Methods and systems for removing spike noise in magnetic resonance imaging
AssigneeGE Precision Healthcare LLC
InventorsDan Wu, Shiyu Li, Qingyu Dai, Kun Wang, Jiabin Yao, Yongchuan Lai
Life of this patent
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Abstract
Spike noise in a k-space dataset acquired in magnetic resonance imaging may be removed by generating a mask including a set of data points which constitute spike noise in the k-space dataset based on the acquired k-space dataset via a trained deep learning network, the mask corresponding to a location of the spike noise in the acquired k-space dataset. An image reconstructed based on the acquired k-space dataset and the mask may be displayed.
Claims18
A method for magnetic resonance imaging (MRI), comprising:
- acquiring a k-space dataset;
- identifying, by a trained deep learning network, a set of data points which constitute spike noise in the k-space dataset;
- updating the k-space dataset by removing the spike noise from the k-space dataset;
- and
- reconstructing a magnetic resonance (MR) image from the updated k-space dataset.
A magnetic resonance imaging (MRI) apparatus, comprising:
- a gradient coil assembly;
- a radio frequency (RF) coil assembly;
- and
- a controller coupled to the gradient coil assembly and the RF coil assembly and configured to:
- acquire a k-space dataset via the gradient coil assembly and the RF coil assembly;
- use a trained deep learning network to identify a set of data points which constitute spike noise in the k-space dataset;
- update the k-space dataset by removing the spike noise from the acquired k-space dataset;
- and
- reconstruct a magnetic resonance (MR) image from the updated k-space dataset,
- wherein the reconstructed MR image is generated upon receipt of a request to remove spike noise.
A non-transitory computer-readable medium comprising instructions that, when executed, cause a processor to:
- acquire a k-space dataset;
- immediately after acquiring the k-space dataset and prior to displaying an image reconstructed based on the k-space dataset, determine, via a trained deep learning network, locations of data points that constitute noise in the k-space dataset;
- update the k-space dataset by removing noise from the acquired k-space dataset;
- display a magnetic resonance (MR) image reconstructed from the updated k-space dataset;
- and
- indicate hardware failure responsive to an area of the noise greater than a threshold area.
Dependencies are inferred from the claim text.