An automated pipeline for computation and analysis of functional ventilation and perfusion lung MRI with matrix pencil decomposition: TrueLung.

Orso Pusterla, Corin Willers, Robin Sandkühler, Simon Andermatt, Sylvia Nyilas, Philippe C Cattin, Philipp Latzin, Oliver Bieri, Grzegorz Bauman
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Abstract

Purpose: To introduce and evaluate TrueLung, an automated pipeline for computation and analysis of free-breathing and contrast-agent free pulmonary functional magnetic resonance imaging.

Materials and methods: Two-dimensional time-resolved ultra-fast balanced steady-state free precession acquisitions were transferred to TrueLung, which included image quality checks, image registration, and computation of perfusion and ventilation maps with matrix pencil decomposition. Neural network whole-lung and lobar segmentations allowed quantification of impaired relative perfusion (RQ) and fractional ventilation (RFV). TrueLung delivered functional maps and quantitative outcomes, reported for clinicians in concise documents. We evaluated the pipeline using 1.5T data from 75 children with cystic fibrosis by assessing the feasibility of functional MR imaging, average scan time, and the robustness of the functional outcomes. Whole-lung and lobar segmentations were manually refined when necessary, and the impact on RQ and RFV was quantified.

Results: Functional imaging was feasible in all included CF children without any dropouts. On average, 7.9 ± 1.8 (mean±SD) coronal slice positions per patient were acquired, resulting in a mean scan time of 6min 20s per patient. The whole pipeline required 20min processing time per subject. TrueLung delivered the functional maps of all the subjects for radiological assessment. Quality controlling maps and segmentations lasted 1min 12s per patient. The automated segmentations and quantification of whole-lung defects were satisfying in 88% of patients (97% of slices) and the lobar quantification in 73% (93% of slices). The segmentations refinements required 16s per patient for the whole-lung, and 2min 10s for the lobe masks. The relative differences in RFV and RQ between fully-automated and manually refined data were 0.7% (1.2%) and 2.0% (2.9%) for whole-lung quantification (median, [third quartile]), and excluding two outliers, 1.7% (3.9%) and 1.2% (3.8%) for the lobes, indicating the refinements could be potentially omitted in several patients.

Conclusions: TrueLung quickly delivers functional maps and quantitative outcomes in an objective and standardized way, suitable for radiological and pneumological assessment with minimal manual input. TrueLung can be used for clinical research in cystic fibrosis and might be applied across various lung diseases.

利用矩阵铅笔分解计算和分析功能性通气和灌注肺磁共振成像的自动管道:TrueLung.
目的:介绍并评估用于计算和分析自由呼吸和无造影剂肺功能磁共振成像的自动化管道 TrueLung:将二维时间分辨超快平衡稳态自由前驱采集图像传输到TrueLung,其中包括图像质量检查、图像配准以及利用矩阵铅笔分解计算灌注和通气图。神经网络全肺和肺叶分割可量化受损的相对灌注(RQ)和分数通气(RFV)。TrueLung 可提供功能图和定量结果,并以简洁的文档形式报告给临床医生。我们使用 75 名囊性纤维化患儿的 1.5T 数据对该管道进行了评估,评估了功能磁共振成像的可行性、平均扫描时间和功能结果的稳健性。必要时对全肺和肺叶分割进行人工细化,并量化对 RQ 和 RFV 的影响:结果:所有被纳入研究的 CF 儿童都能进行功能成像,无一辍学。平均每位患者采集了 7.9 ± 1.8(平均值±SD)个冠状切片位置,每位患者的平均扫描时间为 6 分 20 秒。每个受试者的整个管道处理时间为 20 分钟。TrueLung 提供了所有受试者的功能图,用于放射学评估。质量控制图和分割每个患者需要 1 分 12 秒。88%的患者(97%的切片)和73%的患者(93%的切片)对全肺缺陷的自动分割和量化结果表示满意,肺叶量化结果也令人满意。每位患者的全肺分割细化需要 16 秒,肺叶掩膜需要 2 分 10 秒。全自动和手动细化数据的 RFV 和 RQ 的相对差异为:全肺量化为 0.7% (1.2%) 和 2.0% (2.9%)(中位数,[第三四分位数]),排除两个异常值后,肺叶为 1.7% (3.9%) 和 1.2% (3.8%),这表明在一些患者中可能会省略细化:TrueLung能以客观、标准化的方式快速提供功能图谱和定量结果,适用于放射学和肺学评估,只需极少的人工输入。TrueLung 可用于囊性纤维化的临床研究,也可应用于各种肺部疾病。
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