Apparatus, method, and non-transitory computer-readable storage medium for enhancing computed tomography image resolution
The present disclosure relates to a spatially-variant model of a point spread function and its role in enhancing medical image resolution. For instance, a method of the present disclosure comprises receiving a first medical image having a first resolution, applying a neural network to the first medical image, the neural network including a first subset of layers and, subsequently, a second subset of layers, the first subset of layers of the neural network generating, from the first medical image, a second medical image having a second resolution and the second subset of layers of the neural network generating, from the second medical image, a third medical image having a third resolution, and outputting the third medical image, wherein the first resolution is lower than the second resolution and the second resolution is lower than the third resolution.
1. An apparatus for enhancing computed tomography image resolution, comprising:
processing circuitry configured to
receive a first medical image having a first resolution,
apply a neural network to the first medical image, the neural network including a first subset of layers and, subsequently, a second subset of layers, the first subset of layers of the neural network being configured to generate, from the first medical image, a second medical image having a second resolution and the second subset of layers of the neural network being configured to generate, from the second medical image, a third medical image having a third resolution, and
output the third medical image,
wherein the first resolution is lower than the second resolution, the second resolution is lower than the third resolution, and the first subset of layers of the neural network generates the second medical image based on an intrinsic physics-based model of a point spread function.
2. The apparatus according to claim 1 , wherein the intrinsic physics-based model of the point spread function is a spatially-variant model.
3. The apparatus according to claim 2 , wherein the spatially-variant model is a Gaussian-based model.
4. The apparatus according to claim 1 , wherein the second subset of layers of the neural network is based on a convolutional neural network.
5. A method for enhancing computed tomography image resolution, comprising:
receiving, by processing circuitry, a first medical image having a first resolution;
applying, by the processing circuitry, a neural network to the first medical image, the neural network including a first subset of layers and, subsequently, a second subset of layers, the first subset of layers of the neural network generating, from the first medical image, a second medical image having a second resolution and the second subset of layers of the neural network generating, from the second medical image, a third medical image having a third resolution; and
outputting, by the processing circuitry, the third medical image,
wherein the first resolution is lower than the second resolution, the second resolution is lower than the third resolution, and the first subset of layers of the neural network generates the second medical image based on an intrinsic physics-based model of a point spread function.
6. The method according to claim 5 , wherein the intrinsic physics-based model of the point spread function is a spatially-variant model.
7. The method according to claim 6 , wherein the spatially-variant model is a Gaussian-based model.
8. The method according to claim 5 , wherein the second subset of layers of the neural network is based on a convolutional neural network.
9. A non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method for enhancing computed tomography image resolution, comprising:
receiving a first medical image having a first resolution;
applying a neural network to the first medical image, the neural network including a first subset of layers and, subsequently, a second subset of layers, the first subset of layers of the neural network generating, from the first medical image, a second medical image having a second resolution and the second subset of layers of the neural network generating, from the second medical image, a third medical image having a third resolution; and
outputting the third medical image,
wherein the first resolution is lower than the second resolution, the second resolution is lower than the third resolution, and the first subset of lavers of the neural network generates the second medical image based on an intrinsic physics-based model of a point spread function.
10. The non-transitory computer-readable storage medium according to claim 9 , wherein the intrinsic physics-based model of the point spread function is a spatially-variant model.
11. The non-transitory computer-readable storage medium according to claim 10 , wherein the spatially-variant model is a Gaussian-based model.
12. The non-transitory computer-readable storage medium according to claim 9 , wherein the second subset of layers of the neural network is based on a convolutional neural network.