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: 7851326
• 负责人: Subhadra Srinivasan
• 金额: $ 36.39万
• 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/7851326
• 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）：该提案中的总体假设是新兴的混合成像系统，将光谱带入成像中需要当前不存在的计算工具。具体而言，可以实现边界元素方法（BEM）算法，以对小动物的乳腺组织和荧光层析成像进行图像引导进行三维（3D）图像重建。该系统智能地利用了从MRI到指导NIR光谱法（NIR）的解剖结构来改善对乳腺癌的诊断。和CT组织结构可指导NIR，从而可以准确恢复荧光摄取。传统上，MR和CT的IG-NIR方法都依靠数值模型来实现数值模型，以进行数量离散化（例如有限元素）。在此提案中，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获得先验的基础组织边界。特别是，将开发一个计算工具箱，该工具箱使用MRI图像无缝地为不同组织层（例如脂肪，纤维状界和肿瘤）创建表面网格。该工具箱将使用这些网格以及NIR测量结果来重建总血红蛋白，氧饱和度和水的3D组织血管估计。和每个组织层中散射尺寸和数量密度的细胞估计。还将开发一个荧光工具箱，该工具箱从Microct图像中获取表面网格，并同时使用BEM同时解决一组耦合的扩散方程，以恢复小动物不同组织器官中的3D荧光值。这些工具箱以及图形用户界面允许NIR，MRI和Microct图像的3D图像可视化和并置，将为使用混合成像技术的不同研究组提供易于使用的边界元素软件。利用正在进行的乳房成像试验中的临床数据，我们建议分析50名患者的3D边界元素组织估计的结果，以探索该技术对组织诊断的敏感性和特异性测量，以及其非侵入性研究癌症的潜力。在CT荧光环境中成像的小动物的体内测量也将通过单独的资助项目获得BEM分子成像的测试。这个新型的BEM工具箱具有强度对诸如FEM等体积离散方法的优势及其在解决图像引导的重建问题方面的计算效率将为3D光学成像设定标准。这将进一步将MRI-NIR 3D成像作为日常诊断工具，从而提供无创的高分辨率功能表征。该工具箱的两个版本将计划开发，一个版本更高级，可以通过与Art Inc的互动来转化为商业版，同时，将分发开放式访问版本，该版本允许NIRS领域的新手用户设置新的和不断发展的工具使用BEM Toolbox。 公共卫生相关性：混合MRI-NEAR-IN-FRADER（NIR）系统有可能使用光学签名中的补充信息减少乳腺癌诊断中的假阳性和随访侵入性手术的数量。此处提出的计算工具箱将提供一种强大而有效的方法，用于在此框架中对大型临床主题种群的可行，更准确的三维成像。总体而言，这将进一步进一步研究，研究正常和患病的乳腺组织和荧光成像的高分辨率光学特征，用于研究生化和细胞机制。