Song Hao, Ruan Dan, Liu Wenyang, Stenger V Andrew, Pohmann Rolf, Fernández-Seara Maria A, Nair Tejas, Jung Sungkyu, Luo Jingqin, Motai Yuichi, Ma Jingfei, Hazle John D, Gach H Michael
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
Med Phys. 2017 Mar;44(3):962-973. doi: 10.1002/mp.12099. Epub 2017 Feb 21.
Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition.
A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers.
The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm.
Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient.
将使用人工神经网络（ANN）进行的呼吸运动预测与伪连续动脉自旋标记（pCASL）MRI相结合，以实现肾脏自由呼吸灌注测量。在本研究中，我们评估了ANN在图像采集期间准确预测肾脏位置的性能。
在ANN训练和pCASL通过延迟测量期间，将笔形束导航器与pCASL序列相结合，以测量肺/膈肌运动。ANN算法在后台同时运行，根据导航器数据在0.7秒15层采集期间预测器官位置。预测结果被提供给脉冲序列，以前瞻性地调整轴向切片采集，使其与预测的器官位置相匹配。在多层采集后立即获取额外的导航器，以评估ANN的性能和准确性。该技术在8名健康志愿者身上进行了测试。
对于这8名志愿者，ANN的均方根误差（RMSE）和平均绝对误差（MAE）分别为1.91±0.17毫米和1.43±0.17毫米。RMSE随着通过延迟而增加。MAE通常在图像采集过程中从第一次预测到最后一次预测逐渐增加。使用±1毫米的目标预测精度时，过冲为23.58%±3.05%。
成功地证明了前瞻性运动校正的呼吸运动预测可用于肾脏自由呼吸灌注MRI。该方法可替代多次屏气，且患者所需的努力最小。
