Clipped DeepControl: Deep neural network two-dimensional pulse design with an amplitude constraint layer

Artificial Intelligence in Medicine - Tập 135 - Trang 102460 - 2023
Mads Sloth Vinding1, Torben Ellegaard Lund1
1Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Faculty of Health, Aarhus University, Denmark

Tài liệu tham khảo

Brown, 2014 Caan, 2019, MP2RAGEME: T 1, T 2 *, and QSM mapping in one sequence at 7 tesla, Human Brain Mapp, 40, 1786, 10.1002/hbm.24490 Dietrich, 2021, 3D Free-breathing multichannel absolute Mapping in the human body at 7T, Magn Reson Med, 85, 2552, 10.1002/mrm.28602 Finsterbusch, 2010, Fast-spin-echo imaging of inner fields-of-view with 2D-selective RF excitations, J Magn Reson Imaging, 31, 1530, 10.1002/jmri.22196 Herrler, 2021, Fast online-customized (FOCUS) parallel transmission pulses: A combination of universal pulses and individual optimization, Magn Reson Med, 85, 3140, 10.1002/mrm.28643 Vinding, 2017, Local, SAR and global SAR and power-constrained large-flip-angle pulses with optimal control and virtual observation points, Magn Reson Med, 77, 374, 10.1002/mrm.26086 Bernal, 2019, Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review, Artif Intell Med, 95, 64, 10.1016/j.artmed.2018.08.008 Boutillon, 2022, Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network, Artif Intell Med, 132, 10.1016/j.artmed.2022.102364 Li, 2022, Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions, J Biomed Inform, 127, 10.1016/j.jbi.2022.104030 Parimbelli, 2021, A review of AI and data science support for cancer management, Artif Intell Med, 117, 10.1016/j.artmed.2021.102111 Helmy Abdou, 2022, CapillaryNet: An automated system to quantify skin capillary density and red blood cell velocity from handheld vital microscopy, Artif Intell Med, 127 Popescu, 2022, Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart, Nat Cardiovasc Res, 1, 334, 10.1038/s44161-022-00041-9 Nielsen, 2018, Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning, Stroke, 49, 1394, 10.1161/STROKEAHA.117.019740 Schunkë, 2022, A rapid review of machine learning approaches for telemedicine in the scope of covid-19, Artif Intell Med, 129, 10.1016/j.artmed.2022.102312 Vinding, 2019, Ultrafast (milliseconds), multidimensional RF pulse design with deep learning, Magn Reson Med, 82, 586, 10.1002/mrm.27740 Vinding, 2021, Optimal control gradient precision trade-offs: Application to fast generation of DeepControl libraries for MRI, J Magn Reson, 333, 10.1016/j.jmr.2021.107094 Maximov, 2015, Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods, J Magn Reson, 254, 110, 10.1016/j.jmr.2015.03.003 Massire, 2013, Design of non-selective refocusing pulses with phase-free rotation axis by gradient ascent pulse engineering algorithm in parallel transmission at 7T, J Magn Reson, 230, 76, 10.1016/j.jmr.2013.01.005 Aigner, 2016, Efficient high-resolution RF pulse design applied to simultaneous multi-slice excitation, J Magn Reson, 263, 33, 10.1016/j.jmr.2015.11.013 Reeth, 2019, A simplified framework to optimize MRI contrast preparation, Magn Reson Med, 81, 424, 10.1002/mrm.27417 Vinding, 2021, DeepControl: 2DRF pulses facilitating B1+ inhomogeneity and B0 off-resonance compensation in vivo at 7 T, Magn Reson Med, 85, 3308, 10.1002/mrm.28667 Vinding, 2021, DeepControl: AI-powered slice flip-angle homogenization by 2D RF pulses, 0785 Deng J, Dong W, Socher R, Li L-J, Li Kai, Fei-Fei Li. ImageNet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. 2009, p. 248–55. http://dx.doi.org/10.1109/CVPR.2009.5206848. Merolla, 2016 Kingma, 2017 Shin, 2021, Deep reinforcement learning-designed radiofrequency waveform in MRI, Nat Mach Intell, 3, 985, 10.1038/s42256-021-00411-1 Goodwin, 2016, Modified Newton-Raphson GRAPE methods for optimal control of spin systems, J Chem Phys, 144, 10.1063/1.4949534 Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0 Grissom, 2016 Lustig, 2008, A fast method for designing time-optimal gradient waveforms for arbitrary k-space trajectories, IEEE Trans Med Imaging, 27, 866, 10.1109/TMI.2008.922699 Combi, 2022, A manifesto on explainability for artificial intelligence in medicine, Artif Intell Med, 133, 10.1016/j.artmed.2022.102423 Amey, 2021, Neural network interpretation using descrambler groups, Proc Natl Acad Sci USA, 118, 10.1073/pnas.2016917118 Zhang, 2021, Multi-task convolutional neural network-based design of radio frequency pulse and the accompanying gradients for magnetic resonance imaging, NMR Biomed, 34, 10.1002/nbm.4443