Ultrafast (milliseconds), multidimensional RF pulse design with deep learning

@article{Vinding2018UltrafastM,
  title={Ultrafast (milliseconds), multidimensional RF pulse design with deep learning},
  author={Mads Sloth Vinding and Birk Skyum and Ryan Sangill and Torben Ellegaard Lund},
  journal={Magnetic Resonance in Medicine},
  year={2018},
  volume={82},
  pages={586 - 599}
}
Some advanced RF pulses, like multidimensional RF pulses, are often long and require substantial computation time because of a number of constraints and requirements, sometimes hampering clinical use. However, the pulses offer opportunities of reduced‐FOV imaging, regional flip‐angle homogenization, and localized spectroscopy, e.g., of hyperpolarized metabolites. Proposed herein is a novel deep learning approach to ultrafast design of multidimensional RF pulses with intention of real‐time pulse… 
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18 Citations
Background Citations
4
Methods Citations
6
Results Citations
1

18 Citations

DeepControl: 2DRF pulses facilitating B1+ inhomogeneity and B0 off‐resonance compensation in vivo at 7 T

Rapid 2DRF pulse design with subject‐specific B1+ inhomogeneity and B0 off‐resonance compensation at 7 T predicted from convolutional neural networks is presented.

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

Multi‐task convolutional neural network‐based design of radio frequency pulse and the accompanying gradients for magnetic resonance imaging

A convolutional neural network with multi‐task learning, trained with SLR transformation pairs, that is capable of simultaneously generating RF and two channels of gradient waveforms, given the desired spatially two‐dimensional excitation profiles is presented.

Deep reinforcement learning-designed radiofrequency waveform in MRI

An artificial intelligence (AI)powered RF pulse design framework, DeepRF, is proposed, which utilizes the self-learning characteristics of deep reinforcement learning to generate a novel RF pulse.

Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR

A novel approach of applying deep reinforcement learning to an RF pulse design that suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a “machine-designed” MRI sequence.

Optimal control gradient precision trade-offs: Application to fast generation of DeepControl libraries for MRI.

Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping : DeepRF SLR

A novel approach of applying deep reinforcement learning to an RF pulse design that suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a “machine-designed” MRI sequence.

DeepControl: 2D RF pulses facilitating B1+ inhomogeneity and B0 off-resonance compensation in vivo at 7T

The reconstructed image data agrees well with the simulations using the acquired $B_1^+$ and $B-0$ maps and the 2D RF pulse predicted by the convolutional neural networks is as good as the conventional RF pulse obtained by optimal control.

Three‐dimensional static and dynamic parallel transmission of the human heart at 7 T

It is demonstrated in vivo that kT‐point pTx pulses are highly suitable for mitigating nominal FA heterogeneities across the entire 3D heart volume at 7 T and demonstrate a practical tradeoff between nominal FA heterogeneity mitigation and RF power.

Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model.

A 29-month-old male whole-body native numerical model, "MARTIN", that includes 28 head and 86 body tissue compartments, segmented directly from the high spatial resolution MRI and CT images is introduced.

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