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实时流量与快速 GPU 重建,连续评估心输出量。

Real-time flow with fast GPU reconstruction for continuous assessment of cardiac output.

机构信息

UCL Institute of Cardiovascular Science, Centre for Cardiovascular Imaging, London, United Kingdom.

出版信息

J Magn Reson Imaging. 2012 Dec;36(6):1477-82. doi: 10.1002/jmri.23736. Epub 2012 Jun 28.

Abstract

PURPOSE

To demonstrate the feasibility of real-time phase contrast magnetic resonance (PCMR) assessment of continuous cardiac output with a heterogeneous (CPU/GPU) system for online image reconstruction.

MATERIALS AND METHODS

Twenty healthy volunteers underwent aortic flow examination during exercise using a real-time spiral PCMR sequence. Acquired data were reconstructed in online fashion using an iterative sensitivity encoding (SENSE) algorithm implemented on an external computer equipped with a GPU card. Importantly, data were sent back to the scanner console for viewing. A multithreaded CPU implementation of the real-time PCMR reconstruction was used as a reference point for the online GPU reconstruction assessment and validation. A semiautomated segmentation and registration algorithm was applied for flow data analysis.

RESULTS

There was good agreement between the GPU and CPU reconstruction (-0.4 ± 0.8 mL). There was a significant speed-up compared to the CPU reconstruction (15×). This translated into the flow data being available on the scanner console ≈9 seconds after acquisition finished. This compares to an estimated time using the CPU implementation of 83 minutes.

CONCLUSION

Our heterogeneous image reconstruction system provides a base for translation of complex MRI algorithms into clinical workflow. We demonstrated its feasibility using real-time PCMR assessment of continuous cardiac output as an example.

摘要

目的

展示使用异构(CPU/GPU)系统进行在线图像重建的实时相位对比磁共振(PCMR)连续心输出量评估的可行性。

材料与方法

二十名健康志愿者在运动过程中使用实时螺旋 PCMR 序列进行主动脉流量检查。使用配备 GPU 卡的外部计算机上的迭代灵敏度编码(SENSE)算法实时重建采集数据。重要的是,数据被发回扫描器控制台进行查看。使用实时 PCMR 重建的多线程 CPU 实现作为在线 GPU 重建评估和验证的参考点。应用半自动分割和配准算法进行流量数据分析。

结果

GPU 和 CPU 重建之间存在良好的一致性(-0.4 ± 0.8 毫升)。与 CPU 重建相比,速度明显加快(15 倍)。这意味着流量数据在采集完成后约 9 秒即可在扫描器控制台获得。相比之下,使用 CPU 实现估计需要 83 分钟。

结论

我们的异构图像重建系统为将复杂的 MRI 算法转化为临床工作流程提供了基础。我们使用实时 PCMR 连续心输出量评估作为示例,证明了其可行性。

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