International journal of radiation oncology, biology, physics · 2026
Multimodal Contrast-Free Pulmonary Perfusion Imaging by Integrating Computed Tomography and Magnetic Resonance Imaging for Enhanced Lung Function Assessment.
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Hu Dianlin, Li Bing, Li Hui, Ge Hong, Xiong Tianyu, Chen Zhi, Huang Yuhua, Li Tian, Ren Ge, Cai Jing
Аннотация
PURPOSE: Anatomic image-driven lung function imaging methods have been explored for thoracic radiation therapy, but most contrast-free approaches rely on unimodal surrogates. This study aimed to develop a multimodal contrast-free pulmonary perfusion reconstruction framework multimodal contrast-free perfusion (MCF-Q) that integrates computed tomography (CT) and magnetic resonance imaging (MRI) to leverage complementary anatomic and functional information from routinely acquired CT and noncontrast MRI, improve agreement with single-photon emission CT perfusion (SPECT-Q), and explore its potential to support functional lung avoidance radiation therapy (FLART).
METHODS AND MATERIALS: This prospective analysis included 21 patients with lung cancer who underwent pulmonary SPECT-Q, CT, and 1H MRI. MCF-Q adopted a dual-branch deep learning architecture to extract complementary features from CT and MRI and fuse them into pulmonary perfusion maps. Sevenfold cross-validation was performed to evaluate voxel-wise and function-wise agreement between MCF-Q and SPECT-Q, including Spearman's correlation coefficient (R) and the Dice similarity coefficient. The dosimetric analysis was also conducted by comparing a conventional radiation therapy plan with FLART plans guided by different perfusion maps.
RESULTS: For voxel-wise assessment, the MCF-Q achieved an R value of 0.7831 ± 0.0821. For function-wise similarity, the MCF-Q gained the Dice similarity coefficient value of 0.8396 ± 0.0379 in high-functional regions, and 0.7680 ± 0.0555 in low-functional regions. All metrics calculated from MCF-Q showed significant improvement over single-modality-based lung function imaging methods. In dosimetric performance, the MCF-Q-guided FLART achieved better dose sparing in high-functional regions, while maintaining comparable whole-lung and organ-at-risk dose metrics.
CONCLUSIONS: In this study, the proposed MCF-Q demonstrated the feasibility of multimodal perfusion reconstruction from CT and MRI, with improved agreement with SPECT-Q, and provided radiation therapy planning-relevant functional information that may facilitate functional lung avoidance strategies. These findings support the value of integrating routinely acquired CT and MRI for contrast-free, planning-relevant perfusion estimation, warranting validation in larger cohorts.
METHODS AND MATERIALS: This prospective analysis included 21 patients with lung cancer who underwent pulmonary SPECT-Q, CT, and 1H MRI. MCF-Q adopted a dual-branch deep learning architecture to extract complementary features from CT and MRI and fuse them into pulmonary perfusion maps. Sevenfold cross-validation was performed to evaluate voxel-wise and function-wise agreement between MCF-Q and SPECT-Q, including Spearman's correlation coefficient (R) and the Dice similarity coefficient. The dosimetric analysis was also conducted by comparing a conventional radiation therapy plan with FLART plans guided by different perfusion maps.
RESULTS: For voxel-wise assessment, the MCF-Q achieved an R value of 0.7831 ± 0.0821. For function-wise similarity, the MCF-Q gained the Dice similarity coefficient value of 0.8396 ± 0.0379 in high-functional regions, and 0.7680 ± 0.0555 in low-functional regions. All metrics calculated from MCF-Q showed significant improvement over single-modality-based lung function imaging methods. In dosimetric performance, the MCF-Q-guided FLART achieved better dose sparing in high-functional regions, while maintaining comparable whole-lung and organ-at-risk dose metrics.
CONCLUSIONS: In this study, the proposed MCF-Q demonstrated the feasibility of multimodal perfusion reconstruction from CT and MRI, with improved agreement with SPECT-Q, and provided radiation therapy planning-relevant functional information that may facilitate functional lung avoidance strategies. These findings support the value of integrating routinely acquired CT and MRI for contrast-free, planning-relevant perfusion estimation, warranting validation in larger cohorts.