Zachiu C, Ries M, Ramaekers P, Guey J-L, Moonen C T W, de Senneville B Denis
Imaging Division, UMC Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, Netherlands.
Phys Med Biol. 2017 Oct 4;62(20):8154-8177. doi: 10.1088/1361-6560/aa8c66.
Biological motion is a problem for non- or mini-invasive interventions when conducted in mobile/deformable organs due to the targeted pathology moving/deforming with the organ. This may lead to high miss rates and/or incomplete treatment of the pathology. Therefore, real-time tracking of the target anatomy during the intervention would be beneficial for such applications. Since the aforementioned interventions are often conducted under B-mode ultrasound (US) guidance, target tracking can be achieved via image registration, by comparing the acquired US images to a separate image established as positional reference. However, such US images are intrinsically altered by speckle noise, introducing incoherent gray-level intensity variations. This may prove problematic for existing intensity-based registration methods. In the current study we address US-based target tracking by employing the recently proposed EVolution registration algorithm. The method is, by construction, robust to transient gray-level intensities. Instead of directly matching image intensities, EVolution aligns similar contrast patterns in the images. Moreover, the displacement is computed by evaluating a matching criterion for image sub-regions rather than on a point-by-point basis, which typically provides more robust motion estimates. However, unlike similar previously published approaches, which assume rigid displacements in the image sub-regions, the EVolution algorithm integrates the matching criterion in a global functional, allowing the estimation of an elastic dense deformation. The approach was validated for soft tissue tracking under free-breathing conditions on the abdomen of seven healthy volunteers. Contact echography was performed on all volunteers, while three of the volunteers also underwent standoff echography. Each of the two modalities is predominantly specific to a particular type of non- or mini-invasive clinical intervention. The method demonstrated on average an accuracy of ∼1.5 mm and submillimeter precision. This, together with a computational performance of 20 images per second make the proposed method an attractive solution for real-time target tracking during US-guided clinical interventions.
由于目标病变会随移动/可变形器官一起移动/变形，因此在移动/可变形器官中进行非侵入性或微创干预时，生物运动是一个难题。这可能导致高漏诊率和/或病变治疗不彻底。因此，在干预过程中对目标解剖结构进行实时跟踪将有利于此类应用。由于上述干预通常在B型超声（US）引导下进行，通过将采集的超声图像与作为位置参考的单独图像进行比较，可通过图像配准实现目标跟踪。然而，此类超声图像会因散斑噪声而发生本质变化，从而引入不连贯的灰度强度变化。这对于现有的基于强度的配准方法可能是个问题。在本研究中，我们通过采用最近提出的进化配准算法来解决基于超声的目标跟踪问题。该方法在构建时对瞬态灰度强度具有鲁棒性。进化算法不是直接匹配图像强度，而是对齐图像中相似的对比度模式。此外，位移是通过评估图像子区域的匹配标准来计算的，而不是逐点计算，这通常能提供更稳健的运动估计。然而，与之前类似的假设图像子区域存在刚性位移的已发表方法不同，进化算法将匹配标准集成到一个全局函数中，从而能够估计弹性密集变形。该方法在七名健康志愿者腹部的自由呼吸条件下进行软组织跟踪得到了验证。对所有志愿者进行了接触式超声检查，其中三名志愿者还接受了非接触式超声检查。这两种模式中的每一种都主要适用于特定类型的非侵入性或微创临床干预。该方法平均显示出约1.5毫米的精度和亚毫米级的精确度。这与每秒处理20幅图像的计算性能相结合，使得所提出的方法成为超声引导临床干预期间实时目标跟踪的有吸引力的解决方案。
