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首页> 外文期刊>Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine >Controlling motion artefact levels in MR images by suspending data acquisition during periods of head motion
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Controlling motion artefact levels in MR images by suspending data acquisition during periods of head motion

机译:通过在头部运动期间暂停数据采集来控制MR图像中的运动人工制品水平

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Purpose Head movements are a major source of MRI artefacts. Prospective motion correction techniques significantly improve data quality, but strong motion artefacts may remain in the data. We introduce a framework to suspend data acquisition during periods of head motion over a predefined threshold. Methods Data was acquired with prospective motion correction and an external optical tracking system. A predictor of motion impact was introduced that accounts for the amplitude of the signal acquired at the time of the motion. From this predictor, a threshold was defined to trigger the suspension of data acquisition during periods of motion. The framework was tested on 5 subjects, 2 motion behaviors, and 2 head coils (20 and 64 channels). Results The best improvements in data quality were obtained for a threshold value of 0, equivalent to suspending the acquisition based on head speed alone, at the cost of a long prolongation of scan time. For threshold values ~3.5e ?4 , image quality was largely preserved, and prolongation of scan time was minimal. Artefacts occasionally remained with the 64‐channel head coil for all threshold values, seemingly due to head movement in the sharp sensitivity profile of this coil. Conclusion The proposed suspension strategy is more efficient than relying on head speed alone. The threshold for suspension of data acquisition governs the tradeoff between image degradation due to motion and prolonged scan time, and can be tuned by the user according to the desired image quality and participant's tolerability.
机译:目的头部运动是MRI人工制品的主要来源。前瞻性运动校正技术显着提高数据质量,但是强大的运动伪影可以留在数据中。我们介绍一个框架,以在预定阈值期间在头部运动期间暂停数据采集。方法采用预期运动校正和外部光学跟踪系统获取数据。引入了运动冲击的预测因子,其考虑了在运动时获取的信号的幅度。从该预测器中,定义阈值以在运动时段期间触发数据采集的暂停。该框架在5个受试者,2个运动行为和2个头圈(20和64通道)上进行了测试。结果获得了数据质量的最佳改进,获得了0的阈值,相当于仅基于单独的头速度悬挂采集,以长时间的扫描时间。对于阈值〜3.5e?4,图像质量大大保存,扫描时间的延长是最小的。偶尔的人工制品仍然与所有阈值的64通道头圈保持,似乎是由于该线圈的尖锐敏感性曲线中的头部运动。结论所提出的悬浮策略比单独依赖头速度更有效。数据采集​​暂停的阈值控制由于运动和延长扫描时间,并且可以根据所需的图像质量和参与者的可容忍性来调整图像劣化之间的折衷。

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