SYSTEMS AND METHODS FOR MULTI-CONTRAST MULTI-SCALE VISION TRANSFORMERS
Methods and systems are provided for synthesizing a contrast-weighted image in Magnetic resonance imaging (MRI). The method comprises: receiving a multi-contrast image of a subject, where the multi-contrast image comprises one or more images of one or more different contrasts; generating an input to a transformer model based at least in part on the multi-contrast image; and generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, where the target contrast is specified in a query received by the transformer model.
1 . A computer-implemented method for synthesizing a contrast-weighted image comprising:
(a) receiving a multi-contrast image of a subject, wherein the multi-contrast image comprises one or more images of one or more different contrasts;
(b) generating an input to a transformer model based at least in part on the multi-contrast image; and
(c) generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, wherein the target contrast is specified in a query received by the transformer model.
2 . The computer-implemented method of claim 1 , wherein the multi-contrast image is acquired using a magnetic resonance (MR) device.
3 . The computer-implemented method of claim 1 , wherein the input to the transformer model comprises an image encoding generated by a convolutional neural network (CNN) model.
4 . The computer-implemented method of claim 3 , wherein the image encoding is partitioned into image patches.
5 . The computer-implemented method of claim 3 , wherein the input to the transformer model comprises a combination of the image encoding and a contrast encoding.
6 . The computer-implemented method of claim 1 , wherein the transformer model comprises: i) an encoder model receiving the input and outputting multiple representations of the input having multiple scales, ii) a decoder model receiving the query and the multiple representations of the input having the multiple scales and outputting the synthesized image.
7 . The computer-implemented method of claim 6 , wherein the encoder model comprises a multi-contrast shifted window-based attention block.
8 . The computer-implemented method of claim 6 , wherein the decoder model comprises a multi-contrast shifted window-based attention block.
9 . The computer-implemented method of claim 1 , wherein the transformer model is trained utilizing a combination of synthesis loss, reconstruction loss and adversarial loss.
10 . The computer-implemented method of claim 1 , wherein the transformer model is trained utilizing multi-scale discriminators.
11 . The computer-implemented method of claim 1 , wherein the transformer model is capable of taking arbitrary number of contrasts as input.
12 . The computer-implemented method of claim 1 , further comprising displaying interpretation of the transformer model generating the synthesized image.
13 . The computer-implemented method of claim 12 , wherein the interpretation is generated based at least in part on attention scores outputted by a decoder of the transformer model.
14 . The computer-implemented method of claim 12 , wherein the interpretation comprises quantitative analysis of a contribution or importance of each of the one or more different contrasts.
15 . The computer-implemented method of claim 12 , wherein the interpretation comprises a visual representation of the attention scores indicative a relevance of a region in the one or more images or a contrast from the one or more different contrasts to the synthesized image.
16 . 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 multi-contrast image of a subject, wherein the multi-contrast image comprises one or more images of one or more different contrasts;
(b) generating an input to a transformer model based at least in part on the multi-contrast image; and
(c) generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, wherein the target contrast is specified in a query received by the transformer model.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the multi-contrast image is acquired using a magnetic resonance (MR) device.
18 . The non-transitory computer-readable storage medium of claim 16 , wherein the input to the transformer model comprises an image encoding generated by a convolutional neural network (CNN) model.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the image encoding is partitioned into image patches.
20 . The non-transitory computer-readable storage medium of claim 18 , wherein the input to the transformer model comprises a combination of the image encoding and a contrast encoding.