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Iterative Deformable FEM Model for Nonrigid PET/MRI Breast Image Coregistration

机译:非抗体宠物/ MRI乳房图像核心再次迭代可变形的有限元模型

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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:我们实现了一种迭代非引用注册算法,以便在3D中精确地组合功能(PET)和解剖学(MRI)图像。我们的方法依赖于有限元方法(FEM)和放置在乳房表面上的一组基准皮肤标记(FSM)。如果成像乳房中的应力条件在PET和MRI中几乎相同,则该方法适用。在第一阶段中,确定在MRI和PET中观察到的相应FSM的位移矢量,然后使用FEAS在整个乳房体积上线性地分配FSM位移。我们的有关模型依赖于位移场的每个正交分量之间的类比,以及固体中稳态传热(SSHT)中的温度分布场。因此,可以通过标准的热传导FEM软件来解决问题,该表面元件的任意导电性远高于容积元件的电导率。在确定所有网状节点处的位移之后,使用图像翘曲算法将移动(MRI)乳房量登记到目标(PET)母乳卷。在第二次迭代中,为了校正任何残留表面和体积误解,将改进过程应用于运动图像,其使用FSM与3D中的目标图像大致对齐。为了执行该过程,我们使用最近点方法确定每个移动和目标图像表面上的许多对应点。然后,在估计表面上的对应点之间的位移向量之后,我们再次应用我们的SSHT模型。我们在12名可疑乳腺病变的12名患者中测试了我们的模型。通过使用两种PET和MRI可见的病变,我们建立了目标登记误差低于两种PET体素。表面配准误差与PET的空间分辨率相当。

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