Synopsis

Keywords: Image acquisition: Multiparametric

Deep Learning-Powered MRI Parameter Mapping uses deep neural networks to estimate T1 relaxation, T2 relaxation, diffusion, and susceptibility from MRI data. The network architecture typically includes perceptrons, 2D and 3D convolutional neural networks trained on large datasets. Generalization is a significant challenge, but data augmentation and network architecture modifications are being explored. Despite this, deep learning-powered MRI parameter mapping has the potential to revolutionize clinical MRI by enabling fast and accurate estimation of tissue parameters for diagnosis and treatment planning.

Deep Learning-Powered MRI Parameter Mapping is a field of research that aims to accurately estimate the tissue parameters of T1 relaxation, T2 relaxation, diffusion, and susceptibility from MRI data. This is achieved by utilizing deep neural networks that can effectively capture complex patterns in the data and make accurate predictions. The network architecture for deep learning-powered MRI parameter mapping typically consists of perceptrons, 2D and 3D convolutional neural networks. These networks are trained using large datasets of labeled MRI data, where the ground truth parameters are either generated via simulation or measured through conventional MRI methods. One of the main challenges in deep learning-powered MRI parameter mapping is the issue of generalization. The trained networks often show excellent performance on the training data but may fail to generalize to new data with different characteristics. Approaches to address this issue either via data or via network architecture will be discussed. Overall, deep learning-powered MRI parameter mapping has the potential to revolutionize clinical MRI by enabling accurate and fast estimation of tissue parameters, which can aid in diagnosis and treatment planning. However, further research is needed to address the issue of generalization and to improve the reliability and robustness of the deep learning algorithms.

Acknowledgements

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References

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