients with tumors at different locations were selected for evaluation. The estimated images (EIs) were quantitatively evaluated against ground truth images by calculating the Dice Coefficient and Center-of-Mass-Shift (COMS) of the tumor volume. The minimal total scan angle/time was also determined for all patients.

Results: The phantom studies showed accurate 4D-CBCT estimation requires 200 projections acquired in over 97.8 degree within a total scanning time of 20 seconds (with gantry rotation speed of 6°/s, respiratory period of 4s and frame rate of 10 frame/s). We found the technique was robust against different scanning directions and imaging frame rate was positively related to estimation accuracy with the same angle coverage. The scanning angle and time of the technique could be further reduced by increasing projection number without changing the projection angle coverage. Results of patient studies showed that the technique was able to accurately estimate patient 4D-CBCT using as fewer as 320 projections for 10 phases acquired in 32 seconds over scan angle of 169.8° (with gantry rotation speed of 6°/s and frame rate of 10 frame/s for breathing period of xxx). The estimation efficiency was affected by target location and contrast between target and background.

Conclusion: This technique estimates patient on-board 4D-CBCT with higher efficiency, reduced imaging dose and more mechanical clearance compared to conventional reconstruction techniques. Clinical implementation of this technique can provide an efficient tool for fast low dose inter- and intra-fractional 4D-localization to minimize the treatment errors in lung radiotherapy, which paves the way for further margin reduction and dose escalation.

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Liu, Xiaoning (2018). Optimization and Clinical Evaluation of a Prior Knowledge-based 4D Cone Beam CT Estimation Technique for Lung Radiotherapy. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/17048.

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