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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