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: 7524750
• 负责人: Subhadra Srinivasan
• 金额: $ 36.46万
• 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/7524750
• 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; Invasive; Knowledge; Lesion; Light; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Mammary Gland Parenchyma; Measurement; Measures; Methods; Modality; Modeling; Molecular; Near-Infrared Spectroscopy; Normal tissue morphology; Numbers; Optical Tomography; Optics; Organ; Oxygen; Pathology; Patients; Personal Satisfaction; Population; Procedures; Property; Public Health; Rate; Receiver Operating Characteristics; Recovery; Research; Resolution; Scheme; Sensitivity and Specificity; Series; Spectrum Analysis; Standards of Weights and Measures; 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; improved; in vivo; molecular imaging; novel; optical imaging; prevent; reconstruction; size; 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)：本提案中的总体假设是，将光谱技术引入成像的新兴混合成像系统需要计算工具，而这些工具目前还不存在。具体地说，可以实现边界单元法(BEM)算法来执行三维(3D)图像重建，用于图像引导的乳腺组织近红外(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的解剖结构来引导近红外光谱(NIRS)来提高乳腺癌的诊断水平，并利用CT组织结构来引导NIRS来准确地恢复荧光摄取。对于IG-NIR，MR和CT方法传统上都依赖于需要离散体积的扩散方程的数值模型(如有限元)。在这个方案中，我们将使用边界元法(只需要表面离散化)来求解扩散方程，并将其应用于3D图像重建，假设潜在的组织边界可以从MRI或CT先验地获得。特别是，将开发一个计算工具箱，使用MRI图像为不同的组织层(如脂肪、纤维腺和肿瘤)无缝创建表面网格。该工具箱将使用这些网格和近红外测量来重建总血红蛋白、血氧饱和度和水分的3D组织血管估计，以及每个组织层中散射体大小和数密度的细胞估计。还将开发一个荧光工具箱，从MicroCT图像中获取表面网格，并同时使用边界元法求解一组耦合的扩散方程，以恢复小动物不同组织器官的三维荧光值。这些工具箱与图形用户界面一起，允许近红外、核磁共振和微CT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小组提供易于使用的边界元素软件。利用正在进行的乳腺成像试验的临床数据，我们建议分析来自50名患者的3D边界元素组织估计的结果，以探索该技术在组织诊断中的敏感性和特异性，以及它在非侵入性研究癌症方面的潜力。在CT荧光成像环境下对小动物进行的活体测量也将通过一个单独的资助项目提供，用于测试BEM分子成像。这种新型的边界元工具箱以其相对于有限元等体积离散方法的优势，以及在解决图像引导重建问题方面的计算效率，将为三维光学成像树立标准。这将进一步使用MRI-NIR 3D成像作为日常诊断工具，提供对病变组织的非侵入性高分辨率功能表征。将计划开发工具箱的两个版本，一个更先进，可通过与ART Inc.的互动转换为商业版本，同时将分发一个开放获取版本，使近红外领域的新手用户能够建立使用边界管理工具箱的新的和不断发展的工具。 公共卫生相关性：利用光学信号的补充信息，MRI-近红外(NIR)混合系统有可能减少乳腺癌诊断中的假阳性和后续侵入性手术的数量。本文提出的计算工具箱将为在此框架下对大规模临床受试者群体进行可行和更准确的三维成像提供一种强大而高效的方法。总体而言，这将进一步推进体内正常和疾病乳腺组织的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。