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摘要翻译：
我们提出了SIBIA（Scalable Integrated Biophysics-based Image Analysis）框架，用于联合图像配准和生物物理反演，并将其应用于胶质母细胞瘤（原发性脑肿瘤）的MR图像分析。我们特别考虑以下问题。给定正常脑MRI和癌症患者MRI的分割，我们希望确定肿瘤生长参数和配准图，以便如果我们在正常分割图像中“生长肿瘤”（使用我们的肿瘤模型），然后将其配准到患者分割图像，那么配准失配尽可能小。我们称之为“耦合问题”，因为它将生物物理反演和配准问题双向耦合。在图像配准阶段，我们解决了一个由欧拉速度场参数化的大变形差同配准问题。在生物物理反演步骤中，我们在反应扩散肿瘤生长模型中估计参数，该模型被描述为一个偏微分方程。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，导出了最优性条件，并导出了求解耦合问题的Picard迭代格式。此外，我们进行了几个测试，以实验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。我们用11个dual-x86节点，证明了我们可以在几分钟内精确地解决三维耦合问题($256^3$分辨率）。此外，我们还证明，通过我们的耦合方法，我们可以成功地将正常MRI与带瘤MRI进行配准，同时获得与正常对正常MRI配准时所获得的Dice系数相匹配的Dice系数。
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英文标题：
《Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image
Registration》
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作者：
Klaudius Scheufele, Andreas Mang, Amir Gholami, Christos Davatzikos,
George Biros, Miriam Mehl
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最新提交年份：
2018
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分类信息：
一级分类：Physics 物理学
二级分类：Medical Physics 医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling. Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Mathematics 数学
二级分类：Optimization and Control 优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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一级分类：Quantitative Biology 数量生物学
二级分类：Tissues and Organs 组织器官
分类描述：Blood flow in vessels, biomechanics of bones, electrical waves, endocrine system, tumor growth
血管内血流，骨骼生物力学，电波，内分泌系统，肿瘤生长
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英文摘要：
We present the SIBIA (Scalable Integrated Biophysics-based Image Analysis) framework for joint image registration and biophysical inversion and we apply it to analyse MR images of glioblastomas (primary brain tumors). In particular, we consider the following problem. Given the segmentation of a normal brain MRI and the segmentation of a cancer patient MRI, we wish to determine tumor growth parameters and a registration map so that if we "grow a tumor" (using our tumor model) in the normal segmented image and then register it to the patient segmented image, then the registration mismatch is as small as possible. We call this "the coupled problem" because it two-way couples the biophysical inversion and registration problems. 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. In this paper, our contributions are the introduction of a PDE-constrained optimization formulation of the coupled problem, the derivation of the optimality conditions, and the derivation of a Picard iterative scheme for the solution of the coupled problem. In addition, we perform several 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 we can accurately solve the coupled problem in three dimensions ($256^3$ resolution) in a few minutes using 11 dual-x86 nodes. Also, we demonstrate that, with our coupled approach, we can successfully register normal MRI to tumor-bearing MRI while obtaining Dice coefficients that match those achieved when registering of normal-to-normal MRI.
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PDF链接：
https://arxiv.org/pdf/1710.0642
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