Low-dose quantitative cone-beam CT imaging in radiation therapy

Abstract

Cone-beam CT (CBCT) applications in radiation therapy are limited by excessive imaging dose from repeated scans and poor image quality mainly due to scatter contamination. Recently, we develop a Compressed Sensing (CS) reconstruction algorithm referred to as Accelerated Barrier Optimization for CS (ABOCS) with the features of fast convergence and consistent parameter tuning. ABOCS has shown promises in recovering faithful signals from low-dose projection data, but does not serve well the needs of quantitative CBCT imaging if effective shading correction is not in place. In the meanwhile, our quantitative CBCT imaging scheme uses the planning CT (pCT) as the prior and successfully suppresses the shading artifacts, but requires a large number of projections for an accurate FBP reconstruction. In this work, we propose a low-dose quantitative CBCT imaging strategy by combining the merits of ABOCS reconstruction and the pCT-based shading correction. We lower the CBCT dose by reducing the number of projections. The shading artifacts have been effectively removed using the pCT-based quantitative CBCT imaging scheme. The streaking artifacts due to view aliasing and high noise level are greatly suppressed using the ABOCS reconstruction which is based on the TV optimization framework with data fidelity errors as the constraints. As demonstrated on a pelvis patient study, the number of projections is reduced to only 25%. The mean CT number error is reduced to be less than 15 HU in the selected regions of interest. Using 25% data, ABOCS still successfully achieves a comparable image quality (e.g., spatial non-uniformity and soft-tissue contrast) to that using FBP reconstruction on the full data set after an effective pCT-based shading correction. We propose a low-dose quantitative CBCT imaging scheme using 25% normal dose to achieve a less than 15 HU CT-number error. This method combines the benefits of CS reconstruction and shading correction, which is promising for clinical applications.