Shieh Chun-Chien, Kipritidis John, O'Brien Ricky T, Cooper Benjamin J, Kuncic Zdenka, Keall Paul J
Radiation Physics Laboratory, Sydney Medical School, The University of Sydney, NSW 2006, Australia. Institute of Medical Physics, School of Physics, The University of Sydney, NSW 2006, Australia.
Phys Med Biol. 2015 Jan 21;60(2):841-68. doi: 10.1088/0031-9155/60/2/841. Epub 2015 Jan 7.
Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp-Davis-Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and did not suffer from residual noise/streaking and motion blur migrated from the prior image as in PICCS. AAIR was also found to be more computationally efficient than both ASD-POCS and PICCS, with a reduction in computation time of over 50% compared to ASD-POCS. The use of anatomy segmentation was, for the first time, demonstrated to significantly improve image quality and computational efficiency for thoracic 4D CBCT reconstruction. Further developments are required to facilitate AAIR for practical use.
与目前实际应用的Feldkamp-Davis-Kress（FDK）算法相比，全变差（TV）最小化重建可以显著减少胸部四维锥束计算机断层扫描（4D CBCT）图像中的噪声和条纹。然而，TV最小化重建容易过度平滑解剖细节，并且计算效率也很低。本研究的目的是证明一个概念验证，即通过将胸部解剖学的一般知识通过解剖分割纳入重建中，可以克服这些缺点。所提出的方法，称为解剖自适应图像正则化（AAIR）方法，利用自适应最速下降投影到凸集（ASD-POCS）框架，但在每次迭代中引入了一个额外的解剖分割步骤。解剖分割信息在重建中使用启发式方法实现，以自适应抑制感兴趣解剖结构处的过度平滑。AAIR的性能取决于描述解剖分割先验权重和分割阈值的参数。一项敏感性研究表明，只要在合适的范围内选择这些参数，重建结果对它们并不敏感。使用数字体模和患者扫描对AAIR进行了验证，并与FDK、ASD-POCS和先验图像约束压缩感知（PICCS）方法进行了比较。对于体模情况，平均绝对差和结构相似性指数表明，AAIR重建在定量上是最准确的。对于患者情况，AAIR产生了最高的信噪比（即最低水平的噪声和条纹）以及肿瘤和骨骼解剖结构的最高对比噪声比（即解剖细节的最佳可见性）。总体而言，与ASD-POCS相比，AAIR不太容易过度平滑解剖细节，并且不像PICCS那样受到从前一图像迁移来的残留噪声/条纹和运动模糊的影响。还发现AAIR在计算上比ASD-POCS和PICCS都更有效，与ASD-POCS相比，计算时间减少了50%以上。首次证明使用解剖分割可以显著提高胸部4D CBCT重建的图像质量和计算效率。需要进一步发展以促进AAIR的实际应用。
