Klein T, Hansson M, Navab Nassir
Computer Aided Medical Procedures, TU München, Germany.
Med Image Comput Comput Assist Interv. 2012;15(Pt 1):422-9. doi: 10.1007/978-3-642-33415-3_52.
Nowadays ultrasound (US) examinations are typically performed with conventional machines providing two dimensional imagery. However, there exist a multitude of applications where doctors could benefit from three dimensional ultrasound providing better judgment, due to the extended spatial view. 3D freehand US allows acquisition of images by means of a tracking device attached to the ultrasound transducer. Unfortunately, view dependency makes the 3D representation of ultrasound a non-trivial task. To address this we model speckle statistics, in envelope-detected radio frequency (RF) data, using a finite mixture model (FMM), assuming a parametric representation of data, in which the multiple views are treated as components of the FMM. The proposed model is show-cased with registration, using an ultrasound specific distribution based pseudo-distance, and reconstruction tasks, performed on the manifold of Gamma model parameters. Example field of application is neurology using transcranial US, as this domain requires high accuracy and data systematically features low SNR, making intensity based registration difficult. In particular, 3D US can be specifically used to improve differential diagnosis of Parkinson's disease (PD) compared to conventional approaches and is therefore of high relevance for future application.
如今，超声（US）检查通常使用提供二维图像的传统机器进行。然而，存在许多应用场景，由于三维超声提供了更广阔的空间视野，医生可以从中受益，从而做出更好的判断。三维徒手超声允许通过连接到超声换能器的跟踪设备获取图像。不幸的是，视图依赖性使得超声的三维表示成为一项具有挑战性的任务。为了解决这个问题，我们在包络检测后的射频（RF）数据中，使用有限混合模型（FMM）对散斑统计进行建模，假设数据的参数表示，其中多个视图被视为FMM的组件。所提出的模型通过使用基于超声特定分布的伪距离进行配准以及在伽马模型参数流形上执行的重建任务进行展示。应用示例领域是使用经颅超声的神经学，因为该领域需要高精度，并且数据系统地具有低信噪比，使得基于强度的配准变得困难。特别是，与传统方法相比，三维超声可专门用于改善帕金森病（PD）的鉴别诊断，因此对未来应用具有高度相关性。
