网(多面体)
编码孔径
锥束ct
梁(结构)
光圈(计算机存储器)
Cone(正式语言)
光学
生成语法
计算机断层摄影术
对抗制
计算机科学
生成对抗网络
断层摄影术
物理
人工智能
图像(数学)
算法
几何学
探测器
数学
声学
放射科
医学
作者
Tianyi Mao,Liang Lei,Ch. Y. Gao,Chenguang Bian,Dongmiao Wang,Shujin Zhu,Xiubin Dai
标识
DOI:10.1088/1402-4896/ad76e6
摘要
Abstract Coded aperture cone-beam computed tomography (CBCT) represents a crucial method for acquiring high-fidelity three-dimensional (3D) tomographic images while reducing radiation exposure. However, projections are non-uniformly and discontinuously sampled with the coded apertures placed in front of the X-ray source, leading to very small reconstruction scale and time-intensive iterations. In this study, an alternative approach to reconstruct coded aperture CBCT based on generative adversarial U-net is proposed to effectively and efficiently reconstruct large scale tomographic images. Our method entails predicting complete and uniform projections from incomplete and non-uniform coded projections, enabling the requirement of continuity for the use of analytical algorithms in 3D image reconstruction. This novel technique effectively mitigates the traditional trade-off between image fidelity and computational complexity inherent in conventional coded aperture CBCT reconstruction methods. Our experimental results, conducted using clinical datasets comprising CBCT images from 102 patients at Nanjing Medical University, demonstrate that high-quality CBCT images with voxel dimensions of $400 \times 400 \times 400$ can be reconstructed within 35 seconds, even when 95\% of projections are blocked, yielding tomographic images with PSNR values exceeding 25dB and SSIM values surpassing 0.85.
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