Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
Int J Radiat Oncol Biol Phys. 2015 Mar 15;91(4):840-8. doi: 10.1016/j.ijrobp.2014.12.013.
This study applied automatic feature detection on cine-magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy.
In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error.
An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57 mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance.
Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.
本研究应用电影磁共振成像（MRI）肝脏图像的自动特征检测，以便在 MRI 引导和基于替代物的跟踪方法之间进行前瞻性比较，用于运动补偿肝脏放射治疗。
在 30 名受试者（5 名志愿者加 25 名患者）的人群中，在高时间分辨率下采集穿过肝脏的 2 个斜矢状切片。基于尺度不变特征变换（SIFT）的算法用于在整个图像序列中提取和跟踪多个特征。还直接从图像序列中测量腹部标记的位置，并通过多参数分析量化每个特征的内部运动。模拟了具有最先进的外部/内部相关模型的基于替代物的肿瘤跟踪。测量了几何跟踪误差，并研究了其与外部运动参数的相关性。最后，作为最大允许跟踪误差的函数，量化了依赖于 MRI 引导的跟踪精度的潜在增益。
在整个肝脏中，每个受试者平均提取了 45 个特征。多参数运动分析报告了相关的受试者间和受试者内变异性，突出了患者特异性和空间分布测量的价值。基于替代物的跟踪误差（相对于运动幅度）在 7%至 23%（1.02-3.57 毫米）范围内，并且受到外部运动参数的显著影响。当考虑 1.5 毫米的跟踪误差容限时，与基于替代物的运动跟踪相比，MRI 引导的跟踪增益在 50%的受试者中大于 30%。
应用于电影 MRI 的自动特征检测允许获得详细的肝脏运动描述。该信息用于量化基于替代物的跟踪方法的性能，并与 MRI 引导的放射治疗进行前瞻性比较，这可以支持为患者制定最佳治疗策略。
