Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.
Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.
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DOI：
10.1117/1.jbo.27.3.036003
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发表时间：
2022-03
影响因子：
3.5
通讯作者：
Arridge S
中科院分区：
文献类型：
--
作者：
Di Sciacca G;Maffeis G;Farina A;Dalla Mora A;Pifferi A;Taroni P;Arridge S
扩散光学层析成像是一个不适定问题。结合超声可以改善应用于乳腺癌诊断的扩散光学断层扫描的结果，并允许病变分类。提供一个仿真管道，用于评估具有并发超声信息的漫射光学断层扫描的重建和分类方法。利用VICTRE软件建立了一套乳腺良恶性病变数字模型。声学和光学特性被分配给体模，用于生成B模式图像和光学数据。基于两区域非线性拟合并结合超声信息的重建算法进行了测试。将机器学习分类方法应用于重建值，以在重建后将病变区分为良性和恶性。该方法使我们能够生成逼真的US和光学数据，并测试了大量的现实模拟的两个区域的重建方法。当从超声图像中提取信息时，至少75%的病变被正确分类。在理想的两区分离条件下，准确率高于80%。实现了用于生成真实超声和漫射光学数据的管道。应用于具有非线性光学模型和形态信息的光学重建的机器学习方法允许将恶性病变与良性病变区分开。
Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions. To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information. A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction. The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%. A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.