A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony N. Price, Daniel Rueckert

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

929 Zitate (Scopus)

Abstract

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.

OriginalspracheEnglisch
Aufsatznummer8067520
Seiten (von - bis)491-503
Seitenumfang13
FachzeitschriftIEEE Transactions on Medical Imaging
Jahrgang37
Ausgabenummer2
DOIs
PublikationsstatusVeröffentlicht - Feb. 2018
Extern publiziertJa

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