Deep learning fusion of multi-channel imaging from polarization-sensitive optical coherence tomography

Research context

Research on electrical energy conversion, storage and generation dates back to the nineteenth century, but only in recent years have scientists begun to investigate the impact of electron spin on these processes. The ability to control and manipulate this intrinsically quantum property of matter opens new approaches to addressing energy science challenges. The chiral-induced spin selectivity (CISS) effect is central to this effort, as it enables control over the transport and generation of both pure spin currents and spin-polarized charge currents. In this Review, we first introduce design strategies for implementing CISS in materials and then describe examples of how CISS has been used to improve electrocatalysis and spintronics. We conclude with a forward-looking perspective on the next steps for leveraging CISS in energy science.

Keywords: Optical coherence tomography, Deep learning, Fusion, Image fusion, Coherence (philosophical gambling strategy), Optical imaging, Diffuse optical imaging, Image processing, Artificial intelligence, Computer science, Optics, Computer vision, Tomography, Medical imaging, Preclinical imaging, Visualization, Convolutional neural network, Speckle noise, Physics, Speckle pattern, Sensor fusion, Hyperspectral imaging, Light scattering

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Cite / 引用

Yunlong Liu, Chen Wang, Paul Calle, Justin Reynolds, S. Ly, Haoyang Cui, Alberto de Armendi, Shashank Shettar, Kar-Ming Fung, Qi Li, Rupa R Haldavnekar, Qinggong Tang, Chongle Pan. Deep learning fusion of multi-channel imaging from polarization-sensitive optical coherence tomography. Biomedical Optics Express (2026). https://doi.org/10.1364/boe.597049

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