QUANTITATIVE ULTRASOUND IMAGE PROCESSING FOR CHARACTERIZATION OF PROSTATE CANCER

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 我们正在优化经直肠超声耦合光学断层扫描（TRUST），通过利用经直肠超声（US）的解剖细节和经直肠近红外（NIR）断层扫描的功能信息来改善图像引导前列腺活检。 经直肠近红外断层扫描提供了一种新的信息维度，用于确定US上可疑病变的恶性程度，并可能检测到US上不明显的病变。 经直肠超声不仅可以保证近红外辐射源的准确定位，而且可以为引导的近红外图像重建提供空间先验。 在这个项目中，我们建议定量评估TRUST图像的前列腺在体内和前列腺癌的组织学图像。 我们将重点关注US和NIR成像中的重要变量，包括与优化NIR图像重建相关的变量。 我们将首先通过先进的图像处理技术，包括噪声模式抑制和图像分割的预处理US图像提取空间先验信息，以指导近红外图像重建。 接下来，我们将量化的准确性，在美国引导重建的光学特性相对于预定的真值，并使用感知差异计算机模型（PDM），以确定重建图像与先验信息和无之间的视觉差异。 最后，我们将使用组织学图像，通过将细胞或亚细胞细胞器的密度和尺寸分布等信息转换为相关的光学特性，建立前列腺癌的光学特性与异常细胞形态之间的关系。 这将导致理解的光散射光谱的前列腺癌作为一个新的层面进行检测。 这些建议的方法将优化近红外图像重建，并了解前列腺癌的光学病理。这项研究的完成将为评估和优化前列腺癌的TRUST检测建立准确的工具。 该研究成果也可应用于其他多模态成像技术，如PET/CT，SPECT/CT和PET/MRI。

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. We are optimizing trans-rectal ultrasound-coupled optical tomography (TRUST) to improve image guided prostate biopsy by utilizing both the anatomic details of trans-rectal ultrasound (US) and functional information from trans-rectal near infrared (NIR) tomography. Trans-rectal NIR tomography presents a new dimension of information necessary to determining malignancy of lesions suspicious on US and potentially detecting lesions that are otherwise inconspicuous on US. The trans-rectal US not only ensure accurate positioning of the NIR applicator but also can provide spatial prior for guided NIR image reconstruction. In this project, we propose to quantitatively assess TRUST images of prostate in vivo and histological images of prostate cancer. We will focus on important variable in US and NIR imaging, including those associated with optimizing NIR image reconstruction. We will first pre-process US images by the advanced image processing techniques including noise pattern suppression and image segmentation to extract the spatial a priori information to guide NIR image reconstruction. Next, we will quantify the accuracy of optical properties in the US-guided reconstructed with respect to pre-determined true values, and use a perceptual difference computer model (PDM) to determine the visual difference between images reconstructed with and without priori information. Finally, we will establish a relationship between the optical property of prostate cancer and the abnormal cell-morphology using histology images, by converting information such as density and size distribution of cellular or sub-cellular organelles to associated optical properties. This will lead to the understanding of optical scattering spectra of the prostate cancer as a new dimension for detection. These proposed approaches will optimize NIR image reconstruction, and understanding of the optical pathology of prostate cancer. The completion of this research will establish accurate tools for evaluating and optimizing the TRUST detection of prostate cancer. The research outcome may also apply to other multi-modality imaging techniques such as PET/CT, SPECT/CT, and PET/MRI.