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首页> 外文会议>Conference on Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling >Patient-specific Deep Deformation Models (PsDDM) to register planning and interventional ultrasound volumes in image fusion-guided interventions.
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Patient-specific Deep Deformation Models (PsDDM) to register planning and interventional ultrasound volumes in image fusion-guided interventions.

机译：特定于患者的深度变形模型（PsDDM）可在图像融合引导的干预措施中注册计划和介入性超声体积。

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Image fusion-guided interventions often require planning MR/CT and interventional U/S images to be registered in realtime. Organ motion, patient breathing and inconsistent ultrasound probe positioning during intervention, all contribute to the challenges of real-time 3D deformable registration, where alignment accuracy and computation time are often mutual trade-offs. In this work, we propose a novel framework to align planning and interventional 3D U/S by training patient-specific deep-deformation models (PsDDM) at the planning stage. During intervention, planning 3D U/S volumes are efficiently warped onto the interventional 3D U/S volumes using the trained deep-deformation model, thus enabling the transfer of other modality (planning MR/CT) information in real-time on interventional images. The alignment of planning MR/CT to planning U/S is not time-critical as these can be aligned before the intervention with desired accuracy using any known multimodal deformable registration method. The feasibility of training PsDDM is shown on liver U/S data acquired with a custom-built MR-compatible, hands-free 3D ultrasound probe that allows simultaneous acquisition of planning MR and U/S. Liver U/S volumes exhibit large motion in time due to respiration and therefore serve as a good anatomy to quantify the accuracy of the PsDDM. For quantitative evaluation of the PsDDM, a large vessel bifurcation was manually annotated on 9 U/S volumes that were not used for training the PsDDM but from the same subject. Mean target registration error (TRE) between the centroids was 0.84mm ± 0.39mm, mean Hausdorff distance (HD) was 1.80mm ± 0.29mm and mean surface distance (MSD) was 0.44mm ± 0.06mm for all volumes. In another experiment, the PsDDM was trained using liver volumes from one scanning session, while the model was tested on data from a separate scanning session of the same patient, for which qualitative alignment results were presented.
机译：图像融合引导的干预通常需要计划MR / CT和介入性U / S图像以进行实时注册。器官运动，患者呼吸以及介入过程中超声探头的位置不一致，都对实时3D变形注册带来了挑战，其中对准精度和计算时间通常是相互取舍的。在这项工作中，我们提出了一个新颖的框架，可以通过在计划阶段训练患者特定的深度变形模型（PsDDM）来使计划和介入式3D U / S保持一致。在干预期间，使用经过训练的深度变形模型，可以将计划3D U / S量有效地扭曲到介入3D U / S量上，从而能够在干预图像上实时传输其他模态（计划MR / CT）信息。计划MR / CT与计划U / S的对准不是时间紧迫的，因为可以使用任何已知的多模态可变形配准方法在干预之前以期望的精度对准这些MR / CT。使用定制的MR兼容，免提3D超声探头采集的肝脏U / S数据显示了训练PsDDM的可行性，该探头可同时采集计划的MR和U / S。肝脏U / S体积由于呼吸而及时运动，因此可作为量化PsDDM准确性的良好解剖结构。为了定量评估PsDDM，在9个U / S体积上手动标注了大血管分叉，这些体积不用于训练PsDDM，而是来自同一受试者。对于所有体积，质心之间的平均目标配准误差（TRE）为0.84mm±0.39mm，平均Hausdorff距离（HD）为1.80mm±0.29mm，平均表面距离（MSD）为0.44mm±0.06mm。在另一项实验中，使用来自一次扫描的肝脏体积对PsDDM进行了训练，而该模型是根据来自同一位患者的另一次扫描过程中的数据进行测试的，给出了定性比对结果。

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