鉴别器
计算机科学
人工智能
发电机(电路理论)
直方图
医学影像学
深度学习
模式识别(心理学)
计算机视觉
图像(数学)
电信
功率(物理)
物理
量子力学
探测器
作者
Yanjun Peng,Jindong Sun,Yande Ren,Dapeng Li,Yanfei Guo
标识
DOI:10.1016/j.knosys.2023.110802
摘要
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are commonly used tools for medical diagnostic assessment. Considering the ionizing radiation of CT imaging, estimating CT images from radiation-free MRI would be beneficial for medical diagnosis. Although state-of-art generative adversarial networks or end-to-end architecture models can generate realistic natural images, it is challenging to generate medical CT images with high signal-to-noise ratios and are paired with MRI. We propose a histogram-driven generative adversarial network (HisGAN) to address this issue, estimate CT images paired with MR, and develop a histogram-based dynamic scaling factor to facilitate learning different image styles. By employing an adversarial learning strategy to train the end-to-end generator, the generator better simulate the nonlinear mapping from source to target. For the generator, the proposed method applies multiple learnable parameters to adjust the overall weights of the dilated convolution layers to ensure sufficient expansive receptive fields for improved performance. Additionally, the method utilizes deep residual networks to train randomly smoothed generated images and employs adversarial loss to enhance the generation of the discriminator, achieving a balance between the generator and the discriminator. Our approach can synthesize image details at the pixel level in the target domain and has been evaluated using two datasets for MR to CT, T1 to T2, and Flair to T1ce modality synthesis tasks. The proposed method outperforms existing generative adversarial models applied to medical image synthesis.
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