VNC-Net architecture.

<p>The model takes contrast enhanced dual-energy CT (CE DECT) images as input and generates virtual non-contrast (VNC) images as output. The numbers denoted below the encoder/decoder blocks represent the number of channels. The bottom of the figure depicts the structure of the down-sampling bl...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Jungye Kim (20485427) (author)
مؤلفون آخرون: Jimin Lee (2552782) (author), Bitbyeol Kim (17020584) (author), Sangwook Kim (2235727) (author), Hyeongmin Jin (20485430) (author), Seongmoon Jung (9310778) (author)
منشور في: 2024
الموضوعات:
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الوصف
الملخص:<p>The model takes contrast enhanced dual-energy CT (CE DECT) images as input and generates virtual non-contrast (VNC) images as output. The numbers denoted below the encoder/decoder blocks represent the number of channels. The bottom of the figure depicts the structure of the down-sampling block of the encoder, which comprises a UnetConv2D block and a max pooling layer. The UnetConv2D component includes two ConvBNReLU blocks, each consisting of a Conv layer that executes 2D convolutional operations, followed by batch normalization and a ReLU activation function.</p>