Abstract
Diffuse optical tomography (DOT) leverages near-infrared light propagation through in vivo tissue to assess its optical properties and identify abnormalities such as cancerous lesions. While this relatively new optical imaging modality is cost-effective and non-invasive, its inverse problem (i.e., recovering an image from raw signal measurements) is ill-posed, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. Solving the inverse problem becomes even more challenging in the case of limited-angle data acquisition given the restricted number of sources and sensors, the sparsity of the recovered information, and the presence of noise, representative of real world acquisition environments. Traditional optimization-based reconstruction methods are computationally intensive and thus too slow for real-time imaging applications. We propose a novel image reconstruction method for breast cancer DOT imaging. Our method is highlighted by two components: (i) a deep learning network with a novel hybrid loss, and (ii) a distribution transfer learning module. Our model is designed to focus on lesion specific information and small reconstruction details to reduce reconstruction loss and lesion localization errors. The transfer learning module alleviates the need for real training data by taking advantage of cross-domain learning. Both quantitative and qualitative results demonstrate that the proposed method’s accuracy surpasses existing methods’ in detecting tissue abnormalities.
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Acknowledgments
We thank NVIDIA Corporation for the donation of Titan X GPUs used in this research, Compute Canada for HPC resources, Michael Smith Foundation, BC Cancer Agency, and the Natural Sciences and Engineering Research Council of Canada (NSERC) and NSERC-CREATE-Bioinformatics for partial funding. The authors also thank Dr. Ramani Ramaseshan from the BC Cancer Agency for his suggestions.
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Ben Yedder, H., Shokoufi, M., Cardoen, B., Golnaraghi, F., Hamarneh, G. (2019). Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_8
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DOI: https://doi.org/10.1007/978-3-030-32239-7_8
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