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FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation.FReSCO:使用深度伪影抑制和分割技术实现低延迟心输出量监测的血流重建和分割。
Magn Reson Med. 2022 Nov;88(5):2179-2189. doi: 10.1002/mrm.29374. Epub 2022 Jul 4.
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ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning.ArtifactID:使用深度学习识别低场 MRI 脑部中的伪影。
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8
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二维相位对比流动 MRI 的在线自动质量控制,用于适应特定于个体的扫描时间。

Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.

机构信息

Laboratory of Imaging Technology, Cardiovascular Branch, Division of Intramural Research, National Heart Lung & Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.

Laboratory of Cardiovascular Intervention, Cardiovascular Branch, Division of Intramural Research, National Heart Lung & Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.

出版信息

Magn Reson Med. 2024 Aug;92(2):751-760. doi: 10.1002/mrm.30083. Epub 2024 Mar 12.

DOI:10.1002/mrm.30083
PMID:38469944
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11142871/
Abstract

PURPOSE

To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR.

METHODS

We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR-driven "stop" was assessed inline.

RESULTS

SNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR-driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery).

CONCLUSION

The inline quality control enables subject-specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject-specific scan time.

摘要

目的

开发一种在线自动质量控制方法,以实现具有特定于受检者的扫描时间的一致诊断图像质量,并展示该方法在二维相位对比流 MRI 中达到预定 SNR 的应用。

方法

我们设计了图像重建和数据采集之间的闭环反馈框架,以间歇性地检查 SNR(每 20 秒一次),并在达到目标 SNR 时自动停止采集。对自由呼吸二维伪黄金角度螺旋相位对比序列进行了修改,以从重建中听取图像质量信息。在 0.55T 对 10 名健康志愿者和 1 名患者进行成像。目标 SNR 是基于对心输出量误差的回顾性分析选择的,在线评估自动 SNR 驱动“停止”的性能。

结果

SNR 计算和在线部署的自动分割在 20 秒内是可行的。SNR 驱动的采集时间为 2 分 39 秒±67 秒(主动脉)和 3 分钟±80 秒(主肺动脉),6 名健康志愿者的采集时间最短/最长为 1 分 43 秒/4 分 52 秒(主动脉)和 1 分 43 秒/5 分 50 秒(主肺动脉),而在定量流量测量中,相对绝对误差低于 2.1%(主动脉)和 6.3%(主肺动脉)的情况下,确保了诊断性测量。

结论

在线质量控制可实现特定于受检者的优化扫描时间,同时确保一致的诊断图像质量。在人群中自动停止时间的分布揭示了特定于受检者的扫描时间的价值。