Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer

Hajar Emami; Ming Dong; Carri K. Glide-Hurst

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arXiv · 2006.15264

Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer

Hajar Emami·Ming Dong·Carri K. Glide-Hurst

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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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Hajar Emami, Ming Dong, Carri K. Glide-Hurst. 2020-06-27. Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer. https://arxiv.org/abs/2006.15264

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