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Non-rigid registration of a 3D ultrasound and a MR image data set of the female pelvic floor using a biomechanical model

机译:使用生物力学模型对女性骨盆底的3D超声和MR图像数据集进行非刚性配准

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摘要

Background The visual combination of different modalities is essential for many medical imaging applications in the field of Computer-Assisted medical Diagnosis (CAD) to enhance the clinical information content. Clinically, incontinence is a diagnosis with high clinical prevalence and morbidity rate. The search for a method to identify risk patients and to control the success of operations is still a challenging task. The conjunction of magnetic resonance (MR) and 3D ultrasound (US) image data sets could lead to a new clinical visual representation of the morphology as we show with corresponding data sets of the female anal canal with this paper. Methods We present a feasibility study for a non-rigid registration technique based on a biomechanical model for MR and US image data sets of the female anal canal as a base for a new innovative clinical visual representation. Results It is shown in this case study that the internal and external sphincter region could be registered elastically and the registration partially corrects the compression induced by the ultrasound transducer, so the MR data set showing the native anatomy is used as a frame for the US data set showing the same region with higher resolution but distorted by the transducer Conclusion The morphology is of special interest in the assessment of anal incontinence and the non-rigid registration of normal clinical MR and US image data sets is a new field of the adaptation of this method incorporating the advantages of both technologies.
机译:背景技术不同形式的视觉组合对于计算机辅助医学诊断(CAD)领域中的许多医学成像应用至关重要,以增强临床信息内容。在临床上,失禁是一种具有较高的临床患病率和发病率的诊断。寻找一种识别危险患者并控制手术成功的方法仍然是一项艰巨的任务。磁共振(MR)和3D超声(US)图像数据集的结合可能会导致形态学的新临床视觉表示,正如我们在本文中对女性肛管的相应数据集所显示的那样。方法我们基于女性肛门管的MR和US图像数据集的生物力学模型,提出了一种针对非刚性配准技术的可行性研究,以此作为新型创新性临床视觉表现的基础。结果在本案例研究中表明,内括约肌区域和外括约肌区域可以弹性配准,并且配准可以部分纠正超声换能器引起的压缩,因此将显示天然解剖结构的MR数据集用作US数据的框架结论形态学在评估肛门失禁时特别受关注,正常临床MR和US图像数据集的非刚性配准是适应这一领域的新领域结合了两种技术优势的方法。

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