Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging Article Swipe

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Lingting Zhu , Yizheng Chen , Lianli Liu , Lei Xing , Lequan Yu ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.19420

Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI. We reveal two distinct types of features in multi-modality imaging, namely intra- and inter-modality features, and present a multi-sensor learning (MSL) framework to utilize the crossover inter-modality features for augmented multi-modality imaging. The MSL imaging approach breaks down the boundaries of traditional imaging modalities and allows for optimal hybridization of CT and MRI, which maximizes the use of sensory data. We showcase the effectiveness of our DMI strategy through synergetic CT-MRI brain imaging. The principle of DMI is quite general and holds enormous potential for various DMI applications across disciplines.

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Modality (human–computer interaction) Sensory system Transfer of learning Computer science Neuroscience Artificial intelligence Psychology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.19420
PDF: https://arxiv.org/pdf/2409.19420
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4403812018

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DOI: https://doi.org/10.48550/arxiv.2409.19420

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Title: Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-09-28

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Authors: Lingting Zhu, Yizheng Chen, Lianli Liu, Lei Xing, Lequan Yu

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Landing page: https://arxiv.org/abs/2409.19420

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PDF URL: https://arxiv.org/pdf/2409.19420

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Concepts: Modality (human–computer interaction), Sensory system, Transfer of learning, Computer science, Neuroscience, Artificial intelligence, Psychology

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Cited by: 0

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