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首页> 外文会议>Image Processing pt.2; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Iterative Deformable FEM Model for Nonrigid PET/MRI Breast Image Coregistration
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Iterative Deformable FEM Model for Nonrigid PET/MRI Breast Image Coregistration

机译：非刚性PET / MRI乳房图像增强的迭代可变形有限元模型

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摘要

We implemented an iterative nonrigid registration algorithm to accurately combine functional (PET) and anatomical (MRI) images in 3D. Our method relies on a Finite Element Method (FEM) and a set of fiducial skin markers (FSM) placed on breast surface. The method is applicable if the stress conditions in the imaged breast are virtually the same in PET and MRI. In the first phase, the displacement vectors of the corresponding FSM observed in MRI and PET are determined, then FEM is used to distribute FSM displacements linearly over the entire breast volume. Our FEM model relies on the analogy between each of the orthogonal components of displacement field, and the temperature distribution field in a steady state heat transfer (SSHT) in solids. The problem can thus be solved via standard heat-conduction FEM software, with arbitrary conductivity of surface elements set much higher than that of volume elements. After determining the displacements at all mesh nodes, moving (MRI) breast volume is registered to target (PET) breast volume using an image-warping algorithm. In the second iteration, to correct for any residual surface and volume misregistration, a refinement process is applied to the moving image, which was already grossly aligned with the target image in 3D using FSM. To perform this process we determine a number of corresponding points on each moving and target image surfaces using a nearest-point approach. Then, after estimating the displacement vectors between the corresponding points on the surfaces we apply our SSHT model again. We tested our model on twelve patients with suspicious breast lesions. By using lesions visible in both PET and MRI, we established that the target registration error is below two PET voxels. The surface registration error is comparable to the spatial resolution of PET.
机译：我们实施了迭代式非刚性配准算法，以在3D模式下准确组合功能（PET）和解剖（MRI）图像。我们的方法依赖于有限元方法（FEM）和放置在乳房表面的一组基准皮肤标记（FSM）。如果成像的乳房中的压力条件在PET和MRI中实际上相同，则该方法适用。在第一阶段，确定在MRI和PET中观察到的相应FSM的位移矢量，然后使用FEM在整个乳房体积上线性分布FSM位移。我们的FEM模型依赖于固体中稳态传热（SSHT）中位移场的每个正交分量与温度分布场之间的类比。因此，可以通过标准的导热FEM软件解决该问题，将表面元素的任意电导率设置为远高于体积元素的电导率。在确定所有网格节点的位移之后，使用图像变形算法将移动的（MRI）乳房体积与目标（PET）乳房体积进行配准。在第二次迭代中，为了校正任何残留的表面和体积重合失调，对运动图像应用细化处理，该运动图像已经使用FSM在3D中与目标图像进行了大致对齐。为了执行此过程，我们使用最近点方法确定每个运动和目标图像表面上的相应点数。然后，在估计表面上相应点之间的位移矢量后，我们再次应用我们的SSHT模型。我们在十二名可疑乳腺病变患者上测试了我们的模型。通过使用在PET和MRI中都可见的病变，我们确定目标配准误差低于两个PET体素。表面配准误差可与PET的空间分辨率媲美。

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