Deep learning for accelerated MR imaging
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Magnetic resonance imaging is a non-invasive imaging modality that enables multi-contrast acquisition of an underlying anatomy, thereby supplementing mul-titude of information for diagnosis. However, prolonged scan duration may pro-hibit its practical use. Two mainstream frameworks for accelerating MR image acquisitions are reconstruction and synthesis. In reconstruction, acquisitions are accelerated by undersampling in k-space, followed by reconstruction algorithms. Lately deep neural networks have oﬀered signiﬁcant improvements over tradi-tional methods in MR image reconstruction. However, deep neural networks rely heavily on availability of large datasets which might not be readily available for some applications. Furthermore, a caveat of the reconstruction framework in general is that the performance naturally starts degrading towards higher accel-eration factors where fewer data samples are acquired. In the alternative syn-thesis framework, acquisitions are accelerated by acquiring a subset of desired contrasts, and recovering the missing ones from the acquired ones. Current syn-thesis methods are primarily based on deep neural networks, which are trained to minimize mean square or absolute loss functions. This can bring about loss of intermediate-to-high spatial frequency content in the recovered images. Fur-thermore, the synthesis performance in general relies on similarity in relaxation parameters between source and target contrasts, and large dissimilarities can lead to artifactual synthesis or loss of features. Here, we tackle issues associated with reconstruction and synthesis approaches. In reconstruction, the data scarcity is-sue is addressed by pre-training a network on large readily available datasets, and ﬁne-tuning on just a few samples from target datasets. In synthesis, the loss of intermediate-to-high spatial frequency is catered for by adding adversarial and high-level perceptual losses on top of traditional mean absolute error. Fi-nally, a joint reconstruction and synthesis approach is proposed to mitigate the issues associated with both reconstruction and synthesis approaches in general. Demonstrations on MRI brain datasets of healthy subjects and patients indicate superior performance of the proposed techniques over the current state-of-the art ones.
KeywordsMagnetic resonance imaging
Deep neural networks
Generative adversarial networks