Velazco Garcia Jose D, Navkar Nikhil V, Gui Dawei, Morales Cristina M, Christoforou Eftychios G, Ozcan Alpay, Abinahed Julien, Al-Ansari Abdulla, Webb Andrew, Seimenis Ioannis, Tsekos Nikolaos V
Department of Computer Science, University of Houston, Houston, TX, USA.
Department of Surgery, Hamad Medical Corporation, Doha, Qatar.
J Digit Imaging. 2019 Jun;32(3):420-432. doi: 10.1007/s10278-018-0152-1.
This work presents a platform that integrates a customized MRI data acquisition scheme with reconstruction and three-dimensional (3D) visualization modules along with a module for controlling an MRI-compatible robotic device to facilitate the performance of robot-assisted, MRI-guided interventional procedures. Using dynamically-acquired MRI data, the computational framework of the platform generates and updates a 3D model representing the area of the procedure (AoP). To image structures of interest in the AoP that do not reside inside the same or parallel slices, the MRI acquisition scheme was modified to collect a multi-slice set of intraoblique to each other slices; which are termed composing slices. Moreover, this approach interleaves the collection of the composing slices so the same k-space segments of all slices are collected during similar time instances. This time matching of the k-space segments results in spatial matching of the imaged objects in the individual composing slices. The composing slices were used to generate and update the 3D model of the AoP. The MRI acquisition scheme was evaluated with computer simulations and experimental studies. Computer simulations demonstrated that k-space segmentation and time-matched interleaved acquisition of these segments provide spatial matching of the structures imaged with composing slices. Experimental studies used the platform to image the maneuvering of an MRI-compatible manipulator that carried tubing filled with MRI contrast agent. In vivo experimental studies to image the abdomen and contrast enhanced heart on free-breathing subjects without cardiac triggering demonstrated spatial matching of imaged anatomies in the composing planes. The described interventional MRI framework could assist in performing real-time MRI-guided interventions.
这项工作展示了一个平台,该平台将定制的MRI数据采集方案与重建及三维(3D)可视化模块集成在一起,同时还有一个用于控制MRI兼容机器人设备的模块,以促进机器人辅助的MRI引导介入手术的实施。利用动态采集的MRI数据,该平台的计算框架生成并更新一个代表手术区域(AoP)的3D模型。为了对不在同一或平行切片内的AoP中的感兴趣结构进行成像,对MRI采集方案进行了修改,以收集相互成斜角的多切片集;这些切片被称为组成切片。此外,这种方法交错采集组成切片,以便在相似的时间实例期间采集所有切片的相同k空间段。k空间段的这种时间匹配导致各个组成切片中成像对象的空间匹配。组成切片用于生成和更新AoP的3D模型。通过计算机模拟和实验研究对MRI采集方案进行了评估。计算机模拟表明,k空间分割和这些段的时间匹配交错采集可实现用组成切片成像的结构的空间匹配。实验研究使用该平台对携带装有MRI造影剂的管道的MRI兼容操纵器的操作进行成像。在无心脏触发的自由呼吸受试者身上对腹部和对比增强心脏进行成像的体内实验研究表明,组成平面中成像解剖结构的空间匹配。所描述的介入MRI框架可协助进行实时MRI引导的干预。