Department of Radiology, University of Chicago, Chicago, Illinois 60637, USA.
Med Phys. 2010 Jan;37(1):32-9. doi: 10.1118/1.3263618.
Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation.
The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step.
The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts.
They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.
反向螺旋锥束 CT（CBCT）是一种扫描配置，可应用于图像引导放射治疗，在该治疗中需要对患者进行准确的解剖成像，以进行图像引导程序。作者之前开发了一种从完全在反向螺旋内的物体的非截断数据进行图像重建的算法。本研究旨在开发一种针对超出反向螺旋的长物体的反向螺旋 CBCT 的图像重建方法，从而导致数据截断。
所提出的方法包括两个重建步骤。在第一步中，基于弦的反向投影-滤波（BPF）算法从原始锥束数据重建物体的体积图像。由于反向螺旋的中间存在无弦区域，因此在第一步中获得的图像包含未重建的中心间隙区域。在第二步中，通过使用基于 Pack-Noo 公式的滤波反投影（FBP）算法，从通过从原始锥束数据减去第一步中重建的图像的重新投影获得的修改后的锥束数据中重建间隙区域。
作者已经进行了数值研究，以验证从反向螺旋锥束数据进行图像重建中所提出方法的有效性。结果证实，所提出的方法可以在没有数据截断伪影或锥角伪影的情况下准确重建长物体的图像。
他们开发并验证了一种 BPF-FBP 串联算法，用于从反向螺旋锥束数据重建长物体的图像。基于弦的 BPF 算法用于将长物体问题转换为短物体问题。所提出的方法适用于其他扫描配置，例如简化的圆形正弦轨迹。
