. Author manuscript; available in PMC: 2021 Jan 10.

Published in final edited form as: Med Phys. 2020 Oct 30;47(12):6294–6309. doi: 10.1002/mp.14523

Table 1.

The configuration of the proposed convolutional neural network

Components	Configuration
Input	2-channel images: #1 channel – CT image from lower energy bin #2 channel – CT image from higher energy bin
1^st Convolution (Conv)	3×3 filters w. 60 channels 60 output channels
Inception-B #1	1×1 filters w. 20 channels 3×3 filters w. 20 channels 120 output channels
Inception-B #2	1×1 filters w. 40 channels 3×3 filters w. 40 channels 240 output channels
Inception-R #1	1×1 filters w. 40 channels 3×3 filters w. 40 channels 120 output channels
Inception-R #2	1×1 filters w. 20 channels 3×3 filters w. 20 channels 60 output channels
Last convolution	1×1 filters w. 3 channels 3 output channels
Output	3-channel images: #1 channel – hydroxyapatite image #2 channel – iodine image #3 channel – soft-tissue image

Zero padding was applied to all convolution layers