Attention-Guided Generative Adversarial Network to Address Atypical
Anatomy in Modality Transfer
- URL: http://arxiv.org/abs/2006.15264v3
- Date: Wed, 14 Apr 2021 22:26:39 GMT
- Title: Attention-Guided Generative Adversarial Network to Address Atypical
Anatomy in Modality Transfer
- Authors: Hajar Emami, Ming Dong, Carri K. Glide-Hurst
- Abstract summary: We propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI images.
Experimental results on fifteen brain cancer patients show that attention-GAN outperformed existing synCT models and achieved an average MAE of 85.22$pm$12.08, 232.41$pm$60.86, 246.38$pm$42.67.
- Score: 3.167912607974845
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, interest in MR-only treatment planning using synthetic CTs (synCTs)
has grown rapidly in radiation therapy. However, developing class solutions for
medical images that contain atypical anatomy remains a major limitation. In
this paper, we propose a novel spatial attention-guided generative adversarial
network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI
images as the input to address atypical anatomy. Experimental results on
fifteen brain cancer patients show that attention-GAN outperformed existing
synCT models and achieved an average MAE of 85.22$\pm$12.08, 232.41$\pm$60.86,
246.38$\pm$42.67 Hounsfield units between synCT and CT-SIM across the entire
head, bone and air regions, respectively. Qualitative analysis shows that
attention-GAN has the ability to use spatially focused areas to better handle
outliers, areas with complex anatomy or post-surgical regions, and thus offer
strong potential for supporting near real-time MR-only treatment planning.
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