DIFFEOMORPHIC MR IMAGE REGISTRATION AND RECONSTRUCTION

1 . A computer-implemented method to train a neural network to perform image registration, the method comprising:

providing as input to the neural network, a first image and a second image, wherein the first image and the second image are reconstructed from a fast spin echo (FSE) magnetic resonance (MR) imaging sequence;

determining, using the neural network, a dense displacement field based at least on the first image and the second image;

obtaining, using the neural network, a transformed image based on the first image and the dense displacement field, wherein the transformed image is aligned with the second image;

computing a registration loss value based on comparison of the transformed image and the second image; and

adjusting one or more parameters of the neural network based on the registration loss value.

2 . The computer-implemented method of claim 1 , wherein determining the dense displacement field comprises:

predicting, using the neural network, a stationary velocity field; and

integrating, using the neural network, the stationary velocity field to determine the dense displacement field.

3 . The computer-implemented method of claim 1 , wherein the dense displacement field is a diffeomorphic displacement field.

4 . The computer-implemented method of claim 1 , wherein the first image is reconstructed from a first set of echoes of the FSE MR imaging sequence, and wherein the second image is reconstructed from a second set of echoes of the FSE MR imaging sequence.

5 . The computer-implemented method of claim 1 , wherein obtaining the transformed image comprises:

applying, with a spatial transform network, the dense displacement field to the first image, wherein the spatial transform network outputs the transformed image.

6 . The computer-implemented method of claim 1 , wherein computing the registration loss value comprises:

minimizing a local normalized cross correlation value based on the transformed image and the second image.

7 . The computer-implemented method of claim 1 , wherein training the neural network is an unsupervised process.

8 . A device to perform image registration, the device comprising:

one or more processors; and

a memory coupled to the one or more processors, with instructions stored thereon that, when executed by the processor, cause the one or more processors to perform operations comprising:

providing a first image and a second image as input to a trained neural network, wherein the first image and the second image are reconstructed from a fast spin echo (FSE) magnetic resonance (MR) imaging sequence;

obtaining, as output of the trained neural network, a dense displacement field for the first image;

obtaining a transformed image by applying the dense displacement field to the first image with a spatial transform network, wherein corresponding features of the transformed image and the second image are aligned; and

outputting the transformed image.

9 . The device of claim 8 , wherein obtaining the dense displacement field comprises:

obtaining, using the trained neural network, a stationary velocity field; and

integrating, using the trained neural network, the stationary velocity field to determine the dense displacement field.

10 . The device of claim 8 , wherein the device is a portable low-field MR imaging device having a display device and at least one permanent magnet.

11 . The device of claim 10 , wherein the first image is reconstructed from a first set of echoes of the FSE MR imaging sequence, and wherein the second image is reconstructed from a second set of echoes of the FSE MR imaging sequence.

12 . The device of claim 11 , wherein the first set of echoes comprise odd echoes, and wherein the second set of echoes comprises even echoes.

13 . The device of claim 12 , wherein the first image and the second image are of a human tissue or a human organ.

14 . The device of claim 13 , wherein outputting the transformed image comprises displaying the transformed image on the display device.

15 . A non-transitory computer-readable medium to train a neural network to perform image registration with instructions stored thereon that, when executed by a processor of a server, cause the processor to perform operations, the operations comprising:

providing as input to the neural network, a first image and a second image, wherein the first image and the second image are reconstructed from a fast spin echo (FSE) magnetic resonance (MR) imaging sequence;

determining, using the neural network, a dense displacement field based at least on the first image and the second image;

obtaining, using the neural network, a transformed image based on the first image and the dense displacement field, wherein the transformed image is aligned with the second image;

computing a registration loss value based on comparison of the transformed image and the second image; and

adjusting one or more parameters of the neural network based on the registration loss value.

16 . The non-transitory computer-readable medium of claim 15 , wherein determining the dense displacement field comprises:

predicting, using the neural network, a stationary velocity field; and

integrating, using the neural network, the stationary velocity field to determine the dense displacement field.

17 . The non-transitory computer-readable medium of claim 15 , wherein the first image is reconstructed from a first set of echoes of the FSE MR imaging sequence, and wherein the second image is reconstructed from a second set of echoes of the FSE MR imaging sequence.

18 . The non-transitory computer-readable medium of claim 15 , wherein obtaining the transformed image comprises:

applying, with a spatial transform network, the dense displacement field to the first image, wherein the spatial transform network outputs the transformed image.

19 . The non-transitory computer-readable medium of claim 15 , wherein computing the registration loss value comprises:

minimizing a local normalized cross correlation value based on the transformed image and the second image.

20 . The non-transitory computer-readable medium of claim 15 , wherein training the neural network is an unsupervised process.