Ultra-Low Dose 4D-CBCT Image Reconstruction Via Joint Sparse and Nonlocal Regularization

Abstract

4D cone-beam CT (4D-CBCT) is becoming an indispensable tool in cancer radiotherapy, image-guided interventions, and many other clinical applications. However, excessive radiation exposure presents a major concern for its widespread clinical applications due to its prolonged scanning duration. While intense efforts have been made in the development of 4D-CBCT under sparse-view projections, few progress has been made to address the noise problem in ultra-low dose 4D-CBCT when the projection data are also subject to photon starvation. The purpose of this work is to establish a joint sparse and nonlocal regularization approach for improved 4D-CBCT image quality with order of magnitude less radiation dose as compared to the existing techniques. To address the under-sampling problem in 4D-CBCT, we adopted the spatiotemporal tensor framelet (STF) regularization model to characterize the spatiotemporal sparse property of patient anatomy by effective use of inter-phase correlation of the projections. To simultaneously tackle the imaging noise problem caused by photon starvation, the spatiotemporal nonlocal total variation (SNTV) regularization model was also incorporated to take into account the spatiotemporal nonlocal self-recursive property of anatomical structures in 4D images. The proposed joint STF-SNTV regularization model in conjunction with the projection data fidelity constraint was optimized via the Bregman operator splitting technique. The proposed approach was evaluated first using dynamic digital phantoms and then using physical experiment data in the low-dose context of both sparse and noisy projections. Root mean squared error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were utilized to quantitatively evaluate the reconstructed image quality. With at least one order of magnitude less radiation dose, the presented joint regularization approach achieved improved image quality in terms of noise suppression when comparing with the existing STF regularization approach. Our approach also outperformed the existing SNTV approach in terms of resolution and detail preservation. Simulation studies showed that the improvement over the STF approach is 22.41% in RMSE and 10.07% in SNR; the improvement over the SNTV approach is 51.32% in RMSE and 34.32% in SNR. While for the experimental data, the improvement over the STF is 4.42% in CNR, and the improvement over the SNTV is 14.51% in CNR. All quantitative metrics indicated the superior performance of the proposed method. The presented approach is not only effective in suppressing the view-aliasing artifacts due to angular under-sampling, but also superior in suppressing noise artifacts caused by photon starvation. With improved image quality and reduced imaging dose, our approach represents a significant step forward in 4D-CBCT guided radiation therapy.