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Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

机译：心肌追踪和变形算法的基准框架：一个开放式数据库

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摘要

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 = 1.20. mm, IUCL = 0.73. mm, UPF = 1.10. mm, 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.
机译：在本文中，我们提出了用于验证心脏运动分析算法的基准框架。报告的方法是对通过MICCAI研讨会向医学影像界发出的公开挑战的回应。该数据库包括来自动态体模和15位健康志愿者的磁共振（MR）和3D超声（3DUS）数据集。参与者处理了3D标签MR数据集（3DTAG），电影稳态无进动MR数据集（SSFP）和3DUS数据集，总计1158个图像量。用于运动跟踪的地面真相基于12个地标（3个心室水平的4面墙）。使用具有4D可视化功能的内部应用程序，由两个观察员在整个心动周期的3DTAG数据中对其进行手动跟踪。计算了幻像数据集（0.77.mm）和志愿者数据集（0.84.mm）的观察者间变异性的中值。使用基于点的相似度转换将真实情况注册到3DUS坐标。四个机构通过提供数据的运动估计值来应对这一挑战：德国不来梅的弗劳恩霍夫MEVIS（MEVIS）;伦敦帝国学院-英国伦敦大学学院（IUCL）;西班牙巴塞罗那庞培法布拉大学（UPF）;法国Inria-Asclepios项目（INRIA）。本手稿中介绍了这四种方法的实施和评估的详细信息。手动跟踪的地标用于评估所有方法的跟踪准确性。对于3DTAG，对于幻像数据集（MEVIS = 1.20。mm，IUCL = 0.73。mm，UPF = 1.10。mm，INRIA = 1.09。mm）和志愿者数据集（MEVIS = 1.33。）在所有时间范围内均计算中值。毫米，IUCL = 1.52毫米，UPF = 1.09。毫米，INRIA = 1.32。毫米）。对于3DUS，对于幻像数据集（MEVIS = 4.40。mm，UPF = 3.48。mm，INRIA = 4.78。mm）和志愿者数据集（MEVIS = 3.51。mm，UPF = 3.71。毫米，INRIA = 4.07。毫米）。对于SSFP，在幻影数据集（UPF = 6.18。mm，INRIA = 3.93。mm）和志愿者数据集（UPF = 3.09。mm，INRIA = 4.78。mm）的舒张末期和收缩末期计算中值。最后，生成应变曲线并进行定性比较。在不同的模式和方法之间找到了很好的一致性，除了径向应变在较低图像质量的情况下显示出高可变性。

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