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

IF 2 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical radiology Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI:10.1016/j.crad.2026.107248

J. Kroschke , Y. Huber , B. Kerber , M. Lohezic , T. Frauenfelder

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引用次数: 0

Abstract

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.

查看原文本刊更多论文

深度学习图像重建算法对前瞻性健康队列中相位分辨功能肺（PREFUL）磁共振成像（MRI）得出的通气和灌注参数的影响

目的：基于深度学习（DL）的重建算法越来越多地融入临床实践，为缩短成像时间和提高图像质量提供了潜力。本研究首次评估了dl重建磁共振成像（MRI）图像是否会影响由相分辨功能肺（PREFUL） MRI得出的通气和灌注参数。材料与方法：本前瞻性研究纳入24例经肺量测定证实肺功能正常的健康志愿者，其中男性12例，平均年龄28.9±12.8岁。使用2D快速破坏梯度回波序列在1.5T下进行自由呼吸肺MRI。使用传统的非dl和基于dl的方法重建原始成像数据。通过信噪比（SNR）和噪声对比比（CNR）评估图像质量。使用PREFUL MRI计算区域通气量（Rvent）、流量-容积环相关度量（fv1 - cm）、灌注(Q)、通气和灌注缺陷百分比（VDPs和QDPs）以及通气-灌注（V/Q）匹配。参数比较采用配对统计检验和Bland-Altman分析。结果：基于dl的图像重建显著改善了图像质量（信噪比：41.7±19.5 vs 18.0±6.5；噪比：179.9±84.4 vs 75.3±25.7）。结论：基于dl的图像重建在不改变PREFUL分析的通气和灌注量化的情况下提高了肺部MRI图像质量。在pft正常的健康个体中，这种方法可以安全地整合到功能性肺MRI工作流程中。这些结果突出了基于dl的重建对图像特征的保留以及PREFUL方法的鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical radiology 医学-核医学

CiteScore

4.70

自引率

3.80%

发文量

528

审稿时长

76 days

期刊介绍： Clinical Radiology is published by Elsevier on behalf of The Royal College of Radiologists. Clinical Radiology is an International Journal bringing you original research, editorials and review articles on all aspects of diagnostic imaging, including: • Computed tomography • Magnetic resonance imaging • Ultrasonography • Digital radiology • Interventional radiology • Radiography • Nuclear medicine Papers on radiological protection, quality assurance, audit in radiology and matters relating to radiological training and education are also included. In addition, each issue contains correspondence, book reviews and notices of forthcoming events.