@article{5fd849b5c58148bc893be7afb0c7b847,
title = "Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys",
abstract = "Purpose: 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. Methods: A pencil-beam navigator was integrated with a pCASL sequence to measure lung/di-aphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concur-rently 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 immedi-ately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers. Results: 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 acquisi-tion. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm. Conclusion: 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.",
keywords = "arterial spin label, artificial neural network, kidney, magnetic resonance imaging, respiratory motion prediction",
author = "Hao Song and Dan Ruan and Wenyang Liu and Stenger, {V. Andrew} and Rolf Pohmann and Fern{\'a}ndez-Seara, {Maria A.} and Tejas Nair and Sungkyu Jung and Jingqin Luo and Yuichi Motai and Jingfei Ma and Hazle, {John D.} and Gach, {H. Michael}",
note = "Funding Information: This research was conducted primarily with the support of National Institutes of Health National Cancer Institute grant R01 CA159471. Our research also received support from the Nevada Cancer Institute, the University of Pittsburgh, Washington University in St. Louis, The University of Texas MD Anderson Cancer Center, and UCLA. We are indebted to Cornell University{\textquoteright}s Yi Wang, Than Nguyen, and Pascal Spincemaille for providing technical expertise during the formative period of this project. We also appreciate the technical assistance of Siemens Healthcare, especially Agus Priatna, Tiejun Zhao, Vibhas Deshpande, and Mark Brown. We thank Tamara Locke from The University of Texas MD Anderson Cancer Center for editing our manuscript. Funding Information: This research was conducted primarily with the support of National Institutes of Health National Cancer Institute grant R01 CA159471. Our research also received support from the Nevada Cancer Institute, the University of Pittsburgh, Washington University in St. Louis, The University of Texas MD Anderson Cancer Center, and UCLA. We are indebted to Cornell University{\textquoteright}s Yi Wang, Than Nguyen, and Pascal Spincemaille for providing technical expertise during the for-mative period of this project. We also appreciate the technical assistance of Siemens Healthcare, especially Agus Priatna, Tiejun Zhao, Vibhas Deshpande, and Mark Brown. We thank Tamara Locke from The University of Texas MD Anderson Cancer Center for editing our manuscript. Publisher Copyright: {\textcopyright} 2017 American Association of Physicists in Medicine.",
year = "2017",
month = mar,
doi = "10.1002/MP.12099",
language = "English",
volume = "44",
pages = "962--973",
journal = "Medical Physics",
issn = "0094-2405",
number = "3",
}