WE‐G‐141‐06: Progressive Cone Beam CT Dose Control in Image‐Guided Radiation Therapy

Purpose: In image‐guided radiotherapy (IGRT), currently cone beam CT (CBCT) offers the best solution for patient setup. Yet its associated imaging dose is a clinical concern. One unique feature of CBCT‐based IGRT is that, the same patient is often repeatedly scanned during a treatment course, and the sequence of CBCT images at different fractions share a large amount of redundant information. We propose a progressive dose control (PDC) scheme to utilize this temporal correlation for imaging dose reduction. Methods: A dynamic CBCT scan protocol, as opposed to the static one in the current practice, is proposed to gradually reduce the imaging dose at each fraction. The CBCT image at each day is processed by a prior image based non‐local means (PINLM) module to enhance its quality using previous CBCT images as prior information. The increasing amount of prior information prevents the loss of image quality due to dose reduction. Two proof‐of‐principle experiments have been conducted using measured phantom data and synthetic patient data simulated by a Monte Carlo method. Results: In the measured phantom case, PINLM is able to improve the image quality of a CBCT acquired at 0.2 mAs by reducing the noise level from 34.95 HU to 12.45 HU, utilizing a prior image acquired at 0.4 mAs (standard low‐dose protocol). In the synthetic patient case, acceptable image quality is maintained at four consecutive fractions with gradually decreased exposure level at 0.4, 0.1, 0.07 and 0.05 mAs. Compared with the standard low‐dose protocol with a constant 0.4 mAs for all fractions, an overall imaging dose reduction of over 60% is achieved. Conclusion: PINLM‐PDC is able to reduce CBCT imaging dose in IGRT utilizing the temporal correlations among the sequence of CBCTs while maintaining the image quality. This work is supported in part by NIH (1R01CA154747‐01), Varian Medical Systems through a Master Research Agreement, the Early Career Award from Thrasher Research Fund, and the University of California Lab Fees Research Program.