Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information.
Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information.
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DOI：
10.1364/boe.388816
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发表时间：
2020-05
影响因子：
3.4
通讯作者：
Menghao Zhang;K. M. S. Uddin;Shuying Li;Quing Zhu
中科院分区：
文献类型：
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作者：
Menghao Zhang;K. M. S. Uddin;Shuying Li;Quing Zhu
超声引导下的弥散光学断层扫描(DOT)是一种很有前途的无创功能成像技术，可用于乳腺癌的诊断和监测乳腺癌的治疗效果。然而，由于较大的病变是高度吸收的，使用反射几何重建这些病变可能会出现光阴影，这导致对其深层部分的不准确量化。这里我们提出了一种结合半自动交互神经网络(CNN)的深度正则化重建算法，用于吸收分布的深度相关重建。CNN片段共同注册US以提取空间和深度先验，深度正则化算法将这些参数合并到重建中。通过仿真和模型数据验证，该算法能显著改善重建的大目标吸收图的深度分布。对26名有较大乳房病变的患者进行评估，该算法显示这些病变的吸收图从上到下的重建均匀度提高了2.4到3倍。
Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising non-invasive functional imaging technique for diagnosing breast cancer and monitoring breast cancer treatment response. However, because larger lesions are highly absorbing, reconstructions of these lesions using reflection geometry may exhibit light shadowing, which leads to inaccurate quantification of their deeper portions. Here we propose a depth-regularized reconstruction algorithm combined with a semi-automated interactive neural network (CNN) for depth-dependent reconstruction of absorption distribution. CNN segments co-registered US to extract both spatial and depth priors, and the depth-regularized algorithm incorporates these parameters into the reconstruction. Through simulation and phantom data, the proposed algorithm is shown to significantly improve the depth distribution of reconstructed absorption maps of large targets. Evaluated with 26 patients with larger breast lesions, the algorithm shows 2.4 to 3 times improvement in the top-to-bottom reconstructed homogeneity of the absorption maps for these lesions.