SYSTEMS AND METHODS FOR ACCELERATING SPECT IMAGING

1 . A computer-implemented method for improving image quality comprising:

(a) receiving a first medical image of a subject, wherein the first medical image is acquired with an acceleration scheme using single-photon emission computed tomography (SPECT);

(b) combining the medical image with a second medical image acquired using computed tomography (CT) to generate an input image; and

(c) applying a deep learning network model to the input image and outputting an enhanced medical image, wherein the deep learning network model is selected based at least in part on the acceleration scheme.

2 . The computer-implemented method of claim 1 , wherein the enhanced medical image has an image quality same as a SPECT image acquired with an acquisition time longer than the acquisition time of the acceleration scheme or has an image quality improved over the first medical image.

3 . The computer-implemented method of claim 1 , wherein the acceleration scheme comprises at least a first parameter indicating a shortened acquisition time per acquisition plane or a second parameter indicating a reduction of the number of acquisition planes.

4 . The computer-implemented method of claim 1 , wherein the acceleration scheme comprises a first parameter indicating a shortened acquisition time per acquisition plane, and wherein the deep learning network model is trained using training dataset comprising a SPECT image acquired with shortened acquisition time per acquisition plane, a corresponding CT image and a SPECT image acquired using a standard acquisition time acquisition plane.

5 . The computer-implemented method of claim 4 , wherein the input image to the deep learning network model comprises a plurality of image slices and wherein the deep learning network model comprises a 2D convolutional layer.

6 . The computer-implemented method of claim 4 , wherein the deep learning network model is trained using a loss function to enhance an accuracy in a region of interest.

7 . The computer-implemented method of claim 4 , wherein the deep learning network model is trained using an attention mask.

8 . The computer-implemented method of claim 1 , wherein the acceleration scheme comprises a second parameter indicative of a reduction of the number of acquisition planes, and wherein the deep learning network model is trained using training dataset comprising a SPECT image acquired with a reduction of acquisition planes, a corresponding CT image and a SPECT image acquired using a standard number of acquisition planes.

9 . The computer-implemented method of claim 8 , wherein the input image to the deep learning network model comprises co-registered 3D volume of the first medical image and the second medical image.

10 . The computer-implemented method of claim 9 , wherein the deep learning network model comprises a 3D convolutional layer.

11 . The computer-implemented method of claim 1 , wherein the deep learning network model is selected from a plurality of trained models, and wherein the plurality of trained models correspond to different types of artifacts or different acceleration schemes.

12 . The computer-implemented method of claim 1 , wherein the first medical image is processed by a convolutional neural network (CNN) to synthesize one or more projection planes prior to operation (c).

13 . The computer-implemented method of claim 1 , wherein the first medical image and the second medical image are acquired simultaneously.

14 . The computer-implemented method of claim 13 , wherein the second medical image is acquired without acceleration.

15 . 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) receiving a first medical image of a subject, wherein the first medical image is acquired with an acceleration scheme using single-photon emission computed tomography (SPECT);

(b) combining the medical image with a second medical image acquired using computed tomography (CT) to generate an input image; and

(c) applying a deep learning network model to the input image and outputting an enhanced medical image, wherein the deep learning network model is selected based at least in part on the acceleration scheme.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the enhanced medical image has an image quality same as a SPECT image acquired with an acquisition time longer than the acquisition time of the acceleration scheme or has an image quality improved over the first medical image.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein the acceleration scheme comprises at least a first parameter indicating a shortened acquisition time per acquisition plane or a second parameter indicating a reduction of the number of acquisition planes.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein the acceleration scheme comprises a first parameter indicating a shortened acquisition time per acquisition plane, and wherein the deep learning network model is trained using training dataset comprising a SPECT image acquired with shortened acquisition time per acquisition plane, a corresponding CT image and a SPECT image acquired using a standard acquisition time acquisition plane.

19 . The non-transitory computer-readable storage medium of claim 18 , wherein the input image to the deep learning network model comprises a plurality of image slices and wherein the deep learning network model comprises a 2D convolutional layer.

20 . The non-transitory computer-readable storage medium of claim 18 , wherein the deep learning network model is trained using a loss function to enhance an accuracy in a region of interest.