PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT

Abstract

4D cone-beam computed tomography (CBCT) is an important imaging modality in image-guided radiation therapy to address the motion-induced artifacts caused by organ movements during the respiratory process. However, due to the extremely sparse projection data for each temporal phase, 4D CBCT reconstructions will suffer from severe streaking artifacts. Therefore, to tackle the streak artifacts and provide high-quality images, we proposed a framework termed Prior-Regularized Iterative Optimization Reconstruction (PRIOR) for 4D CBCT. The PRIOR framework combines the physics-based model and data-driven method simultaneously, with powerful feature extracting capacity, significantly promoting the image quality compared to single model-based or deep learning-based methods. Besides, we designed a specialized deep learning model named PRIOR-Net, which can effectively excavate the static information in the prior image reconstructed from the fully-sampled projections at the encoding stage to improve the reconstruction performance for individual phase-resolved images. Both the simulated and clinical 4D CBCT datasets were performed to evaluate the performance of the PRIOR-Net and the PRIOR framework. Compared with the advanced 4D CBCT reconstruction methods, the proposed methods achieve promising results quantitatively and qualitatively in streak artifact suppression, soft tissue restoration, and tiny detail preservation.

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging

“…Model evaluation will also be conducted in the context of other clinical tasks. Extended network applications, such as CT reconstruction ( 52 ), spectrum CT ( 53 ), and cone beam CT (CBCT) ( 54 ), can also be investigated in the future.…”

Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

“…As the computational resources have become more powerful, deep learning for sparse-view artifact reduction has extended from 2D models for single slice processing to 3D models and processing of 4D CBCT scans [72]. The use of prior (planing) CT and CBCT volumes to enhance the trained models, such as regularized iterative optimization reconstruction (PRIOR-Net [75]) and merge-encoder CNN (MeCNN [73]) have recently become popular for sparse-view artifact reduction. Researchers have also investigated using perceptionaware [76] and physics-based [75] methods.…”

“…The use of prior (planing) CT and CBCT volumes to enhance the trained models, such as regularized iterative optimization reconstruction (PRIOR-Net [75]) and merge-encoder CNN (MeCNN [73]) have recently become popular for sparse-view artifact reduction. Researchers have also investigated using perceptionaware [76] and physics-based [75] methods. The learning paradigm has expanded beyond purely supervised learning to different tasks, such as denoising (DRUNet [77]), artifact reduction [78], self-supervised by dropping projections [18] and unsupervised learning through training conditional and generative adversarial networks (GANs) [79].…”

CROSS: Cross-Domain Residual-Optimization-Based Structure Strengthening Reconstruction for Limited-Angle CT

“…Recently, DL-based methods have been successfully applied to the field of medical imaging [21][22][23]. Especially, convolutional neural network (CNN)-based methods brought more promising results than traditional reconstruction images [24][25][26][27]. Aided by the powerful feature extraction ability of the CNN model, Gu et al directly predict the artifacts in the wavelet domain from the degraded images and performed well in artifact reduction [28].…”