Cong Wenxiang, Intes Xavier, Wang Ge
Rensselaer Polytechnic Institute, Biomedical Imaging Center, Department of Biomedical Engineering, T, United States.
J Biomed Opt. 2017 Sep;22(9):1-6. doi: 10.1117/1.JBO.22.9.096011.
Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.
扩散光学乳腺成像利用近红外（NIR）光在组织中的传播来评估组织的光学特性，以识别异常组织。这种光学成像方法灵敏、经济高效，且不涉及任何电离辐射。然而，扩散光学断层扫描（DOT）的图像重建是一个非线性逆问题，由于数据噪声、近红外光散射和测量不完整性，存在严重的不适定性。提出了一种用于检测乳腺癌的图像重建方法。该方法将图像重建问题分解为异常组织的定位和吸收变化的量化。异常组织的定位基于一个适定的优化模型，可通过差分进化优化方法求解以实现稳定重建。然后在相对较小范围的局部区域中确定异常吸收的量化，这些区域可能存在潜在肿瘤。因此，可以大大减少未知吸收变量的数量，以克服DOT的欠定性质。进行了数值模拟实验以验证所提方法的优点，结果表明该图像重建方法在识别异常组织方面稳定且准确，并且对数据的测量噪声具有鲁棒性。
