Multimodal Contrast-Free Pulmonary Perfusion Imaging by Integrating CT and MRI for Enhanced Lung Function Assessment

Anatomy image-driven lung function imaging methods have been explored for thoracic radiotherapy, but most contrast-free approaches rely on unimodal surrogates. This study aimed to develop a multimodal contrast-free pulmonary perfusion reconstruction framework (MCF-Q) that integrates computed tomography (CT) and magnetic resonance imaging (MRI) to leverage complementary anatomical and functional information from routinely acquired CT and non-contrast MRI, improve agreement with single-photon emission computed tomography perfusion (SPECT-Q), and explore its potential to support functional lung avoidance radiotherapy (FLART).

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. Seven-fold 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 (DSC). The dosimetric analysis was also conducted by comparing a conventional radiotherapy (ConvRT) plan with FLART plans guided by different perfusion maps.

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 DSC 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.

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 radiotherapy-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.