CISTIB, Universitat Pompeu Fabra, Barcelona, Spain.
Med Image Anal. 2013 Aug;17(6):632-48. doi: 10.1016/j.media.2013.03.008. Epub 2013 Apr 20.
In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was issued to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77 mm) and for the volunteer datasets (0.84 mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London - University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73 mm, UPF=1.10mm, INRIA=1.09 mm) and for the volunteer datasets (MEVIS=1.33 mm, IUCL=1.52 mm, UPF=1.09 mm, INRIA=1.32 mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40 mm, UPF=3.48 mm, INRIA=4.78 mm) and for the volunteer datasets (MEVIS=3.51 mm, UPF=3.71 mm, INRIA=4.07 mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset(UPF=6.18 mm, INRIA=3.93 mm) and for the volunteer datasets (UPF=3.09 mm, INRIA=4.78 mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.
在本文中，我们提出了一个用于验证心脏运动分析算法的基准框架。所报告的方法是对通过 MICCAI 研讨会向医学成像界发布的公开挑战的回应。该数据库包括动态体模和 15 名健康志愿者的磁共振（MR）和三维超声（3DUS）数据集。参与者处理了 3 个标记的 MR 数据集（3DTAG）、电影稳态自由进动 MR 数据集（SSFP）和 3DUS 数据集，共计 1158 个图像卷。运动跟踪的地面实况基于 12 个标记点（3 个心室水平的 4 个壁）。它们由两名观察者在整个心脏周期内在 3DTAG 数据中使用具有 4D 可视化功能的内部应用程序手动跟踪。计算了体模数据集（0.77 毫米）和志愿者数据集（0.84 毫米）的观察者间变异性中位数。地面实况使用基于点的相似变换注册到 3DUS 坐标。四个机构通过提供数据的运动估计来响应挑战：德国弗劳恩霍夫 MEVIS（MEVIS）；英国伦敦帝国学院 - 伦敦大学学院（IUCL）；西班牙庞培法布拉大学（UPF）；法国 Inria-Asclepios 项目（INRIA）。本文介绍了这四种方法的实施和评估的详细信息。手动跟踪的标记点用于评估所有方法的跟踪精度。对于 3DTAG，在体模数据集（MEVIS=1.20mm，IUCL=0.73mm，UPF=1.10mm，INRIA=1.09mm）和志愿者数据集（MEVIS=1.33mm，IUCL=1.52mm，UPF=1.09mm，INRIA=1.32mm）上计算了所有时间帧的中位数。对于 3DUS，在体模数据集（MEVIS=4.40mm，UPF=3.48mm，INRIA=4.78mm）和志愿者数据集（MEVIS=3.51mm，UPF=3.71mm，INRIA=4.07mm）上计算了舒张末期和收缩末期的中位数。对于 SSFP，在体模数据集（UPF=6.18mm，INRIA=3.93mm）和志愿者数据集（UPF=3.09mm，INRIA=4.78mm）上计算了舒张末期和收缩末期的中位数。最后，生成了应变曲线并进行了定性比较。除了图像质量较低的情况下径向应变显示出较高的变异性外，不同模态和方法之间发现了很好的一致性。
