基于子空间识别的低剂量CT重建投影域处理。

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Junru Ren, Ningning Liang, Xiaohuan Yu, Yizhong Wang, Ailong Cai, Lei Li, Bin Yan
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引用次数: 0

摘要

目的:低剂量计算机断层扫描(LDCT)在医学应用中具有降低剂量的潜力,但存在噪声导致图像质量低的问题。因此,迫切需要开发新的算法来获得高质量的LDCT图像。方法:利用图像的稀疏性和低秩性,提出一种基于子空间识别的新算法。用奇异值分解稀疏表示传输数据集合,然后用块匹配帧去噪特征图像。然后在先验图像压缩感知(PICCS)框架下,利用相关信息对投影进行正则化;在处理后的投影上应用典型的解析算法,得到目标图像。通过数值模拟和实测数据验证了该算法的有效性。数值模拟数据是基于真实的临床扫描三维数据得到的,真实数据是通过扫描实验头部幻影得到的。结果:在仿真实验中,与方差为0.04的高斯噪声下的BM3D算法相比,新算法的PSNR和SSIM均值分别提高了1 dB和0.05。同时,在实际数据上,该算法在噪声抑制、细节保留和计算开销方面都优于同类算法。在方差为0.04的高斯噪声下,与BM3D相比,PSNR和SSIM均值分别提高了1.84 dB和0.1 dB。结论:本研究证明了一种基于子空间识别的LDCT新算法的可行性和优越性。它利用三维数据之间的相似性,以简洁的方式提高图像质量,在未来的临床诊断中显示出很好的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Projection domain processing for low-dose CT reconstruction based on subspace identification.

Purpose: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT.

Methods: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom.

Results: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04.

Conclusion: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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