Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy.

James J Sohn,Tian Liu,Xiaofeng Yang,Changsoo Kim,Seu-Ran Lee,Dong Hyun Kim,Jun Zhou

doi:10.3389/fonc.2020.00242

James J Sohn, Tian Liu + Show 5 more

Open Access

https://doi.org/10.3389/fonc.2020.00242

Copy DOI

Abstract

Purpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT projection with the non-local total variation (NLTV) method.Methods: We modeled the low-dose CBCT reconstruction as recovering high quality, high-dose CBCT x-ray projections (100 kVp, 1.6 mAs) from low-dose, noisy CBCT x-ray projections (100 kVp, 0.1 mAs). The restoration of CBCT projections was performed using the NLTV regularization method. In NLTV, the x-ray image is optimized by minimizing an energy function that penalizes gray-level difference between pair of pixels between noisy x-ray projection and denoising x-ray projection. After the noisy projection is restored by NLTV regularization, the standard FDK method was applied to generate the final reconstruction output.Results: Significant noise reduction was achieved comparing to original, noisy inputs while maintaining the image quality comparable to the high-dose CBCT projections. The experimental validations show the proposed NLTV algorithm can robustly restore the noise level of x-ray projection images while significantly improving the overall image quality. The improvement in normalized mean square error (NMSE) and peak signal-to-noise ratio (PSNR) measured from the non-local total variation-gradient projection (NLTV-GPSR) algorithm is noticeable compared to that of uncorrected low-dose CBCT images. Moreover, the difference of CNRs from the gains from the proposed algorithm is noticeable and comparable to high-dose CBCT.Conclusion: The proposed method successfully restores noise degraded, low-dose CBCT projections to high-dose projection quality. Such an outcome is a considerable improvement to the reconstruction result compared to the FDK-based method. In addition, a significant reduction in reconstruction time makes the proposed algorithm more attractive. This demonstrates the potential use of the proposed algorithm for clinical practice in radiotherapy.

Highlights

CBCT has been widely adopted for radiotherapy for tumor visualization and localization [1, 2]
It is noticed that the noise, as well as the artifacts, are significantly reduced, and quality comparable to that of the CBCT reconstructed from high-dose projections is achieved
It is clear that the proposed non-local total variation (NLTV)-gradient projection for sparse reconstruction (GPSR) algorithm achieves better performance compared to the original low-dose CBCT images in terms of both artifact removal and preserves more sharper edges

Summary

Introduction

CBCT has been widely adopted for radiotherapy for tumor visualization and localization [1, 2]. CBCT delivers considerable imaging dose to the patient through ionizing x-rays. The cumulative imaging dose from repeated CBCT scans is clinically significant, and the optimization of x-ray exposure conditions is necessary to meet dose constraints. Keeping the low dose in CBCT with as low as reasonably achievable (ALARA) principles, it is desirable to reduce imaging dose [3,4,5]. Minimizing imaging dose while maintaining adequate image quality for accurate tumor visualization is highly desirable in the clinical setting. Imaging dose is proportional to the exposure level (mAs) from the x-ray tube of the CBCT imaging system. Reducing the exposure level reduces the fluence to x-rays projected onto the patient and reduces the CBCT imaging dose. An excessive reduction in exposure amplifies the noise level of the projection image due to the photon starvation effect. When using the conventional Feldkamp, Davis, and Kress (FDK) algorithm for reconstruction, it is difficult to sustain the image quality due to the amplification of noise with the high bandpass filter applied to noisy projection data [6,7,8]

Methods

Results

Discussion

Conclusion