MAMMOGRAPHIC IMAGING BY DIFFUSION TOMOGRAPHY

描述(改编自申请者摘要)：该项目的长期目标 研究内容是开发一种近红外(NIR)时间分辨光学成像 系统基于在频域中执行的测量 检测乳腺组织中的肿瘤。因为它强烈地分散在 通过组织的光，从光学产生高质量图像的能力 测量将需要评估已经高度 四分五裂。本研究的具体目标是确定和优化 通过解剖学评价进行数据收集和分析的条件 精确的女性乳房光学(AAO)模型。申请者有 之所以采用这种方法，是因为我们认为它代表了一种高效和 评估和优化关键成像的经济有效的方法 参数，这些参数的条件非常类似于 从实际测量中获得，没有繁多的工程等 实际的限制和更高的成本，几乎可以肯定 在涉及人类受试者的类似研究中会遇到这种情况。这个 申请者将通过分配光学吸收和分配AAO模型 中确定的相应组织结构的散射系数 健康志愿者和健康女性乳腺的分割三维MRI图像 有已知肿瘤性和非肿瘤性病变的患者。测定法 这些系数中的一个将使用前面描述的 新制备薄层的双积分球测量技术 从外科标本中获得的乳房组织切片。明确的 组织类型的分配将通过显微镜检查进行 被污染的相邻组织切片。最佳条件的确定 将基于对数据收集所需的权衡的检查， 解决方案的准确性和所需的计算时间，并将分两个阶段进行 方向。正向问题的解决方案将涉及计算 不同AAO的探测器响应和相关成像操作符 作为目标几何形状和模拟的存在的函数的模型介质 不同大小和对比度的病理性包涵体。这些措施将涉及 评估用于解决问题的新的、高效的代码 输运和扩散方程，从而允许直接回答 扩散解的适宜性的重要问题 方程，它在计算上比前者更有效，在 与解剖学、几何学和光学非常相似的条件 女性乳房的特性。关于图像的反问题 重建将通过解线性摄动方程来评估 对于选定的数据集，使用各种不同的方法 计算效率和处理以下问题的能力 病态和病态。以前开发的层剥离 将对多重网格方案进行评估和进一步改进；这将 包括开发一种小波变换方法。敏感度和 重建图像对数据和成像算子中噪声的准确性， 照明方案、视角和源探测器数量的选择 将探讨位置和其他参数。

DESCRIPTION (Adapted from Applicant's Abstract): The long term goal of this research is to develop a near infrared (NIR) time- resolved optical imaging system based on measurements performed in the frequency domain for the detection of tumors in breast tissue. Because of the intense scattering of light by tissue, the ability to generate high quality images from optical measurements will require evaluation of signals that have been highly scattered. The specific goal of this study is to identify and optimize conditions for data collection and analysis by evaluation of anatomically accurate optical (AAO) models of the female breast. The applicants have adopted this approach because we believe it represents an efficient and cost-effective means for evaluation and optimization of critical imaging parameters under conditions that closely resemble those that would be obtained from actual measurement, without the numerous engineering and other practical constraints and significantly higher costs that almost certainly would be encountered in similar studies involving human subjects. The applicants will construct AAO models by assigning optical absorption and scattering coefficients to the corresponding tissue structures identified in segmented 3-D MRI images of the female breast of healthy volunteers and patients having known neoplastic and non-neoplastic lesions. Determination of these coefficients will be performed using a previously described dual-integrating sphere measurement technique on freshly prepared thin slices of breast tissue obtained from surgical specimens. Definitive assignment of the tissue type will be performed by microscopic examination of stained adjacent tissue sections. Determination of optimal conditions will be based on examination of the tradeoffs required for data collection, solution accuracy, and required computing time, and will proceed in two directions. Solutions to the forward problem will involve computing detector responses and associated imaging operators for the various AAO model media as a function of target geometry and presence of simulated pathological inclusions of varying size and contrast. These will involve evaluation of newly available, highly efficient codes for solution of the transport and diffusion equations, thereby permitting a direct answer to the important question of the suitability of solutions to the diffusion equation, which is computationally more efficient than the former, under conditions that closely resemble the anatomy, geometry and optical properties of the female breast. The inverse problem for image reconstruction will be evaluated by solving a linear perturbation equation for selected data sets, using a variety of approaches that vary in their computational efficiency and ability to deal with problems stemming from ill-conditioning and ill-posedness. Previously developed layer-stripping and multigrid schemes will be evaluated and further improved; this will include development of a wavelet transform method. The sensitivity and accuracy of reconstructed images to noise in the data and imaging operators, choice of illumination scheme, view angle, and number of source-detector positions and other parameters will be explored.