A novelty convolutional neural network based direct reconstruction for MRI guided diffuse optical tomography

Wanlong Zhang; Zhe Li; Zhonghua Sun; Kebin Jia; Jinchao Feng

doi:10.1117/12.2606836

2 March 2022 A novelty convolutional neural network based direct reconstruction for MRI guided diffuse optical tomography

Wanlong Zhang, Zhe Li, Zhonghua Sun, Kebin Jia, Jinchao Feng

Author Affiliations +

Proceedings Volume 11952, Multimodal Biomedical Imaging XVII; 119520B (2022) https://doi.org/10.1117/12.2606836
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

Diffuse Optical Tomography (DOT) is a promising non-invasive and relatively low-cost biomedical image technology. The aim of DOT is to reconstruct optical properties of the tissue from boundary measurements. However, the DOT reconstruction is a severely ill-posed problem. To reduce the ill-posedness of DOT and to improve image quality, imageguided DOT has attracted more attention. In this paper, a reconstruction algorithm for DOT is proposed based on the convolutional neural network (CNN). It uses both optical measurements and magnetic resonance imaging (MRI) images as the input of the CNN, and directly reconstructs the distribution of absorption coefficient. The merits of the proposed algorithm are without segmenting MRI images and modeling light propagation. The performance of the proposed algorithm is evaluated using numerical simulation experiments. Our results reveal that the proposed method can achieve superior performance compared with conventional reconstruction algorithms and other deep learning methods. Our result shows that the average SSIM of reconstructed images is above 0.88, and the average PSNR is more than 35 dB.

Conference Presentation

Citation Download Citation

Wanlong Zhang, Zhe Li, Zhonghua Sun, Kebin Jia, and Jinchao Feng "A novelty convolutional neural network based direct reconstruction for MRI guided diffuse optical tomography", Proc. SPIE 11952, Multimodal Biomedical Imaging XVII, 119520B (2 March 2022); https://doi.org/10.1117/12.2606836

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