TMAA-net: tensor-domain multi-planal anti-aliasing network for sparse-view CT image reconstruction.

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2024-11-12 DOI:10.1088/1361-6560/ad8da2
Sungho Yun, Seoyoung Lee, Da-In Choi, Taewon Lee, Seungryong Cho
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Abstract

Objective.Among various deep-network-based sparse-view CT image reconstruction studies, the sinogram upscaling network has been predominantly employed to synthesize additional view information. However, the performance of the sinogram-based network is limited in terms of removing aliasing streak artifacts and recovering low-contrast small structures. In this study, we used a view-by-view back-projection (VVBP) tensor-domain network to overcome such limitations of the sinogram-based approaches.Approach.The proposed method offers advantages of addressing the aliasing artifacts directly in the 3D tensor domain over the 2D sinogram. In the tensor-domain network, the multi-planal anti-aliasing modules were used to remove artifacts within the coronal and sagittal tensor planes. In addition, the data-fidelity-based refinement module was also implemented to successively process output images of the tensor network to recover image sharpness and textures.Main result.The proposed method showed outperformance in terms of removing aliasing artifacts and recovering low-contrast details compared to other state-of-the-art sinogram-based networks. The performance was validated for both numerical and clinical projection data in a circular fan-beam CT configuration.Significance.We observed that view-by-view aliasing artifacts in sparse-view CT exhibit distinct patterns within the tensor planes, making them effectively removable in high-dimensional representations. Additionally, we demonstrated that the co-domain characteristics of tensor space processing offer higher generalization performance for aliasing artifact removal compared to conventional sinogram-domain processing.

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TMAA-net:用于稀疏视图 CT 图像重建的张量域多平面抗锯齿网络。
在各种基于深度网络的稀疏视图 CT 图像重建研究中,主要采用正弦波上标网络来合成额外的视图信息。然而,基于正弦图的网络在去除混叠条纹伪影和恢复低对比度的小结构方面性能有限。在本研究中,我们使用了逐视图反向投影(VVBP)张量域网络来克服基于正弦图的方法的这些局限性。与二维正弦图相比,所提出的方法具有直接在三维张量域中处理混叠伪影的优势。此外,还实现了基于数据保真度的细化模块,对张量网络的输出图像进行连续处理,以恢复图像的清晰度和纹理。与其他最先进的基于正弦图的网络相比,所提出的方法在消除混叠伪影和恢复低对比度细节方面表现出色。在环形扇形光束 CT 配置中,数值和临床投影数据都验证了该方法的性能。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
期刊最新文献
Noise & mottle suppression methods for cumulative Cherenkov images of radiation therapy delivery. Quantitative assessment of areal bone mineral density using multi-energy localizer radiographs from photon-counting detector CT. TMAA-net: tensor-domain multi-planal anti-aliasing network for sparse-view CT image reconstruction. Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation. Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging.
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