Dahmani Jawad, Petit Yvan, Laporte Catherine
École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, QC, Canada.
Int J Comput Assist Radiol Surg. 2024 Feb;19(2):309-320. doi: 10.1007/s11548-023-03006-w. Epub 2023 Aug 18.
The acquisition of good quality ultrasound (US) images requires good acoustic coupling between the ultrasound probe and the patient's skin. In practice, this good coupling is achieved by the operator applying a force to the skin through the probe. This force induces a deformation of the tissues underlying the probe. The distorted images deteriorate the quality of the reconstructed 3D US image.
In this work, we propose two methods to correct these deformations. These methods are based on the construction of a biomechanical model to predict the mechanical behavior of the imaged soft tissues. The originality of the methods is that they do not use external information (force or position value from sensors, or elasticity value from the literature). The model is parameterized thanks to the information contained in the image. This is allowed thanks to the optimization of two key parameters for the model which are the indentation d and the elasticity ratio α.
The validation is performed on real images acquired on a gelatin-based phantom using an ultrasound probe inducing an increasing vertical indentation using a step motor. The results showed a good correction of the two methods for indentations less than 4 mm. For larger indentations, one of the two methods (guided by the similarity score) provides a better quality of correction, presenting a Euclidean distance between the contours of the reference image and the corrected image of 0.71 mm.
The proposed methods ensured the correction of the deformed images induced by a linear probe pressure without using any information coming from sensors (force or position), or generic information about the mechanical parameters. The corrected images can be used to obtain a corrected 3D US image.
获得高质量的超声(US)图像需要超声探头与患者皮肤之间有良好的声耦合。在实践中,这种良好的耦合是通过操作人员通过探头向皮肤施加力来实现的。这种力会引起探头下组织的变形。变形的图像会降低重建的 3D US 图像的质量。
在这项工作中,我们提出了两种方法来纠正这些变形。这些方法基于构建一个生物力学模型来预测被成像的软组织的机械行为。这些方法的新颖之处在于它们不使用外部信息(来自传感器的力或位置值,或来自文献的弹性值)。该模型通过图像中包含的信息进行参数化。这是通过优化模型的两个关键参数来实现的,这两个参数是压痕 d 和弹性比α。
验证是在使用步进电机诱导垂直压痕逐渐增加的基于明胶的体模上采集的真实图像上进行的。结果表明,对于小于 4mm 的压痕,两种方法中的两种方法都能很好地纠正。对于较大的压痕,两种方法之一(由相似度得分引导)提供了更好的校正质量,参考图像和校正图像之间的轮廓的欧几里得距离为 0.71mm。
所提出的方法确保了在不使用来自传感器(力或位置)的任何信息或关于机械参数的通用信息的情况下,纠正线性探头压力引起的变形图像。校正后的图像可用于获得校正后的 3D US 图像。