Deep learning enabled high-speed image reconstruction for breast diffuse optical tomography

Deng, Bin; Gu, Hanxue; Carp, Stefan A.

doi:10.1117/12.2577736

Cited by 4 publications

(3 citation statements)

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“…Nizam et al and Deng et al have demonstrated progress in improving CW diffuse optical tomography reconstruction quality using a variation of the automated transform by manifold approximation (AUTOMAP) architecture. [43][44][45] Li et al 46 has applied deep learning and DOT to clinical applications, improving the accuracy of breast tumor imaging. Deep learning has also been applied for generating data in DOT applications.…”

Section: Data-driven Modelsmentioning

confidence: 99%

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Unrolled-DOT: an interpretable deep network for diffuse optical tomography

et al. 2023

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Significance: Imaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning.Aim: We used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch.Approach: Our model "Unrolled-DOT" uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers. Results:In experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers. Conclusion:We demonstrated a learning-based ToF-DOT inverse solver that achieves state-ofthe-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.

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Section: Data-driven Modelsmentioning

confidence: 99%

“…and Deng et al. have demonstrated progress in improving CW diffuse optical tomography reconstruction quality using a variation of the automated transform by manifold approximation (AUTOMAP) architecture 43 – 45 Li et al 46 .…”

Section: Related Workmentioning

confidence: 99%

Unrolled-DOT: an interpretable deep network for diffuse optical tomography

et al. 2023

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“…The work of Deng et al. 130 extended on the AUTOMAP architecture 131 shown in Fig. 1(a) , where an FC layer inputs the data into an encoder–decoder structure that is followed by a U-Net arrangement for image denoising and quality improvement.…”

Section: Deep Learning For Diffuse Optics-based Tomographic Imagingmentioning

confidence: 99%

Deep learning in macroscopic diffuse optical imaging

et al. 2022

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. Significance : Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis. Aim : We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI). Approach : First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography. Results : The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships. Conclusions : The heavily validated capability of DL’s use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient’s bedside.

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Learn an Index Operator by CNN for Solving Diffusive Optical Tomography: A Deep Direct Sampling Method

Jiang

Guo

2023

J Sci Comput

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In this work, we investigate the diffusive optical tomography (DOT) problem in the case that limited boundary measurements are available. Motivated by the direct sampling method (DSM) proposed in Chow et al. (SIAM J Sci Comput 37(4):A1658–A1684, 2015), we develop a deep direct sampling method (DDSM) to recover the inhomogeneous inclusions buried in a homogeneous background. In this method, we design a convolutional neural network to approximate the index functional that mimics the underling mathematical structure. The benefits of the proposed DDSM include fast and easy implementation, capability of incorporating multiple measurements to attain high-quality reconstruction, and advanced robustness against the noise. Numerical experiments show that the reconstruction accuracy is improved without degrading the efficiency, demonstrating its potential for solving the real-world DOT problems.

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Deep learning enabled high-speed image reconstruction for breast diffuse optical tomography

Cited by 4 publications

References 0 publications

Unrolled-DOT: an interpretable deep network for diffuse optical tomography

Unrolled-DOT: an interpretable deep network for diffuse optical tomography

Deep learning in macroscopic diffuse optical imaging

Learn an Index Operator by CNN for Solving Diffusive Optical Tomography: A Deep Direct Sampling Method

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