PARAMETRIC RECONSTRUCTION IN DIFFUSE OPTICAL IMAGING

漫射光学成像中的参数重建

• 批准号: 8169493
• 负责人: ALBERT Edward CERUSSI
• 金额: $ 1.96万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8169493
• 关键词: Agreement; Breast; Clinical; Clinical Data; Clinical Distribution; Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Diffuse; Diffusion; Elements; Funding; Grant; Heterogeneity; Image; Institution; Least-Squares Analysis; Left; Lesion; Location; Malignant - descriptor; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Optics; Patients; Pilot Projects; Procedures; Process; Property; Relative (related person); Research; Research Personnel; 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; 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 用于光学成像的常规层析成像方法最适合于包含来自大量源-检测器对但很少波长（即，空间上“丰富”，但频谱上“稀疏”）。 由于技术复杂性，定量宽带光谱技术难以在传统成像配置中实现。 因此，这些常规的断层摄影方法不容易用仅使用有限数量的源-检测器对的宽带光谱技术来实现。 我们在“稀疏层析成像”或“参数重建”方面的努力是面向光谱“丰富”但空间“稀疏”的数据集。' 有证据表明，一个相对简单的层析成像的方法可能是一个很好的折衷之间的手持采集宽带光谱的需求和深度切片成像在癌症应用的需要。 基于我们使用前向有限元建模程序对乳腺临床数据的分析，我们认为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结构范围（图XXX）。 左上角的图片代表了肿瘤的传统观点：在背景（下标'bkg'）和异质性（下标'het'）之间整齐分割的光学特性的扰动。 在典型的漫射光谱测量中，源（S）和检测器（D）在组织上串联扫描。 提供了实际漫射光学光谱测量的结果（圆圈），与使用由有限元方法提供的SDA的辐射传输模型的正向模型模拟（正方形）进行比较。 我们发现，没有一组物理的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，临床和模拟数据共享更接近的一致性（右下）。 在10例患者的初步研究中，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。 对于恶性病变尤其如此。 病变的较大空间范围放宽了可视化目标所需的源-检测器对的数量。 此外，通过将光学性质的空间分布约束为高斯（或其他）分布所给出的那些，也减少了重构变量的数量。 使用这种方法的简单重建，我们称之为“稀疏层析成像”，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以与任何成像数据一起使用，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仪器对乳腺病变进行扫描。 将在VTS内处理数据，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，我们调用具有相对于背景光学性质的光学吸收和散射性质的扰动的高斯空间分布的乳腺病变的有限元模型基于扩散的模型。 然后，使用有限元方法用该模型生成模拟数据。 接下来，将使用相同的半无限均匀扩散模型的相同最小二乘拟合处理模拟数据。 最后，我们将比较我们的临床观察与我们的模拟结果，在卡方意义上。 这个过程将被迭代，直到卡方被最小化。 我们意识到，这一过程实际上是“模糊”目标的位置。 然而，我们并不认为这种对肿瘤的描述是非物理的，因为众所周知，与放射学确定的大小相比，肿瘤的边缘可以相当广泛。