Star-Lack Josh, Sun Mingshan, Oelhafen Markus, Berkus Timo, Pavkovich John, Brehm Marcus, Arheit Marcel, Paysan Pascal, Wang Adam, Munro Peter, Seghers Dieter, Carvalho Luis Melo, Verbakel W F A R
Applied Research Laboratory, Varian Medical Systems, 3120 Hansen Way, Palo Alto, CA, 94304, USA.
Imaging Laboratory, Varian Medical Systems, Tafernstrasse 7, CH-5405, Baden-Dattwil, Switzerland.
Med Phys. 2018 Jun 5. doi: 10.1002/mp.13034.
Four-dimensional (4D) cone-beam computed tomography (CBCT) of the lung is an effective tool for motion management in radiotherapy but presents a challenge because of slow gantry rotation times. Sorting the individual projections by breathing phase and using an established technique such as Feldkamp-Davis-Kress (FDK) to generate corresponding phase-correlated (PC) three-dimensional (3D) images results in reconstructions (FDK-PC) that often contain severe streaking artifacts due to the sparse angular sampling distributions. These can be reduced by further slowing down the gantry at the expense of incurring unwanted increases in scan times and dose. A computationally efficient alternative is the McKinnon-Bates (MKB) reconstruction algorithm that has shown promise in reducing view aliasing-induced streaking but can produce ghosting artifacts that reduce contrast and impede the determination of motion trajectories. The purpose of this work was to identify and correct shortcomings in the MKB algorithm.
In the general MKB approach, a time-averaged 3D prior image is first reconstructed. The prior is then forward-projected at the same angles as the original projection data creating time-averaged reprojections. These reprojections are subsequently subtracted from the original (unblurred) projections to create motion-encoded difference projections. The difference projections are reconstructed into PC difference images that are added to the well-sampled 3D prior to create the higher quality 4D image. The cause of the ghosting in the traditional 4D MKB images was studied and traced to motion-induced streaking in the prior that, when reprojected, has the undesirable effect of re-encoding for motion in what should be a purely time-averaged reprojection. A new method, designated as the modified McKinnon-Bates (mMKB) algorithm, was developed based on destreaking the prior. This was coupled with a postprocessing 4D bilateral filter for noise suppression and edge preservation (mMKB ). The algorithms were tested with the 4D XCAT phantom using four simulated scan times (57, 60, 120, 180 s) and with two in vivo thorax studies (acquisition time of 60 and 90 s). Contrast-to-noise ratios (CNRs) of the target lesions and overall visual quality of the images were assessed.
Prior destreaking (mMKB algorithm) reduced ghosting artifacts and increased CNRs for all cases, with the biggest impacts seen in the end inhale (EI) and end exhale (EE) phases of the respiratory cycle. For the XCAT phantom, mMKB lesion CNR was 44% higher than the MKB lesion CNR and was 81% higher than the FDK-PC lesion CNR (EI and EE phases). The bilateral filter provided a further average CNR improvement of 87% with the highest increases associated with longer scan times. Across all phases and scan times, the maximum mMKB -to-FDK-PC CNR improvement was over 300%. In vivo results agreed with XCAT results. Significantly less ghosting was observed throughout the mMKB images including near the lesions-of-interest and the diaphragm allowing for, in one case, visualization of a small tumor with nearly 30 mm of motion. The maximum FDK-PC-to-MKB CNR improvement for Patient 1's lesion was 261% and for Patient 2's lesion was 318%.
The 4D mMKB algorithm yields good quality coronal and sagittal images in the thorax that may provide sufficient information for patient verification.
肺部四维（4D）锥形束计算机断层扫描（CBCT）是放射治疗中运动管理的有效工具，但由于机架旋转时间较慢而带来挑战。按呼吸相位对各个投影进行排序，并使用诸如费尔德坎普-戴维斯-克雷斯（FDK）等既定技术生成相应的相位相关（PC）三维（3D）图像，会导致重建图像（FDK-PC）由于稀疏的角度采样分布而经常包含严重的条纹伪影。可以通过进一步减慢机架速度来减少这些伪影，但这会导致扫描时间和剂量不必要地增加。一种计算效率高的替代方法是麦金农-贝茨（MKB）重建算法，该算法在减少视图混叠引起的条纹方面显示出前景，但可能会产生重影伪影，降低对比度并阻碍运动轨迹的确定。这项工作的目的是识别并纠正MKB算法中的缺点。
在一般的MKB方法中，首先重建一个时间平均的3D先验图像。然后将该先验图像以与原始投影数据相同的角度进行正向投影，创建时间平均的重投影。随后从原始（未模糊）投影中减去这些重投影，以创建运动编码的差异投影。将差异投影重建为PC差异图像，并将其添加到采样良好的3D先验图像中，以创建更高质量的4D图像。研究了传统4D MKB图像中重影的原因，并追溯到先验图像中运动引起的条纹，当对其进行重投影时，会在应该是纯时间平均重投影的图像中产生不希望的运动重新编码效果。基于对先验图像进行去条纹处理，开发了一种新方法，称为改进的麦金农-贝茨（mMKB）算法。这与用于噪声抑制和边缘保留的后处理4D双边滤波器相结合（mMKB ）。使用4D XCAT体模，在四个模拟扫描时间（57、60、120、180秒）下以及两项体内胸部研究（采集时间为60和90秒）中对算法进行了测试。评估了目标病变的对比度噪声比（CNR）和图像的整体视觉质量。
先验去条纹处理（mMKB算法）在所有情况下均减少了重影伪影并提高了CNR，在呼吸周期的吸气末（EI）和呼气末（EE）阶段影响最大。对于XCAT体模，mMKB病变CNR比MKB病变CNR高44%，比FDK-PC病变CNR高81%（EI和EE阶段）。双边滤波器使平均CNR进一步提高了87%，扫描时间越长，提高幅度越大。在所有阶段和扫描时间内，mMKB与FDK-PC的最大CNR改善超过300%。体内结果与XCAT结果一致。在整个mMKB图像中，包括在感兴趣病变和膈肌附近，观察到的重影明显减少，在一个病例中，能够可视化一个运动近30毫米的小肿瘤。患者1病变的FDK-PC与MKB的最大CNR改善为261%，患者2病变的为318%。
4D mMKB算法在胸部产生高质量冠状面和矢状面图像，可为患者验证提供足够信息。
