Medical Imaging Publications
Title
Tripartite-GAN: Synthesizing liver contrast-enhanced MRI to improve tumor detection.
Document Type
Article
Publication Date
7-1-2020
Journal
Medical image analysis
Volume
63
First Page
101667
Last Page
101667
URL with Digital Object Identifier
https://doi.org/10.1016/j.media.2020.101667
Abstract
Contrast-enhanced magnetic resonance imaging (CEMRI) is crucial for the diagnosis of patients with liver tumors, especially for the detection of benign tumors and malignant tumors. However, it suffers from high-risk, time-consuming, and expensive in current clinical diagnosis due to the use of the gadolinium-based contrast agent (CA) injection. If the CEMRI can be synthesized without CA injection, there is no doubt that it will greatly optimize the diagnosis. In this study, we propose a Tripartite Generative Adversarial Network (Tripartite-GAN) as a non-invasive, time-saving, and inexpensive clinical tool by synthesizing CEMRI to detect tumors without CA injection. Specifically, our innovative Tripartite-GAN combines three associated-networks (an attention-aware generator, a convolutional neural network-based discriminator, and a region-based convolutional neural network-based detector) for the first time, which achieves CEMRI synthesis and tumor detection promoting each other in an end-to-end framework. The generator facilitates detector for accurate tumor detection via synthesizing tumor-specific CEMRI. The detector promotes the generator for accurate CEMRI synthesis via the back-propagation. In order to synthesize CEMRI of equivalent clinical value to real CEMRI, the attention-aware generator expands the receptive field via hybrid convolution, and enhances feature representation and context learning of multi-class liver MRI via dual attention mechanism, and improves the performance of convergence of loss via residual learning. Moreover, the attention maps obtained from the generator newly added into the detector improve the performance of tumor detection. The discriminator promotes the generator to synthesize high-quality CEMRI via the adversarial learning strategy. This framework is tested on a large corpus of axial T1 FS Pre-Contrast MRI and axial T1 FS Delay MRI of 265 subjects. Experimental results and quantitative evaluation demonstrate that the Tripartite-GAN achieves high-quality CEMRI synthesis that peak signal-to-noise rate of 28.8 and accurate tumor detection that accuracy of 89.4%, which reveals that Tripartite-GAN can aid in the clinical diagnosis of liver tumors.
Notes
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