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Coupling brain-tumor biophysical models and diffeomorphic image registration

机译：脑肿瘤生物物理模型与微形图像配准的耦合

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We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a framework for joint image registration and biophysical inversion and we apply it to analyze MR images of glioblastomas (primary brain tumors). We have two applications in mind. The first one is normal-to-abnormal image registration in the presence of tumor-induced topology differences. The second one is biophysical inversion based on single-time patient data. The underlying optimization problem is highly non-linear and non-convex and has not been solved before with a gradient-based approach.Given the segmentation of a normal brain MRI and the segmentation of a cancer patient MRI, we determine tumor growth parameters and a registration map so that if we "grow a tumor" (using our tumor model) in the normal brain and then register it to the patient image, then the registration mismatch is as small as possible. This "coupled problem" two-way couples the biophysical inversion and the registration problem. In the image registration step we solve a large-deformation diffeomorphic registration problem parameterized by an Eulerian velocity field. In the biophysical inversion step we estimate parameters in a reaction-diffusion tumor growth model that is formulated as a partial differential equation (PDE). In SIBIA, we couple these two sub-components in an iterative manner. We first presented the components of SIBIA in "Gholami et al., Framework for Scalable Biophysics-based Image Analysis, IEEE/ACM Proceedings of the SC2017", in which we derived parallel distributed memory algorithms and software modules for the decoupled registration and biophysical inverse problems. In this paper, our contributions are the introduction of a PDE-constrained optimization formulation of the coupled problem, and the derivation of a Picard iterative solution scheme. We perform extensive tests to experimentally assess the performance of our method on synthetic and clinical datasets. We demonstrate the convergence of the SIBIA optimization solver in different usage scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled problem in three dimensions (256(3) resolution) in a few minutes using 11 dual-x86 nodes. (C) 2018 Elsevier B.V. All rights reserved.
机译：我们提出了SIBIA（基于可伸缩的综合生物物理学的图像分析），一种用于联合图像配准和生物物理反转的框架，并将其应用于分析胶质母细胞瘤（原发性脑肿瘤）的MR图像。我们考虑两个应用程序。第一个是存在肿瘤诱发的拓扑差异的正常到异常图像配准。第二个是基于一次性患者数据的生物物理反演。潜在的优化问题是高度非线性和非凸的，并且以前没有使用基于梯度的方法来解决。鉴于正常脑MRI的分割和癌症患者MRI的分割，我们确定了肿瘤生长参数和配准图，这样，如果我们在正常大脑中“生长出一个肿瘤”（使用我们的肿瘤模型），然后将其配准到患者图像中，则配准失配会尽可能小。该“耦合问题”双向耦合了生物物理反演和配准问题。在图像配准步骤中，我们解决了由欧拉速度场参数化的大变形微晶配准问题。在生物物理反演步骤中，我们估计反应扩散肿瘤生长模型中的参数，该模型被公式化为偏微分方程（PDE）。在SIBIA中，我们以迭代方式将这两个子组件耦合在一起。我们首先在“ Gholami等人，基于可扩展生物物理的图像分析框架，SC2017的IEEE / ACM会议录”中介绍了SIBIA的组件，在其中我们导出了并行的分布式内存算法和软件模块，用于解耦配准和生物物理逆问题。在本文中，我们的贡献是引入了耦合问题的PDE约束优化公式，并推导了Picard迭代求解方案。我们进行广泛的测试，以实验方式评估我们的方法在合成和临床数据集上的性能。我们演示了SIBIA优化求解器在不同使用场景下的收敛性。我们证明了使用SIBIA，我们可以使用11个双x86节点在几分钟内准确地解决三维（256（3）分辨率）耦合问题。 （C）2018 Elsevier B.V.保留所有权利。

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