Stewart Neil J, de Arcos Jose, Biancardi Alberto M, Collier Guilhem J, Smith Laurie J, Norquay Graham, Marshall Helen, Brau Anja C S, Lebel R Marc, Wild Jim M
POLARIS, Division of Clinical Medicine, School of Medicine & Population Health, Faculty of Health, The University of Sheffield, Sheffield, UK.
Insigneo Institiute, The University of Sheffield, Sheffield, UK.
Magn Reson Med. 2024 Dec;92(6):2546-2559. doi: 10.1002/mrm.30250. Epub 2024 Aug 18.
To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for improving the SNR of hyperpolarized Xe lung ventilation MRI.
Xe lung ventilation MRI data acquired from patients with asthma and/or chronic obstructive pulmonary disease (COPD) were retrospectively reconstructed with a commercial DL reconstruction pipeline at five different denoising levels. Quantitative imaging metrics of lung ventilation including ventilation defect percentage (VDP) and ventilation heterogeneity index (VH) were compared between each set of DL-reconstructed images and alternative denoising strategies including: filtering, total variation denoising and higher-order singular value decomposition. Structural similarity between the denoised and original images was assessed. In a prospective study, the feasibility of using SNR gains from DL reconstruction to allow natural-abundance xenon MRI was evaluated in healthy volunteers.
Xe ventilation image SNR was improved with DL reconstruction when compared with conventionally reconstructed images. In patients with asthma and/or COPD, DL-reconstructed images exhibited a slight positive bias in ventilation defect percentage (1.3% at 75% denoising) and ventilation heterogeneity index (˜1.4) when compared with conventionally reconstructed images. Additionally, DL-reconstructed images preserved structural similarity more effectively than data denoised using alternative approaches. DL reconstruction greatly improved image SNR (greater than threefold), to a level that Xe ventilation imaging using natural-abundance xenon appears feasible.
DL-based image reconstruction significantly improves Xe ventilation image SNR, preserves structural similarity, and leads to a minor bias in ventilation metrics that can be attributed to differences in the image sharpness. This tool should help facilitate cost-effective Xe ventilation imaging with natural-abundance xenon in the future.
评估基于深度学习（DL）的重建方法在提高超极化氙气肺通气磁共振成像（MRI）信噪比（SNR）方面的可行性和实用性。
对从哮喘和/或慢性阻塞性肺疾病（COPD）患者获取的氙气肺通气MRI数据，使用商业DL重建流程在五个不同去噪水平上进行回顾性重建。比较每组DL重建图像与其他去噪策略（包括滤波、全变差去噪和高阶奇异值分解）之间肺通气定量成像指标，包括通气缺陷百分比（VDP）和通气异质性指数（VH）。评估去噪图像与原始图像之间的结构相似性。在一项前瞻性研究中，评估了在健康志愿者中利用DL重建提高的SNR来进行自然丰度氙气MRI的可行性。
与传统重建图像相比，DL重建提高了氙气通气图像的SNR。在哮喘和/或COPD患者中，与传统重建图像相比，DL重建图像在通气缺陷百分比（75%去噪时为1.3%）和通气异质性指数（约1.4）方面表现出轻微的正偏差。此外，与使用其他方法去噪的数据相比，DL重建图像更有效地保留了结构相似性。DL重建大大提高了图像SNR（超过三倍），达到了使用自然丰度氙气进行氙气通气成像似乎可行的水平。
基于DL的图像重建显著提高了氙气通气图像的SNR，保留了结构相似性，并在通气指标上导致了轻微偏差，这可归因于图像清晰度的差异。该工具应有助于未来利用自然丰度氙气实现经济高效的氙气通气成像。
