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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Deep Learning Based Image Reconstruction for Diffuse Optical Tomography
verfasst von : Hanene Ben Yedder, Aïcha BenTaieb, Majid Shokoufi, Amir Zahiremami, Farid Golnaraghi, Ghassan Hamarneh
Erschienen in: Machine Learning for Medical Image Reconstruction
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Abstract
Diffuse optical tomography (DOT) is a relatively new imaging modality that has demonstrated its clinical potential of probing tumors in a non-invasive and affordable way. Image reconstruction is an ill-posed challenging task because knowledge of the exact analytic inverse transform does not exist a priori, especially in the presence of sensor non-idealities and noise. Standard reconstruction approaches involve approximating the inverse function and often require expert parameters tuning to optimize reconstruction performance. In this work, we evaluate the use of a deep learning model to reconstruct images directly from their corresponding DOT projection data. The inverse problem is solved by training the model via training pairs created using physics-based simulation. Both quantitative and qualitative results indicate the superiority of the proposed network compared to an analytic technique.