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基于三维伪极傅里叶变换的快速三维压缩感知方法实现低剂量锥束计算机断层扫描重建

Low-dose Cone-Beam Computed Tomography Reconstruction through a fast Three-Dimensional Compressed Sensing Method Based on the Three-Dimensional Pseudo-polar Fourier Transform.

作者信息

Teyfouri N, Rabbani Hossein, Jabbari I

机构信息

Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Department of Nuclear Engineering, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran.

出版信息

J Med Signals Sens. 2021 Dec 28;12(1):8-24. doi: 10.4103/jmss.jmss_114_21. eCollection 2022 Jan-Mar.

Abstract

BACKGROUND

Reconstruction of high quality two dimensional images from fan beam computed tomography (CT) with a limited number of projections is already feasible through Fourier based iterative reconstruction method. However, this article is focused on a more complicated reconstruction of three dimensional (3D) images in a sparse view cone beam computed tomography (CBCT) by utilizing Compressive Sensing (CS) based on 3D pseudo polar Fourier transform (PPFT).

METHOD

In comparison with the prevalent Cartesian grid, PPFT re gridding is potent to remove rebinning and interpolation errors. Furthermore, using PPFT based radon transform as the measurement matrix, reduced the computational complexity.

RESULTS

In order to show the computational efficiency of the proposed method, we compare it with an algebraic reconstruction technique and a CS type algorithm. We observed convergence in <20 iterations in our algorithm while others would need at least 50 iterations for reconstructing a qualified phantom image. Furthermore, using a fast composite splitting algorithm solver in each iteration makes it a fast CBCT reconstruction algorithm. The algorithm will minimize a linear combination of three terms corresponding to a least square data fitting, Hessian (HS) Penalty and l1 norm wavelet regularization. We named it PP-based compressed sensing-HS-W. In the reconstruction range of 120 projections around the 360° rotation, the image quality is visually similar to reconstructed images by Feldkamp-Davis-Kress algorithm using 720 projections. This represents a high dose reduction.

CONCLUSION

The main achievements of this work are to reduce the radiation dose without degrading the image quality. Its ability in removing the staircase effect, preserving edges and regions with smooth intensity transition, and producing high-resolution, low-noise reconstruction results in low-dose level are also shown.

摘要

背景

通过基于傅里叶的迭代重建方法,从有限数量投影的扇形束计算机断层扫描(CT)中重建高质量二维图像已经可行。然而,本文重点关注利用基于三维伪极傅里叶变换(PPFT)的压缩感知(CS),在稀疏视图锥束计算机断层扫描(CBCT)中进行更复杂的三维(3D)图像重建。

方法

与普遍使用的笛卡尔网格相比,PPFT重新网格化能够有效消除重采样和插值误差。此外,使用基于PPFT的拉东变换作为测量矩阵,降低了计算复杂度。

结果

为了展示所提方法的计算效率,我们将其与代数重建技术和一种CS类型算法进行比较。我们观察到我们的算法在少于20次迭代中收敛,而其他算法重建合格的体模图像至少需要50次迭代。此外,在每次迭代中使用快速复合分裂算法求解器使其成为一种快速的CBCT重建算法。该算法将最小化对应于最小二乘数据拟合、海森(HS)惩罚和l1范数小波正则化的三项线性组合。我们将其命名为基于PP的压缩感知-HS-W。在360°旋转周围120个投影的重建范围内,图像质量在视觉上与使用720个投影的Feldkamp-Davis-Kress算法重建的图像相似。这代表了高剂量降低。

结论

这项工作的主要成果是在不降低图像质量的情况下降低辐射剂量。还展示了其消除阶梯效应、保留边缘以及强度过渡平滑区域的能力,以及在低剂量水平下产生高分辨率、低噪声重建结果的能力。

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