What is AI Deep Learning Based MRI Reconstruction?

By | November 18, 2022

What is AI Deep Learning Based MRI Reconstruction?

Several groups have begun to explore the use of deep learning-based MRI reconstruction techniques. These methods are designed to decrease the amount of artifacts and denoising that is present in MRI images. They also reduce computation time and the perceived noise in images. These techniques have the potential to revolutionize MRI image reconstruction. They are also effective at accelerating the imaging process for patients with altered mental status.

Deep learning-based MRI reconstruction methods are based on convolutional neural networks (CNNs). CNNs are a type of neural network that has layers of learnable convolutional kernels. CNNs are designed to handle arrays of data. They can also learn a spatial-temporal correlation between data. In addition, CNNs are high-dimensional representations. However, it has been shown that CNNs can overfit unseen data and overfit training data. Deep learning-based MRI reconstruction methods have been used for motion correction of MR images. They have been shown to perform better than traditional iterative reconstruction methods. However, MRI has several artifacts that can prevent it from being completely reliable. Some of these artifacts include white noise and instabilities. Instabilities are nearly invisible to the human eye, and they continuously transform the reconstructed image. They are also prone to systematic deviations in SNR. These can lead to a decrease in image contrast, making it difficult to determine the validity of the reconstructed image.

CNNs typically use an encoder-decoder style network architecture. The decoder portion of CNNs learns the representation of the input data and then reconstructs the output image. The encoder part of CNNs also learns a compressed representation of the input data. Using these techniques, deep learning-based MRI reconstruction methods can complete the reconstruction task in seconds. In contrast, traditional iterative reconstruction methods require a large number of sequential images. The number of sequences also increases the total scan time.

Recent reconstruction challenges have revealed several weaknesses in current deep learning-based MRI reconstruction methods. These include loss of fine image detail, undersampling of data, and the use of incorrect training data. The MRI-VN net is one method that adds a perturbation to the image. This adds false information to the image. The MRI-VN net produces misleading artifacts. The Deep MRI test is an experiment that simulates these instabilities. It uses a test image and a perturbation. This test is designed to demonstrate gradual changes in the perturbation. The results show that the worst perturbation produces misleading artifacts, and the best results are obtained when the perturbation is removed.

What is AI Deep Learning Based MRI Reconstruction









Other types of deep learning-based MRI reconstruction methods include the use of a recurrent CNN, the use of a model-based deep learning architecture, and the use of a variational network. Deep density priors, which are also based on a recurrent CNN, use prior information to optimize the reconstruction. These methods also have a wider range of training data and less dependence on hardware. However, they are more complex to develop and may require large amounts of diverse training data.

In addition, a new type of deep learning-based MRI reconstruction method is being developed. This method is called RAKI (recursive artifact detecting inverse) and is scan-specific. It can perform better than traditional iterative reconstruction methods at high accelerations. It also produces a visually high-quality reconstruction. It has been shown to outperform GRAPPA reconstruction for brain imaging. It also produces low root mean square error. It has been shown to produce reconstructions of white matter lesions in multi-coil complex images from healthy volunteers. It also has been shown to produce reconstructions of arrhythmia in cine SSFP cardiac magnetic resonance images.

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