Demonstrates a dual-decoder model that synthesizes lung images, suggesting improved diagnosis for lung diseases.
We have developed a dual-decoder residual attention network that enables the simultaneous synthesis of lung perfusion and ventilation images from three-dimensional CT. Preliminary results indicate moderate-to-high structural-wise and functional-wise concordances, and our proposed model demonstrates comparable accuracy when benchmarked against single-decoder models. The synthesized perfusion and ventilation images can potentially be used for precise diagnosis and guiding functional lung avoidance radiotherapy.
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Wang et al. (2026) studied this question.
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