Mulder Harriët W, van Stralen Marijn, Ren Ben, Haak Alexander, van Burken Gerard, Viergever Max A, Bosch Johan G, Pluim Josien P W
Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, The Netherlands.
Ultrasound Med Biol. 2018 Jul;44(7):1533-1543. doi: 10.1016/j.ultrasmedbio.2018.02.004. Epub 2018 Apr 16.
Three-dimensional transesophageal echocardiography (TEE) provides real-time soft tissue information, but its use is hampered by its limited field of view. The mosaicing of multiple TEE views makes it possible to visualize a large structure, like the left atrium, in a single volume. To this end, an automatic registration method is required. Similarly to atlas-based segmentation approaches, atlas-based mosaicing (ABM) uses a full volume atlas set to moderate the onerous registration of the individual TEE views. The performance of ABM depends both on the quality of the involved registrations and on the selection of the optimal transformation from the candidate transformations that result from the various atlases. The study described here explored the performance of different selection strategies on multiview TEE data of the left atrium. We found that by incorporating two stages of transformation selection, using the image similarity and the conformity between the candidate transformations as selection criteria, the average registration error dropped below 3 mm with respect to manual registration of these data. Finally, we used this method for the automatic construction of a wide-view TEE volume of the left atrium.
三维经食管超声心动图（TEE）可提供实时软组织信息，但其视野有限，限制了其应用。多个TEE视图的拼接使得在单个容积中可视化大型结构（如左心房）成为可能。为此，需要一种自动配准方法。与基于图谱的分割方法类似，基于图谱的拼接（ABM）使用一组完整的容积图谱来简化各个TEE视图的繁重配准。ABM的性能既取决于所涉及配准的质量，也取决于从各种图谱产生的候选变换中选择最佳变换。本文所述的研究探讨了不同选择策略在左心房多视图TEE数据上的性能。我们发现，通过纳入两个变换选择阶段，以图像相似性和候选变换之间的一致性作为选择标准，相对于这些数据的手动配准，平均配准误差降至3毫米以下。最后，我们使用该方法自动构建了左心房的宽视野TEE容积。
