Impact of a deep-learning image reconstruction algorithm on ventilation and perfusion parameters derived by phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) in a prospective healthy cohort

This is the authors' abstract. We don't add key points for this paper.

AIM: Deep learning (DL)-based reconstruction algorithms are increasingly integrated into clinical practice, offering the potential for shorter imaging times and improved image quality. This study is the first to evaluate whether DL-reconstructed magnetic resonance imaging (MRI) images affect ventilation and perfusion parameters derived from phase-resolved functional lung (PREFUL) MRI. MATERIALS AND METHODS: This prospective study included 24 healthy volunteers (12 male, mean age: 28.9 ± 12.8 years) with normal lung function confirmed by spirometry. Free-breathing lung MRI was performed at 1.5T using 2D fast spoiled gradient echo sequences. Raw imaging data were reconstructed using both conventional non-DL- and DL-based methods. Image quality was assessed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). PREFUL MRI was used to derive regional ventilation (Rvent), flow-volume loop correlation metric (FVL-CM), perfusion (Q), ventilation and perfusion defect percentages (VDPs and QDPs), and ventilation-perfusion (V/Q) match. Parameters were compared using paired statistical tests and Bland-Altman analysis. RESULTS: DL-based reconstruction significantly improved image quality (SNR: 41.7 ± 19.5 vs 18.0 ± 6.5; contrast-to-noise: 179.9 ± 84.4 vs 75.3 ± 25.7; both P<.001). No significant differences were found between DL and non-DL reconstructions for any PREFUL-derived parameters. Bland-Altman analysis showed good agreement and minimal bias across all functional metrics and strong to near-perfect correlation was found (r=0.8998 to 0.9998, P<.001). CONCLUSION: DL-based image reconstruction enhances image quality in lung MRI without altering ventilation or perfusion quantification from PREFUL analysis. In healthy individuals with normal PFTs, this approach can be safely integrated into functional lung MRI workflows. These results highlight the preservation of image characteristics with DL-based reconstruction and the robustness of the PREFUL method.