PARAMETRIC RECONSTRUCTION IN DIFFUSE OPTICAL IMAGING

漫射光学成像中的参数重建

• 批准号: 8362664
• 负责人: ALBERT Edward CERUSSI
• 金额: $ 2.96万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8362664
• 关键词: Agreement; Biotechnology; Breast; Clinical; Clinical Data; Clinical Distribution; Data; Data Set; Diffuse; Diffusion; Elements; Funding; Grant; Heterogeneity; Image; Lasers; Least-Squares Analysis; Left; Lesion; Location; Malignant - descriptor; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; National Center for Research Resources; Optics; Patients; Pilot Projects; Principal Investigator; Procedures; Process; Property; Relative (related person); Research; Research Infrastructure; Resources; Right-On; Scanning; Side; Simulate; Source; Spatial Distribution; Spectrum Analysis; System; Techniques; Technology; Tissues; United States National Institutes of Health; Work; absorption; base; breast lesion; cost; data sharing; detector; instrument; models and simulation; optical imaging; reconstruction; tomography; tumor

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Conventional tomographic approaches for optical imaging are best suited for data sets that contain measurements from a large number of source-detector pairs but few wavelengths (i.e., spatially 'rich,' but spectrally 'sparse'). Due to technical complexities, quantitative broadband spectroscopic technologies are difficult to implement in traditional imaging configurations. As a result, these conventional tomography approaches are not easily implemented with broadband spectroscopy techniques that use only a limited number of source-detector pairs. Our effort in 'sparse tomography,' pr 'parametric reconstruction,' is geared towards data sets that are spectrally 'rich' but spatially 'sparse.' There is evidence that a relatively simple tomographic approach may be a good compromise between the demand for handheld-acquired broadband spectra and the need for depth-sectioned imaging in cancer applications. Based on our analysis of breast clinical data with a forward finite element modeling procedure, we believe the spatial extent of a breast lesion's optical properties is much larger than the structural confines of the tumor as measured by conventional radiological methods (Figure XXX). The top-left panel represents the traditional view of a tumor: a perturbation in optical properties neatly segmented between background (subscript 'bkg') and heterogeneity (subscript 'het'). In a typical Diffuse Optical Spectroscopy measurement, the source (S) and detector (D) are scanned in tandem across a tissue. The results of an actual Diffuse Optical spectroscopy measurement are provided (circles) in comparison with a forward model simulation using a radiative transport model of the SDA provided by the Finite Element Method (squares). We discovered that there was no physical set of simulated optical properties that could replicate the clinical measurement. On the right side of Figure XXX, we present a different view of the tumor: a gradient in optical properties. By varying the properties of the optical property distribution, the clinical and simulated data share a much closer agreement (bottom-right). In a pilot study of 10 patients, we further found that the best agreement between the modeling results and the clinical data is achieved when the spatial extent of the optical property distribution is much larger than radiological size estimates. This is especially true for malignant lesions. The larger spatial extent of the lesion relaxes the number of source-detector pairs necessary to visualize the target. Moreover, by constraining the spatial distribution of optical properties to those given by a Gaussian (or other) distribution also reduces the number of reconstructed variables. Simple reconstructions using such an approach, which we refer to as "sparse tomography," also eliminates the need for regularization parameters. Our proposed sparse tomography procedure can be used with any imaging data, although it may work best with systems that have limited imaging capabilities. We will first use our DOS/I instrument to take scans of breast lesions. The data will be processed within the VTS to recover the optical properties that provide a least squares fits to semi-infinite homogenous diffusion models. Next we invoke a Finite Element Model diffusion-based model of a breast lesion with a Gaussian spatial distribution of a perturbation in optical absorption and scattering properties relative to the background optical properties. Simulated data will then be generated with this model using finite element methods. Next the simulated data will be processed with the same least-squares fit to the same semi-infinite homogenous diffusion model. Finally, we will compare our clinical observation with our simulated result, in a chi-squared sense. This process will be iterated until the chi-squared is minimized. We realize that this procedure is in effect "blurring" the location of the target. However we do not believe that this description of a tumor is unphysical because it is well known that the margins of tumors can be quite extensive compared to the radiologically-determined size.

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的首席研究员可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 用于光学成像的传统层析成像方法最适合包含大量源探测器对但波长很少的测量数据集（即空间“丰富”，但光谱“稀疏”）。 由于技术复杂性，定量宽带光谱技术很难在传统成像配置中实现。 因此，这些传统的断层扫描方法不容易通过仅使用有限数量的源-探测器对的宽带光谱技术来实现。 我们在“稀疏断层扫描”或“参数重建”方面的努力是针对光谱“丰富”但空间“稀疏”的数据集。 有证据表明，相对简单的断层扫描方法可能是手持式采集宽带光谱的需求和癌症应用中深度切片成像的需求之间的良好折衷。 根据我们使用正向有限元建模程序对乳腺临床数据的分析，我们相信乳腺病变光学特性的空间范围远大于传统放射学方法测量的肿瘤的结构范围（图XXX）。 左上图代表了肿瘤的传统视图：光学特性的扰动在背景（下标“bkg”）和异质性（下标“het”）之间整齐地分割。 在典型的漫反射光谱测量中，光源 (S) 和检测器 (D) 在组织上串联扫描。 提供了实际漫光谱测量的结果（圆圈），并与使用有限元法提供的 SDA 辐射传输模型（正方形）进行的正向模型模拟进行了比较。 我们发现没有一组物理模拟光学特性可以复制临床测量。 在图 XXX 的右侧，我们呈现了肿瘤的不同视图：光学特性的梯度。 通过改变光学特性分布的特性，临床数据和模拟数据的一致性更加接近（右下）。 在对 10 名患者进行的试点研究中，我们进一步发现，当光学特性分布的空间范围远大于放射学尺寸估计值时，建模结果与临床数据之间达到最佳一致性。 对于恶性病变尤其如此。 病变的较大空间范围减少了可视化目标所需的源-探测器对的数量。 此外，通过将光学属性的空间分布限制为高斯（或其他）分布给出的空间分布，还减少了重建变量的数量。 使用这种方法进行简单重建（我们称之为“稀疏断层扫描”）也消除了对正则化参数的需要。 我们提出的稀疏断层扫描程序可用于任何成像数据，尽管它可能最适合成像能力有限的系统。 我们将首先使用 DOS/I 仪器对乳房病变进行扫描。 数据将在 VTS 内进行处理，以恢复光学特性，从而为半无限均匀扩散模型提供最小二乘拟合。 接下来，我们调用基于有限元模型扩散的乳腺病变模型，该模型具有相对于背景光学特性的光学吸收和散射特性扰动的高斯空间分布。 然后将使用有限元方法通过该模型生成模拟数据。 接下来，将使用适合相同半无限均匀扩散模型的相同最小二乘法来处理模拟数据。 最后，我们将从卡方意义上比较我们的临床观察结果和模拟结果。 这个过程将被迭代，直到卡方最小化。 我们意识到这个过程实际上“模糊”了目标的位置。 然而，我们并不认为这种对肿瘤的描述是不符合物理的，因为众所周知，与放射学确定的大小相比，肿瘤的边缘可能相当广泛。