Authors:
Gurbandurdy Dovletov
1
;
Utku Karadeniz
1
;
Stefan Lörcks
1
;
Josef Pauli
1
;
Marcel Gratz
2
;
3
and
Harald Quick
2
;
3
Affiliations:
1
Intelligent Systems Group, Faculty of Computer Science, University of Duisburg-Essen, Duisburg, Germany
;
2
High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
;
3
Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
Keyword(s):
Deep Learning, Image-to-Image Translation, Pseudo-CT Synthesis, Attention Mechanisms, Attention U-Net, Generative Adversarial Network.
Abstract:
Deep learning techniques offer the potential to learn the mapping function from MRI to CT domains, allowing the generation of synthetic CT images from MRI source data. However, these image-to-image translation methods often introduce unwanted artifacts and struggle to accurately reproduce bone structures due to the absence of bone-related information in the source data. This paper extends the recently introduced Attention U-Net with Extra Supervision (Att U-Net ES), which has shown promising improvements for the bone regions. Our proposed approach, a conditional Wasserstein GAN with Attention U-Net as the generator, leverages the network’s self-attention property while simultaneously including domain-specific knowledge (or bone awareness) in its learning process. The adversarial learning aspect of the proposed approach ensures that the attention gates capture both the overall shape and the fine-grained details of bone structures. We evaluate the proposed approach using cranial MR and
CT images from the publicly available RIRE data set. Since the images are not aligned with each other, we also provide detailed information about the registration procedure. The obtained results are compared to Att U-Net ES, baseline U-Net and Attention U-Net, and their GAN extensions.
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