BibTex format
@article{Schlemper:2018:10.1007/978-3-030-00928-1_34,
author = {Schlemper, J and Yang, G and Ferreira, P and Scott, A and McGill, LA and Khalique, Z and Gorodezky, M and Roehl, M and Keegan, J and Pennell, D and Firmin, D and Rueckert, D},
doi = {10.1007/978-3-030-00928-1_34},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {295--303},
title = {Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI},
url = {http://dx.doi.org/10.1007/978-3-030-00928-1_34},
volume = {11070 LNCS},
year = {2018}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - © Springer Nature Switzerland AG 2018. Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective therapeutics. Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive technique that can resolve the microscopic structure, organisation, and integrity of the myocardium without the need for exogenous contrast agents. However, this technique suffers from relatively low signal-to-noise ratio (SNR) and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR techniques rely on acquiring and averaging multiple signal acquisitions to improve the SNR. Moreover, in order to mitigate the influence of respiratory movement, patients are required to perform many breath holds which results in prolonged acquisition durations (e.g., ~ 30 min using the existing technology). In this study, we propose a novel cascaded Convolutional Neural Networks (CNN) based compressive sensing (CS) technique and explore its applicability to improve DT-CMR acquisitions. Our simulation based studies have achieved high reconstruction fidelity and good agreement between DT-CMR parameters obtained with the proposed reconstruction and fully sampled ground truth. When compared to other state-of-the-art methods, our proposed deep cascaded CNN method and its stochastic variation demonstrated significant improvements. To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction. In addition, with relatively straightforward modifications to the acquisition scheme, our method can easily be translated into a method for online, at-the-scanner reconstruction enabling the deployment of accelerated DT-CMR in various clinical applications.
AU - Schlemper,J
AU - Yang,G
AU - Ferreira,P
AU - Scott,A
AU - McGill,LA
AU - Khalique,Z
AU - Gorodezky,M
AU - Roehl,M
AU - Keegan,J
AU - Pennell,D
AU - Firmin,D
AU - Rueckert,D
DO - 10.1007/978-3-030-00928-1_34
EP - 303
PY - 2018///
SN - 0302-9743
SP - 295
TI - Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
UR - http://dx.doi.org/10.1007/978-3-030-00928-1_34
VL - 11070 LNCS
ER -
