A generalized image quality improvement strategy of cone-beam CT using multiple spectral CT labels in Pix2pix GAN

Author:

Jiang Yangkang,Zhang YibaoORCID,Luo Chen,Yang Pengfei,Wang Jing,Liang Xiaokun,Zhao WeiORCID,Li Rencang,Niu TianyeORCID

Abstract

Abstract Objective. The quantitative and routine imaging capabilities of cone-beam CT (CBCT) are hindered from clinical applications due to the severe shading artifacts of scatter contamination. The scatter correction methods proposed in the literature only consider the anatomy of the scanned objects while disregarding the impact of incident x-ray energy spectra. The multiple-spectral model is in urgent need for CBCT scatter estimation. Approach. In this work, we incorporate the multiple spectral diagnostic multidetector CT labels into the pixel-to-pixel (Pix2pix) GAN to estimate accurate scatter distributions from CBCT projections acquired at various imaging volume sizes and x-ray energy spectra. The Pix2pix GAN combines the residual network as the generator and the PatchGAN as the discriminator to construct the correspondence between the scatter-contaminated projection and scatter distribution. The network architectures and loss function of Pix2pix GAN are optimized to achieve the best performance on projection-to-scatter transition. Results. The CBCT data of a head phantom and abdominal patients are applied to test the performance of the proposed method. The error of the corrected CBCT image using the proposed method is reduced from over 200 HU to be around 20 HU in both phantom and patient studies. The mean structural similarity index of the CT image is improved from 0.2 to around 0.9 after scatter correction using the proposed method compared with the MC-simulation method, which indicates a high similarity of the anatomy in the images before and after the proposed correction. The proposed method achieves higher accuracy of scatter estimation than using the Pix2pix GAN with the U-net generator. Significance. The proposed scheme is an effective solution to the multiple spectral CBCT scatter correction. The scatter-correction software using the proposed model will be available at: https://github.com/YangkangJiang/Cone-beam-CT-scatter-correction-tool.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

IOP Publishing

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. UBES: Unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT;Computers in Biology and Medicine;2024-03

2. Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs;2023 38th International Conference on Image and Vision Computing New Zealand (IVCNZ);2023-11-29

3. CBCT‐Based synthetic CT image generation using conditional denoising diffusion probabilistic model;Medical Physics;2023-08-30

4. Refinement of image quality in panoramic radiography using a generative adversarial network;Dentomaxillofacial Radiology;2023-07

5. DDHANet: Dual-Domain Hybrid Attention-Guided Network For CT Scatter Correction;2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI);2023-04-18

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