Benchmarking framework for myocardial tracking and deformation algorithms: An open access database
Benchmarking framework for myocardial tracking and deformation algorithms: An open access database
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DOI：
10.1016/j.media.2013.03.008
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发表时间：
2013-08-01
影响因子：
10.9
通讯作者：
Rhode, K. S.
中科院分区：
文献类型：
--
作者：
Tobon-Gomez, C.;De Craene, M.;Rhode, K. S.
在本文中，我们提出了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讲习班向医学成像界发出的公开挑战的回应。该数据库包括来自动态幻影和15名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标记MR数据集（3DTAG）， cine稳态自由进动MR数据集（SSFP）和3DUS数据集，共计1158个图像体积。运动跟踪的基本事实是基于12个地标（3个心室水平的4个壁）。在整个心脏周期中，他们由两名观察者在3DTAG数据中手动跟踪，使用具有4D可视化功能的内部应用程序。计算了模拟数据集（0.77 mm）和志愿者数据集（0.84 mm）的观察者间变异性的中位数。使用基于点的相似度变换将ground truth注册到3DUS坐标。四家机构通过对数据进行运动估计来应对这一挑战：德国不来梅的Fraunhofer MEVIS (MEVIS)；帝国理工学院-伦敦大学学院（IUCL），英国；西班牙巴塞罗那庞培法布拉大学（UPF）；法国INRIA - asclepios项目（INRIA）。这四种方法的实施和评估的细节在这份手稿中提出。用人工跟踪的地标来评估所有方法的跟踪精度。对于3DTAG，计算了幻影数据集（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 mm, IUCL = 1.52 mm, UPF = 1.09 mm, INRIA = 1.32 mm）在所有时间框架内的中位数。对于3DUS，幻影数据集（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 mm, INRIA = 4.07 mm）在舒张末期和收缩期末计算中位数。对于SSFP，在虚拟数据集（UPF = 6.18 mm, INRIA = 3.93 mm）和志愿者数据集（UPF = 3.09 mm, INRIA = 4.78 mm）的舒张末期和收缩期结束时计算中值。最后生成应变曲线并进行定性比较。除了在图像质量较低的情况下显示出高度可变性的径向应变外，在不同的模态和方法之间发现了良好的一致性。(C) 2013 Elsevier B.V.版权所有
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. (C) 2013 Elsevier B.V. All rights reserved.