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使用基于先验图像约束的压缩感知(PICCS)算法从完全截断的投影中重建时间分辨的心脏介入锥形束 CT。

Time-resolved cardiac interventional cone-beam CT reconstruction from fully truncated projections using the prior image constrained compressed sensing (PICCS) algorithm.

机构信息

Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.

出版信息

Phys Med Biol. 2012 May 7;57(9):2461-76. doi: 10.1088/0031-9155/57/9/2461. Epub 2012 Apr 5.

Abstract

C-arm cone-beam CT could replace preoperative multi-detector CT scans in the cardiac interventional setting. However, cardiac gating results in view angle undersampling and the small size of the detector results in projection data truncation. These problems are incompatible with conventional tomographic reconstruction algorithms. In this paper, the prior image constrained compressed sensing (PICCS) reconstruction method was adapted to solve these issues. The performance of the proposed method was compared to that of FDK, FDK with extrapolated projection data (E-FDK), and total variation-based compressed sensing. A canine projection dataset acquired using a clinical C-arm imaging system supplied realistic cardiac motion and anatomy for this evaluation. Three different levels of truncation were simulated. The relative root mean squared error and the universal image quality index were used to quantify the reconstruction accuracy. Three main conclusions were reached. (1) The adapted version of the PICCS algorithm offered the highest image quality and reconstruction accuracy. (2) No meaningful variation in performance was observed when the amount of truncation was changed. (3) This study showed evidence that accurate interior tomography with an undersampled acquisition is possible for realistic objects if a prior image with minimal artifacts is available.

摘要

C 臂锥形束 CT 可在心脏介入环境中替代术前多探测器 CT 扫描。然而,心脏门控导致视场角欠采样,探测器的小尺寸导致投影数据截断。这些问题与传统的层析重建算法不兼容。在本文中,采用了先验图像约束压缩感知(PICCS)重建方法来解决这些问题。将所提出的方法的性能与 FDK、具有外推投影数据的 FDK(E-FDK)和基于全变差的压缩感知进行了比较。使用临床 C 臂成像系统获取的犬科投影数据集为该评估提供了真实的心脏运动和解剖结构。模拟了三种不同程度的截断。使用相对均方根误差和通用图像质量指数来量化重建准确性。得出了三个主要结论。(1) 经改编的 PICCS 算法提供了最高的图像质量和重建准确性。(2) 当截断量发生变化时,性能没有明显变化。(3) 本研究表明,如果有最小伪影的先验图像可用,则对于真实物体,使用欠采样采集进行准确的内部层析是可能的。

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