ULTRA-HIGH RESOLUTION CT RECONSTRUCTION USING GRADIENT GUIDANCE
A computer-implemented method is provided for ultra-high resolution computed tomography. The method comprises: acquiring, using computed tomography (CT), a medical image of a subject, the medical image has a lower resolution; and processing the medical image, with aid of a deep learning network model, to reconstruct an ultra-high resolution medical image, where the deep learning network model is trained using a generative adversarial network (GAN)-based framework with a gradient guidance.
1 . A computer-implemented method for ultra-high resolution computed tomography comprising:
(a) acquiring, using computed tomography (CT), a medical image of a subject, wherein the medical image has a lower resolution; and
(b) processing the medical image, with aid of a deep learning network model, to reconstruct an ultra-high resolution medical image, wherein the deep learning network model is trained using a generative adversarial network (GAN)-based framework with a gradient guidance.
2 . The computer-implemented method of claim 1 , wherein the GAN-based framework comprises a first branch for improving a resolution of a medical image, and a second branch for generating a predicted gradient map.
3 . The computer-implemented method of claim 2 , wherein the predicted gradient map is used to guide the training of the first branch.
4 . The computer-implemented method of claim 3 , wherein the predicted gradient map is concatenated with a feature map of the first branch and is supplied to a residual block.
5 . The computer-implemented method of claim 2 , wherein the second branch uses a pixel-wise loss in a training process.
6 . The computer-implemented method of claim 2 , wherein the first branch uses a combination of pixel-wise loss and a GAN loss in a training process.
7 . The computer-implemented method of claim 2 , wherein the second branch incorporates one or more intermediate feature maps generated by the first branch.
8 . The computer-implemented method of claim 7 , wherein the first branch comprises a set of residual blocks and the one or more intermediate feature maps are generated by one or more residual blocks selected from the set of residual blocks.
9 . The computer-implemented method of claim 2 , wherein the first branch comprises a first set of residual blocks and wherein the second branch comprises a second set of residual blocks.
10 . The computer-implemented method of claim 2 , where an input to the second branch includes a gradient map of the medical image acquired in (a).
11 . The computer-implemented method of claim 1 , wherein the deep learning network model is trained using a loss function comprising a combination of at least pixel-wise loss, adversarial loss, and perceptual loss.
12 . A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
(a) acquiring, using computed tomography (CT), a medical image of a subject, wherein the medical image has a lower resolution; and
(b) processing the medical image, with aid of a deep learning network model, to reconstruct an ultra-high resolution medical image, wherein the deep learning network model is trained using a generative adversarial network (GAN)-based framework with a gradient guidance.
13 . The non-transitory computer-readable storage medium of claim 12 , wherein the GAN-based framework comprises a first branch for improving a resolution of a medical image, and a second branch for generating a predicted gradient map.
14 . The non-transitory computer-readable storage medium of claim 13 , wherein the predicted gradient map is used to guide the training of the first branch.
15 . The non-transitory computer-readable storage medium of claim 14 , wherein the predicted gradient map is concatenated with a feature map of the first branch and is supplied to a residual block.
16 . The non-transitory computer-readable storage medium of claim 13 , wherein the second branch uses a pixel-wise loss in a training process.
17 . The non-transitory computer-readable storage medium of claim 13 , wherein the first branch uses a combination of pixel-wise loss and a GAN loss in a training process.
18 . The non-transitory computer-readable storage medium of claim 13 , wherein the second branch incorporates one or more intermediate feature maps generated by the first branch.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the first branch comprises a set of residual blocks and the one or more intermediate feature maps are generated by one or more residual blocks selected from the set of residual blocks.
20 . The non-transitory computer-readable storage medium of claim 13 , wherein the first branch comprises a first set of residual blocks and wherein the second branch comprises a second set of residual blocks.