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High-resolution coded aperture optimization for super-resolved compressive x-ray cone-beam computed tomography

机译：超分辨压缩X射线锥形光束计算机断层扫描的高分辨率编码光圈优化

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摘要

Compressive x-ray cone-beam computed tomography (CBCT) approaches rely on coded apertures (CA) along multiple view angles to block a portion of the x-ray energy traveling towards the detectors. Previous work has shown that designing CA patterns yields improved images. Most designs, however, are focused on multi-shot fan-beam (FB) systems, handling a 1:1 ratio between CA features and detector elements. In consequence, image resolution is subject to the detector pixel size. Moreover, CA optimization for computed tomography involves strong binarization assumptions, impractical data rearrangements, or computationally expensive tasks such as singular value decomposition (SVD). Instead of using higher-resolution CA distributions in a multi-slice system with a more dense detector array, this work presents a method for designing the CA patterns in a compressive CBCT system under a super-resolution configuration, i.e., high-resolution CA patterns are designed to obtain high-resolution images from lower-resolution projections. The proposed method takes advantage of the Gershgorin theorem since its algebraic interpretation relates the circle radii with the eigenvalue bounds, whose minimization improves the condition of the system matrix. Simulations with medical data sets show that the proposed design attains high-resolution images from lower-resolution detectors in a single-shot CBCT scenario. Besides, image quality is improved in up to 5 dB of peak signal-to-noise compared to random CA patterns for different super-resolution factors. Moreover, reconstructions from Monte Carlo simulated projections show up to 3 dB improvements. Further, for the analyzed cases, the computational load of the proposed approach is up to three orders of magnitude lower than that of SVD-based methods. (C) 2021 Optical Society of America
机译：压缩x射线锥束计算机断层扫描（CBCT）方法依赖于沿多个视角的编码孔径（CA），以阻挡部分x射线能量流向探测器。以前的工作已经表明，设计CA模式可以产生更好的图像。然而，大多数设计都集中在多发扇束（FB）系统上，处理CA特征和探测器元件之间的1:1比率。因此，图像分辨率取决于探测器像素大小。此外，计算机断层扫描的CA优化涉及强烈的二值化假设、不切实际的数据重新排列，或计算昂贵的任务，如奇异值分解（SVD）。本文提出了一种在超分辨率配置下压缩CBCT系统中设计CA模式的方法，即高分辨率CA模式用于从低分辨率投影获得高分辨率图像，而不是在具有更密集探测器阵列的多层系统中使用更高分辨率的CA分布。该方法利用了Gershgorin定理，因为其代数解释将圆半径与特征值边界联系起来，其最小化改进了系统矩阵的条件。对医学数据集的仿真表明，在单次激发CBCT场景中，该设计可以从低分辨率探测器获得高分辨率图像。此外，对于不同的超分辨率因子，与随机CA模式相比，图像质量在高达5 dB的峰值信噪比下得到改善。此外，从蒙特卡罗模拟投影重建显示，高达3分贝的改善。此外，对于分析的情况，该方法的计算量比基于奇异值分解的方法低三个数量级。（2021）美国光学学会

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  • 来源
    《Applied optics》 |2021年第4期|共12页
  • 作者

    Mojica Edson; Correa Claudia V; Arguello Henry;

  • 作者单位

    Univ Ind Santander Dept Comp Sci Bucaramanga 680002 Colombia;

    Univ Ind Santander Dept Comp Sci Bucaramanga 680002 Colombia;

    Univ Ind Santander Dept Comp Sci Bucaramanga 680002 Colombia;

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