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Erschienen in:
Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

verfasst von : Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert

Erschienen in: Information Processing in Medical Imaging

Verlag: Springer International Publishing

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Abstract

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications.

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Vorheriges Kapitel Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
Nächstes Kapitel Population Based Image Imputation
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Metadaten
Titel
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
verfasst von
Jo Schlemper
Jose Caballero
Joseph V. Hajnal
Anthony Price
Daniel Rueckert
Copyright-Jahr
2017
Buch
Information Processing in Medical Imaging

Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-59050-9_51

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