4 code implementations • 5 Dec 2017 • Chen Qin, Jo Schlemper, Jose Caballero, Anthony Price, Joseph V. Hajnal, Daniel Rueckert
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
4 code implementations • 8 Apr 2017 • Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
4 code implementations • 1 Mar 2017 • Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
1 code implementation • 22 Jul 2019 • Chen Qin, Jo Schlemper, Jinming Duan, Gavin Seegoolam, Anthony Price, Joseph Hajnal, Daniel Rueckert
Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
1 code implementation • 6 May 2020 • David Leitão, Rui Pedro A. G. Teixeira, Anthony Price, Alena Uus, Joseph V. Hajnal, Shaihan J. Malik
This work presents and validates an efficiency metric to optimize and compare the performance of qMRI methods.
Medical Physics Image and Video Processing