Tao Qian, Ipek Esra Gucuk, Shahzad Rahil, Berendsen Floris F, Nazarian Saman, van der Geest Rob J
LKEB, Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Department of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
J Magn Reson Imaging. 2016 Aug;44(2):346-54. doi: 10.1002/jmri.25148. Epub 2016 Jan 11.
To realize objective atrial scar assessment, this study aimed to develop a fully automatic method to segment the left atrium (LA) and pulmonary veins (PV) from late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI). The extent and distribution of atrial scar, visualized by LGE-MRI, provides important information for clinical treatment of atrial fibrillation (AF) patients.
Forty-six AF patients (age 62 ± 8, 14 female) who underwent cardiac MRI prior to RF ablation were included. A contrast-enhanced MR angiography (MRA) sequence was acquired for anatomy assessment followed by an LGE sequence for LA scar assessment. A fully automatic segmentation method was proposed consisting of two stages: 1) global segmentation by multiatlas registration; and 2) local refinement by 3D level-set. These automatic segmentation results were compared with manual segmentation.
The LA and PVs were automatically segmented in all subjects. Compared with manual segmentation, the method yielded a surface-to-surface distance of 1.49 ± 0.65 mm in the LA region when using both MRA and LGE, and 1.80 ± 0.93 mm when using LGE alone (P < 0.05). In the PV regions, the distance was 2.13 ± 0.67 mm and 2.46 ± 1.81 mm (P < 0.05), respectively. The difference between automatic and manual segmentation was comparable to the interobserver difference (P = 0.8 in LA region and P = 0.7 in PV region).
We developed a fully automatic method for LA and PV segmentation from LGE-MRI, with comparable performance to a human observer. Inclusion of an MRA sequence further improves the segmentation accuracy. The method leads to automatic generation of a patient-specific model, and potentially enables objective atrial scar assessment for AF patients. J. Magn. Reson. Imaging 2016;44:346-354.
为实现客观的心房瘢痕评估,本研究旨在开发一种全自动方法,用于从延迟钆增强(LGE)磁共振成像(MRI)中分割左心房(LA)和肺静脉(PV)。LGE-MRI显示的心房瘢痕范围和分布为心房颤动(AF)患者的临床治疗提供重要信息。
纳入46例在射频消融术前接受心脏MRI检查的AF患者(年龄62±8岁,14例女性)。先采集对比增强磁共振血管造影(MRA)序列进行解剖结构评估,然后采集LGE序列评估LA瘢痕。提出了一种全自动分割方法,包括两个阶段:1)通过多图谱配准进行全局分割;2)通过三维水平集进行局部细化。将这些自动分割结果与手动分割结果进行比较。
所有受试者的LA和PV均被自动分割。与手动分割相比,该方法在同时使用MRA和LGE时,LA区域的表面到表面距离为1.49±0.65mm,单独使用LGE时为1.80±0.93mm(P<0.05)。在PV区域,距离分别为2.13±0.67mm和2.46±1.81mm(P<0.05)。自动分割与手动分割之间的差异与观察者间差异相当(LA区域P=0.8,PV区域P=0.7)。
我们开发了一种从LGE-MRI中全自动分割LA和PV的方法,其性能与人类观察者相当。纳入MRA序列进一步提高了分割准确性。该方法可自动生成患者特异性模型,并有可能实现对AF患者的客观心房瘢痕评估。《磁共振成像杂志》2016年;44:346 - 354。