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3D freehand ultrasound reconstruction using a piecewise smooth Markov random field

机译：使用分段光滑马尔可夫随机场的3D徒手超声重建

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摘要

In this paper, we introduce a novel three-dimensional (3D) reconstruction framework for ultrasound images using a piecewise smooth Markov random field (MRF) model from irregularly spaced B-scan images obtained by freehand scanning. Freehand 3D ultrasound imaging is a useful system for various clinical applications, including image-guided surgeries and interventions, as well as diagnoses, due to the variety of its scan ranges and relatively low cost. The reconstruction process performs a key role in this system because its sampling irregularities may cause undesired artifacts, and ultrasound images generally suffer from noise and distortions. However, traditional approaches are based on simple geometric interpolations, such as pixel-based or distance-weighted methods, which are sensitive to sampling density and speckle noise. These approaches generally have an additional limitation of smoothing objects boundaries. To reduce speckle noise and preserve boundaries, we devised a piecewise smooth (PS) MRF model and developed its optimization algorithm. In our framework, we can easily apply an individual noise level for each image pixel, which is specified by the characteristics of an ultrasound probe, and possibly, the lateral and axial positions of an image. As a result, the reconstructed volume has sharp object boundaries with reduced speckle noise and artifacts. Our PS-MRF model provides simple segmentation results within a reconstruction framework that is useful for various purposes, such as clear visualization. The corresponding optimization methods have also been developed, and we tested a virtual phantom and a physical phantom model. Experimental results show that our method outperforms existing methods in terms of interpolation and segmentation accuracy. With this method, all computations can be performed with practical time consumption and with an appropriate resolution, via parallel computing using graphic processing units.
机译：在本文中，我们介绍了一种新颖的三维（3D）超声图像重建框架，它使用分段平滑马尔可夫随机场（MRF）模型从徒手扫描获得的不规则间隔B扫描图像中进行构建。手绘3D超声成像由于其扫描范围多种多样且成本相对较低，因此对于各种临床应用（包括图像引导的手术和干预措施以及诊断）都是有用的系统。重建过程在该系统中起着关键作用，因为其采样不规则性可能会导致不良的伪影，并且超声图像通常会受到噪声和失真的影响。但是，传统方法基于简单的几何插值，例如基于像素的方法或基于距离加权的方法，它们对采样密度和斑点噪声敏感。这些方法通常具有平滑对象边界的附加限制。为了减少斑点噪声并保留边界，我们设计了分段平滑（PS）MRF模型并开发了其优化算法。在我们的框架中，我们可以轻松地为每个图像像素应用单独的噪声级别，该噪声级别由超声探头的特性以及图像的横向和轴向位置指定。结果，重建的体积具有清晰的物体边界，减少了斑点噪声和伪影。我们的PS-MRF模型在重建框架内提供了简单的分割结果，可用于多种目的，例如清晰的可视化。还开发了相应的优化方法，并且我们测试了虚拟体模和物理体模模型。实验结果表明，我们的方法在插值和分割精度方面优于现有方法。使用这种方法，可以通过使用图形处理单元的并行计算，以实际的时间消耗和适当的分辨率执行所有计算。

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