ArticleJournal of applied clinical medical physics2026
Simultaneous synthesis of perfusion and ventilation images from CT using a dual-decoder residual attention network for lung disease diagnosis.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDeep learning algorithms can synthesize pulmonary functional images from CT images. However, previous studies have only been able to predict either ventilation or perfusion from CT, limiting the holistic evaluation of lung function. PURPOSE: This study aimed to develop a deep learning-based framework for simultaneously generating lung perfusion and ventilation images from three-dimensional CT.
methodsA total of 98 cases who underwent single-photon emission CT perfusion images (SPECT PI) with
resultsOverall, DDRAN and RAN achieved comparable performance. The average SSIM values of the DDRAN/RAN model were 0.871/0.866 (p < 0.05) for PI and 0.830/0.825 (p < 0.05) for VI, and the Rs values were 0.836/0.819 and 0.732/0.731, respectively. The DDRAN/RAN model achieved average DSC values of 0.795/0.796 for PI and 0.708/0.718 for VI in low-function regions, and 0.857/0.849 for PI and 0.793/0.793 for VI in high-function regions. In the two-part reader study, the synthesized perfusion and ventilation images received almost acceptable scores across all experience levels and demonstrated diagnostic potential.
conclusionsWe 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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