A Boundary Element Method for MRI/NIR Tomography and Image-guided Fluorescence

MRI/NIR 断层扫描和图像引导荧光的边界元方法

• 批准号: 7656870
• 负责人: Subhadra Srinivasan
• 金额: $ 36.76万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7656870
• 关键词: 3-Dimensional; Adipose tissue; Adoption; Algorithms; Animals; Benign; Biochemical; Blood Vessels; Boundary Elements; Breast; Clinical; Clinical Data; Clinical Trials; Computer software; Coupled; Data; Data Set; Development; Diagnosis; Diagnostic; Diffusion; Dimensions; Elements; Environment; Equation; Fluorescence; Funding; Generations; Hemoglobin; Histocompatibility Testing; Human; Hybrids; Image; Imaging Techniques; Knowledge; Lesion; Light; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Mammary Gland Parenchyma; Measurement; Measures; Methods; Modeling; Molecular; Near-Infrared Spectroscopy; Normal tissue morphology; Optical Tomography; Optics; Organ; Oxygen; Pathology; Patients; Population; Procedures; Property; Receiver Operating Characteristics; Recovery; Research; Resolution; Scheme; Sensitivity and Specificity; Series; Spectrum Analysis; Structure; Surface; System; Techniques; Testing; Three-Dimensional Image; Three-Dimensional Imaging; Time; Tissues; Tracer; Translating; Tumor Tissue; Water; Work; animal data; base; breast cancer diagnosis; computerized tools; density; fluorescence imaging; fluorophore; follow-up; graphical user interface; human subject; image reconstruction; image visualization; imaging modality; improved; in vivo; molecular imaging; novel; optical imaging; prevent; public health relevance; reconstruction; stem; tomography; tool; tumor; uptake; user-friendly

DESCRIPTION (provided by applicant): The overall hypothesis in this proposal is emerging hybrid imaging systems that bring spectroscopy into imaging require computational tools which currently do not exist. Specifically, a boundary element method (BEM) algorithm can be implemented to perform three dimensional (3D) image reconstruction for image-guided near infrared (IG-NIR) tomography of breast tissue and fluorescence tomography in small animals. This system intelligently utilizes anatomical structures from MRI to guide NIR spectroscopy (NIRS) to improve diagnosis of breast cancer; and CT tissue structure to guide NIRS allowing accurate recovery of fluorescence uptake. Both MR and CT approaches to IG-NIRS traditionally rely on numerical models to the diffusion equation requiring volume discretization (such as finite element). In this proposal, we will solve the diffusion equation using the BEM (requiring only surface discretization) and apply it for 3D image reconstruction, assuming that the underlying tissue boundaries can be obtained a priori from MRI or CT. In particular, a computational toolbox will be developed that seamlessly creates surface meshes for different tissue layers such as adipose, fibroglandular and tumor, using MRI images. The toolbox will use these grids along with NIR measurements to reconstruct 3D tissue vascular estimates of total hemoglobin, oxygen saturation and water; and cellular estimates of scatterer size and number density in each tissue layer. A fluorescence toolbox will also be developed that obtains surface grids from MicroCT images and solves a set of coupled diffusion equations simultaneously using BEM to recover 3D fluorescence values in different tissue organs of small animals. These toolboxes together with a graphical user interface allowing 3D image visualization and juxtaposition of NIR, MRI and MicroCT images, will provide easy-to-use boundary element software for different research groups utilizing hybrid imaging techniques. Leveraging the clinical data from an ongoing breast imaging trial, we propose to analyze the results from 3D boundary element tissue estimates of 50 patients to explore the sensitivity and specificity measures of this technique for tissue diagnosis as well as its potential to study cancer non-invasively. In-vivo measurements from small animals imaged in a CT-fluorescence setting will also be available through a separate funded project, for testing of BEM molecular imaging. This novel BEM toolbox with its strengths over volume discretization methods such as FEM and its computational efficiency in solving the image-guided reconstruction problem will set the standard for 3D optical imaging. This will further the use of MRI-NIR 3D imaging as an everyday diagnostic tool providing non-invasive high-resolution functional characterization of diseased tissue. Two versions of the toolbox will plan to be developed, one which is more advanced and can be translated into a commercial version through interaction with ART Inc, and at the same time, a open access version will be distributed which allows novice users in the field of NIRS to set up new and evolving tools which use the BEM toolbox. PUBLIC HEALTH RELEVANCE: A hybrid MRI-near-infrared (NIR) system has the potential to reduce false-positives and the number of follow-up invasive procedures in breast cancer diagnosis using complementary information from optical signatures. The computational toolbox proposed here will provide a powerful and efficient method for viable and more accurate three-dimensional imaging of large clinical subject populations in this framework. Overall, this will further advance the study of high-resolution optical signatures of normal and diseased breast tissue in-vivo and fluorescence imaging for studying biochemical and cellular mechanisms in-vivo.

描述（由申请人提供）：本提案的总体假设是新兴的混合成像系统将光谱引入成像，需要目前不存在的计算工具。具体来说，可以实施边界元法（BEM）算法来执行三维（3D）图像重建，用于乳腺组织的图像引导近红外（IG-NIR）断层扫描和小动物的荧光断层扫描。该系统智能地利用 MRI 的解剖结构来指导近红外光谱 (NIRS)，以改善乳腺癌的诊断； CT 组织结构引导 NIRS，从而准确恢复荧光摄取。 IG-NIRS 的 MR 和 CT 方法传统上都依赖于需要体积离散化（例如有限元）的扩散方程的数值模型。在本提案中，我们将使用 BEM（仅需要表面离散化）求解扩散方程，并将其应用于 3D 图像重建，假设可以从 MRI 或 CT 先验获得底层组织边界。特别是，将开发一个计算工具箱，使用 MRI 图像无缝地为不同组织层（例如脂肪、纤维腺体和肿瘤）创建表面网格。该工具箱将使用这些网格以及 NIR 测量来重建总血红蛋白、氧饱和度和水的 3D 组织血管估计值；以及每个组织层中散射体大小和数量密度的细胞估计。还将开发一个荧光工具箱，从 MicroCT 图像中获取表面网格，并使用 BEM 同时求解一组耦合扩散方程，以恢复小动物不同组织器官的 3D 荧光值。这些工具箱与允许 3D 图像可视化以及 NIR、MRI 和 MicroCT 图像并置的图形用户界面一起，将为利用混合成像技术的不同研究小组提供易于使用的边界元软件。利用正在进行的乳腺成像试验的临床数据，我们建议分析 50 名患者的 3D 边界元组织估计结果，以探索该技术用于组织诊断的敏感性和特异性测量及其非侵入性研究癌症的潜力。在 CT 荧光环境下成像的小动物的体内测量也将通过一个单独资助的项目提供，用于测试 BEM 分子成像。这种新颖的 BEM 工具箱具有优于 FEM 等体积离散方法的优势，以及解决图像引导重建问题的计算效率，将为 3D 光学成像设定标准。这将进一步使用 MRI-NIR 3D 成像作为日常诊断工具，提供病变组织的非侵入性高分辨率功能表征。该工具箱将计划开发两个版本，其中一个版本更先进，可以通过与 ART Inc 的交互转化为商业版本，同时将发布一个开放获取版本，允许 NIRS 领域的新手用户建立使用 BEM 工具箱的新的和不断发展的工具。 公共健康相关性：混合 MRI-近红外 (NIR) 系统有可能利用光学特征的补充信息来减少乳腺癌诊断中的误报和后续侵入性手术的数量。这里提出的计算工具箱将为在此框架中对大型临床受试者群体进行可行且更准确的三维成像提供强大而有效的方法。总体而言，这将进一步推进对正常和患病乳腺组织体内高分辨率光学特征的研究以及用于研究体内生化和细胞机制的荧光成像的研究。