MRI-based virtual CT generation from unpaired data

MRI-based virtual CT generation from unpaired data

Gabriel, Luka (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics)

Loog, M. (mentor)
Vilanova Bartroli, A. (graduation committee)
van Gemert, J.C. (graduation committee)
Hildebrandt, K.A. (graduation committee)
van Stralen, Marijn (graduation committee)

Delft University of Technology

2018-08-30

Both MRI and CT imaging are commonly used and combined in medical imaging because of their complementary information about soft tissue and bone respectively. However, CT imaging relies on harmful ionizing radiation. Thus medical imaging scientists are working on transferring harmless MRI scans to CT-like images where using deep learning methods are the current state of the art. Most deep learning methods however require large amounts of paired data to train the transfer systems, which is why we try to perform the transfer using an unpaired method, CycleGANs. Using ex-vivo dog hip MRI and CT scan pairs we trained a CycleGAN and a U-Net using both registered and poorly registered data. We also further investigated the effect of discriminator’s receptive field size in a CycleGAN as well as introduced a novel NMI loss component to its generators. Performance of said systems was evaluated by calculating MAE and Dice scores. We show that CycleGAN's performance is sensitive to its discriminator's receptive field size and can even generate unwanted structrures in the CT-like images. Also, we show that adding NMI loss component with the right weight can improve the system’s performance. The results of comparing the pairwise and unpaired method show that a CycleGAN will only outperform the U-Net when transferring poorly registered data. This suggest that, whenever paired registered data is available, it is better to use a pairwise transfer approach. However, the results of the experiment where the NMI loss component was added suggest that transfer from MRI scans to CT-like images can become more accurate and encourage further research. Finally, this means in certain cases CT scans could be avoided, providing a more harmless medical imaging future.

Medical Imaging
MRI
CT
Deep Learning
CycleGAN

http://resolver.tudelft.nl/uuid:9be59b26-996c-4c0b-903d-00caffb5d018

Student theses

master thesis

© 2018 Luka Gabriel