Real-time anticipation of organ displacement for MR-guidance of interventional procedures

B. D. Senneville, M. Ries, C. Moonen
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引用次数: 2

Abstract

Modern Magnetic Resonance Imaging (MRI) methods now allow the rapid acquisition of images with an excellent tissue contrast and high spatial resolution. Complex organ deformations can thus be estimated using image registration techniques applied to anatomical information. This opens great perspectives for the use of MRI to retroactively target an interventional procedure in mobile organs in real-time. For this purpose, both the update time and the latency of the motion information are two key points. In the current paper, the organ deformation is estimated on a voxel-by-voxel basis and a Kalman predictor is used to compensate for the residual latency. The implementation benefitted from the parallel architecture of Graphical Processing Units (GPU) for accelerating computation times. The efficiency and the potential of the method to anticipate organ displacements in real-time was evaluated on the abdomen of twelve free-breathing volunteers. The deformation of both kidney and liver could be updated with a rate of 10 Hz over sustained periods of several minutes, and the employed Kalman predictor reduced the tracking error in average by 30%.
实时预测器官位移用于介入手术的mr指导
现代磁共振成像(MRI)方法现在可以快速获取具有优异组织对比度和高空间分辨率的图像。因此,可以使用应用于解剖信息的图像配准技术来估计复杂的器官变形。这为利用MRI在移动器官中实时追溯靶向介入手术开辟了广阔的前景。为此,运动信息的更新时间和延迟时间是两个关键点。在本论文中,器官变形是在逐体素的基础上估计的,并使用卡尔曼预测器来补偿残余延迟。该实现得益于图形处理单元(GPU)的并行架构来加速计算时间。在12名自由呼吸志愿者的腹部上评估了该方法实时预测器官移位的效率和潜力。肾脏和肝脏的变形可以在持续几分钟的时间内以10赫兹的速率更新,所采用的卡尔曼预测器平均将跟踪误差降低了30%。
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