Noise suppression for wave-CAIPI
AssigneesSiemens Healthcare GmbH, The General Hospital Corporation
InventorsDaniel Polak, Esther Raithel, Kawin Setsompop
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Abstract
Techniques are disclosed to leverage the use of neural networks or similar machine learning algorithms to de-noise highly accelerated Wave-CAIPIRINHA scans. The described techniques facilitate the generation of 3D sequences using a greatly reduced scan time, with the resulting images having a high spatial resolution and an improved SNR compared to conventional approaches.
Claims23
A method, comprising:
- accessing, via one or more processors, an under-sampled dataset included in a Wave dataset, the under-sampled dataset being associated with a magnetic resonance imaging (MRI) scan, the Wave dataset corresponding to two added oscillating wave gradients thereby yielding a Wave dataset having a corkscrew trajectory in k-space;
- generating, via the one or more processors, a reconstructed dataset from the under-sampled dataset;
- training, via the one or more processors, a convolutional neural network by calculating filter weights that result in the convolutional neural network providing an output image that substantially matches a difference image;
- propagating, via the one or more processors, the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images;
- and
- displaying, via the one or more processors, the one or more de-noised output images.
A convolutional neural network, comprising:
- a storage medium configured to store an under-sampled dataset included in a Wave dataset, the under-sampled dataset being associated with a magnetic resonance imaging (MRI) scan, the Wave dataset corresponding to two added oscillating wave gradients thereby yielding a Wave dataset having a corkscrew trajectory in k-space;
- and
- one or more processors configured to:
- generate a reconstructed dataset from the under-sampled dataset;
- train a convolutional neural network by calculating filter weights that result in the convolutional neural network providing an output image that substantially matches a difference image;
- propagate the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images;
- and
- display the one or more de-noised output images.
The convolutional neural network of 10, wherein the difference image is a result of a subtraction of the ground truth and an accelerated MRI scan that produces the under-sampled dataset.
A non-transitory computer-readable medium associated with a convolutional neural network, the non-transitory medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
- access an under-sampled dataset included in a Wave dataset, the under-sampled dataset being associated with a magnetic resonance imaging (MRI) scan, the Wave dataset corresponding to two added oscillating wave gradients thereby yielding a Wave dataset having a corkscrew trajectory in k-space;
- generate a reconstructed dataset from the under-sampled dataset;
- train the convolutional neural network by calculating filter weights that result in the convolutional neural network providing an output image that substantially matches a difference image;
- propagate the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images;
- and
- display the one or more de-noised output images.
Dependencies are inferred from the claim text.