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首页> 外文期刊>Journal of applied clinical medical physics / >Development of a robust MRI fiducial system for automated fusion of MR‐US abdominal images
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Development of a robust MRI fiducial system for automated fusion of MR‐US abdominal images

机译:开发强大的MRI基准系统以自动融合MR-US腹部图像

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摘要

We present the development of a two‐component magnetic resonance (MR) fiducial system, that is, a fiducial marker device combined with an auto‐segmentation algorithm, designed to be paired with existing ultrasound probe tracking and image fusion technology to automatically fuse MR and ultrasound (US) images. The fiducial device consisted of four ~6.4 mL cylindrical wells filled with 1 g/L copper sulfate solution. The algorithm was designed to automatically segment the device in clinical abdominal MR images. The algorithm's detection rate and repeatability were investigated through a phantom study and in human volunteers. The detection rate was 100% in all phantom and human images. The center‐of‐mass of the fiducial device was robustly identified with maximum variations of 2.9 mm in position and 0.9° in angular orientation. In volunteer images, average differences between algorithm‐measured inter‐marker spacings and actual separation distances were 0.53 ± 0.36 mm. “Proof‐of‐concept” automatic MR‐US fusions were conducted with sets of images from both a phantom and volunteer using a commercial prototype system, which was built based on the above findings. Image fusion accuracy was measured to be within 5 mm for breath‐hold scanning. These results demonstrate the capability of this approach to automatically fuse US and MR images acquired across a wide range of clinical abdominal pulse sequences.
机译:我们介绍了一种两成分磁共振(MR)基准系统的开发,即一种结合了自动分段算法的基准标记设备,旨在与现有的超声探头跟踪和图像融合技术配对以自动融合MR和超声(美国)图像。基准装置由4个〜6.4 mL圆柱孔组成,其中装有1 g / L硫酸铜溶液。该算法旨在自动在临床腹部MR图像中分割设备。通过幻像研究和人类志愿者研究了该算法的检测率和可重复性。在所有幻像和人体图像中,检出率均为100%。可靠地确定了基准设备的质心,位置最大变化为2.9 mm,角度方向最大变化为0.9°。在志愿者图像中,算法测得的标记间间距与实际分离距离之间的平均差为0.53±0.36 mm。 “概念验证”自动MR-US融合是使用基于上述发现构建的商业原型系统,使用幻像和志愿者的图像集进行的。屏气扫描的图像融合精度测得在5 mm以内。这些结果证明了这种方法能够自动融合在广泛的临床腹部脉冲序列中采集的US和MR图像。

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