O'Shea Tuathan P, Bamber Jeffrey C, Harris Emma J
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS foundation Trust, Sutton, London SM2 5PT, United Kingdom.
Med Phys. 2016 Jan;43(1):455. doi: 10.1118/1.4938582.
Ultrasound-based motion estimation is an expanding subfield of image-guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation-based template matching which aims to improve the accuracy of motion estimates.
Liver ultrasound sequences (15-23 Hz imaging rate, 2.5-5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison with normalized cross-correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α-β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set).
Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively.
Ultrasound-based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking performance. A future study will investigate spatial uniformity of motion and its effect on the motion estimation errors.
基于超声的运动估计是图像引导放射治疗中一个不断发展的子领域。尽管超声能够检测到仅为毫米级的组织运动，但其准确性存在差异。为了控制直线加速器的跟踪和门控，超声运动估计在整个成像序列中必须保持高度准确。本研究提出了一种基于相关的模板匹配的时间正则化方法，旨在提高运动估计的准确性。
使用了来自10名自由呼吸状态下健康志愿者的肝脏超声序列（成像速率为15 - 23 Hz，时长为2.5 - 5.5分钟）。对每个序列中的解剖特征（血管）进行手动标注，以便与基于归一化互相关的模板匹配进行比较。来自西门子Acuson™扫描仪的5个序列用于算法开发（训练集）。将增量跟踪（IT）的结果与一种时间正则化方法进行比较，该方法包括一个高度特定的相似性度量和状态观测器，即α-β滤波器/相似性阈值（ABST）。来自医科达Clarity™系统的另外5个序列用于验证，跟踪算法不做改动（验证集）。
总体而言，ABST方法在血管跟踪准确性方面有显著提高。对于训练集，平均误差和第95百分位数（95%）误差（定义为与手动标注的差异）分别为1.6毫米和1.4毫米（相比之下，IT方法分别为6.2毫米和9.1毫米）。对于每个序列，使用状态观测器可使第95%误差得到改善。对于验证集，ABST方法的平均误差和95%误差分别为0.8毫米和1.5毫米。
基于超声的运动估计有潜力长时间高精度地监测肝脏平移。非刚性运动（应变）和超声数据质量可能会对跟踪性能产生影响。未来的研究将调查运动的空间均匀性及其对运动估计误差的影响。
