UCL Institute of Cardiovascular Science, Centre for Cardiovascular Imaging, London, United Kingdom.
J Magn Reson Imaging. 2012 Dec;36(6):1477-82. doi: 10.1002/jmri.23736. Epub 2012 Jun 28.
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.
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.
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.
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.
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。
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。
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。
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。
