Late gadolinium enhancement (LGE) imaging has become an indispensable tool in diag-
nosis and assessment of myocardial infarction (MI). Size, location, and extent of the in-
farcted tissue are important indicators to assess treatment ecacy and to predict functional
recovery.[1] In LGE imaging, T1-weighted inversion recovery pulse sequences are applied
several minutes after injection of a gadolinium-based contrast agent (GBCA). However,
contraindications (e. g. renal insuciency) and severe adverse e ects (e. g. nephrogenic sys-
temic brosis) of GBCAs are known.[2] Therefore, minimization of administered contrast
agent doses is subject of current research. Existing neural network-based approaches either
rely on cardiac wall motion abnormalities[3] or have been developed for brain MRI[4].
The aim of this thesis is to develop a post-processing approach based on convolutional neural
networks (CNN) to accurately segment and quantify myocardial scar in 2-D LGE images
acquired with reduced doses of GBCA. For this purpose, synthetic data generated with an
in-house MRI simulation suite is used for a start. The 4-D XCAT phantom[5] is used for the
simulation, as it o ers multiple possibilities for variations in patient anatomy as well as in
geometry and location of myocardial scar. Furthermore, the simulated images will include
variability in certain acquisition parameters to best re
ect in-vivo data. In addition to LGE
images, T1-maps are simulated with di erent levels of contrast agent dose. In the scope of
this thesis, multiple approaches using di erent combinations of input data (i. e. LGE images
and/or T1-maps at zero-dose and/or low-dose) are explored. The performance of the net-
work will be evaluated on simulated and in-vivo data. Depending on the availability, in-vivo
data will also be incorporated into the training process.
The thesis covers the following aspects:
Generation of simulated training data, best re
ecting in-vivo data
Development of the CNN-based system including implementation using PyTorch
Optional: depending on data availability and on previous results, incorporation of
in-vivo data into the training process
Quantitative evaluation of the implemented network on simulated and in-vivo data
using dice score and clinically relevant MI quanti cation metrics, e. g. the full width
at half maximum method (FWHM)
References
[1] V. Hombach, N. Merkle, P. Bernhard, V. Rasche, and W. Rottbauer, \Prognostic sig-
ni cance of cardiac magnetic resonance imaging: Update 2010,” Cardiology Journal,
2010.
[2] L. Bakhos and M. A. Syed, Contrast Media, pp. 271{281. Cham: Springer International
Publishing, 2015.
1
[3] N. Zhang, G. Yang, Z. Gao, C. Xu, Y. Zhang, R. Shi, J. Keegan, L. Xu, H. Zhang,
Z. Fan, and D. Firmin, \Deep learning for diagnosis of chronic myocardial infarction on
nonenhanced cardiac cine MRI,” Radiology, 2019.
[4] E. Gong, J. M. Pauly, M. Wintermark, and G. Zaharchuk, \Deep learning enables re-
duced gadolinium dose for contrast-enhanced brain MRI,” Journal of Magnetic Reso-
nance Imaging, 2018.
[5] W. P. Segars, G. Sturgeon, S. Mendonca, J. Grimes, and B. M. Tsui, \4D XCAT phantom
for multimodality imaging research,” Medical Physics, 2010.