SYSTEMS AND METHODS OF USING SELF-ATTENTION DEEP LEARNING FOR IMAGE ENHANCEMENT
A computer-implemented method is provided for improving image quality. The method comprises: acquiring, using a medical imaging apparatus, a medical image of a subject, wherein the medical image is acquired with shortened scanning time or reduced amount of tracer dose; applying a deep learning network model to the medical image to generate one or more feature attention maps a medical image of the subject with improved image quality for analysis by a physician.
1 . A computer-implemented method for improving image quality comprising:
(a) acquiring, using a medical imaging apparatus, a medical image of a subject, wherein the medical image is acquired with shortened scanning time or reduced amount of tracer dose; and
(b) applying a deep learning network model to the medical image to generate one or more attention feature maps and an enhanced medical image.
2 . The computer-implemented method of claim 1 , wherein the deep learning network model comprises a first subnetwork for generating the one or more attention feature maps and a second subnetwork for generating the enhanced medical image.
3 . The computer-implemented method of claim 2 , wherein an input data to the second subnetwork includes the one or more attention feature maps.
4 . The computer-implemented method of claim 2 , wherein the first subnetwork and the second subnetwork are deep learning networks.
5 . The computer-implemented method of claim 2 , wherein the first subnetwork and the second subnetwork are trained in an end-to-end training process.
6 . The computer-implemented method of claim 5 , wherein the second subnetwork is trained to adapt to the one or more attention feature maps.
7 . The computer-implemented method of claim 1 , wherein the deep learning network model includes a combination of U-net structure and a residual network.
8 . The computer-implemented method of claim 1 , wherein the one or more attention feature maps include a noise map or lesion map.
9 . The computer-implemented method of claim 1 , wherein the medical imaging apparatus is a transforming magnetic resonance (MR) device or a Positron Emission Tomography (PET) device.
10 . The computer-implemented method of claim 1 , wherein the enhanced medical image has a higher resolution or improved signal-noise ratio.
11 . 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 a medical imaging apparatus, a medical image of a subject, wherein the medical image is acquired with shortened scanning time or reduced amount of tracer dose; and
(b) applying a deep learning network model to the medical image to generate one or more attention feature maps and an enhanced medical image.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein the deep learning network model comprises a first subnetwork for generating the one or more attention feature maps and a second subnetwork for generating the enhanced medical image.
13 . The non-transitory computer-readable storage medium of claim 12 , wherein an input data to the second subnetwork includes the one or more attention feature maps.
14 . The non-transitory computer-readable storage medium of claim 12 , wherein the first subnetwork and the second subnetwork are deep learning networks.
15 . The non-transitory computer-readable storage medium of claim 12 , wherein the first subnetwork and the second subnetwork are trained in an end-to-end training process.
16 . The non-transitory computer-readable storage medium of claim 15 , wherein the second subnetwork is trained to adapt to the one or more attention feature maps.
17 . The non-transitory computer-readable storage medium of claim 11 , wherein the deep learning network model includes a combination of U-net structure and a residual network.
18 . The non-transitory computer-readable storage medium of claim 11 , wherein the one or more attention feature maps include a noise map or lesion map.
19 . The non-transitory computer-readable storage medium of claim 11 , wherein the medical imaging apparatus is a transforming magnetic resonance (MR) device or a Positron Emission Tomography (PET) device.
20 . The non-transitory computer-readable storage medium of claim 11 , wherein the enhanced medical image has a higher resolution or improved signal-noise ratio.