SYSTEMS AND METHODS FOR MEDICAL IMAGES DENOISING USING DEEP LEARNING
Methods and systems are provided for data augmentation. The method comprises: acquiring an input image data and metadata, wherein the metadata relates to information about an image quality; training a conditional diffusion model based on the input image data and the metadata; and using the conditional diffusion model to predict a synthesized low-quality image based on an input high-quality image and corresponding metadata.
1 . A computer-implemented method for training a diffusion model comprising:
(a) obtaining a first image having a first image quality and a corresponding second image having a second image quality, wherein the first image quality is higher than the second image quality;
(b) generating training data comprising the first image, the second image and a metadata comprising information about the first image and the second image; and
(c) training a diffusion model based on the training data and optimizing parameters of the diffusion model to simulate an artifact in the second image.
2 . The computer-implemented method of claim 1 , wherein the metadata comprises the information about a scanning apparatus for acquiring the first image and the second image, an image acquisition process, a dosage of contrast agent administered for acquiring the first image and the second image, or radiopharmaceutical injection.
3 . The computer-implemented method of claim 1 , wherein generating the training data comprises generating a metadata embedding encoding the information.
4 . The computer-implemented method of claim 3 , wherein the training data comprises an embedding encoding the metadata and time associated with the first image or the second image.
5 . The computer-implemented method of claim 4 , wherein the diffusion model is a U-Net model comprising one or more downsampling blocks and one or more upsampling blocks.
6 . The computer-implemented method of claim 5 , wherein the embedding is fused with the first image or the second image in the one or more downsampling blocks or the one or more upsampling blocks.
7 . The computer-implemented method of claim 1 , further comprising during an inference stage, supplying an input comprising an input high-quality image and a corresponding metadata to the diffusion model trained in (c) and outputting a synthesized low-quality image.
8 . The computer-implemented method of claim 7 , wherein the input high-quality image is a 2.5D stack of slices.
9 . The computer-implemented method of claim 8 , further comprising chunking the 2.5D stack of slices into a plurality of chunks.
10 . The computer-implemented method of claim 9 , further comprising randomly sampling an overlapping volume of two consecutive output chunks to aggregate a plurality of output chunks to form the synthesized low-quality image.
11 . A non-transitory computer-readable medium comprising machine-executable code that, upon execution by a computer, implements a method for training a diffusion model, the method comprising:
(a) obtaining a first image having a first image quality and a corresponding second image having a second image quality, wherein the first image quality is higher than the second image quality;
(b) generating training data comprising the first image, the second image and a metadata comprising information about the first image and the second image; and
(c) training a diffusion model based on the training data and optimizing parameters of the diffusion model to simulate an artifact in the second image.
12 . The non-transitory computer-readable medium of claim 11 , wherein the metadata comprises the information about a scanning apparatus for acquiring the first image or the second image, an image acquisition process, a dosage of contrast agent administered for acquiring the first image and the second image, or radiopharmaceutical injection.
13 . The non-transitory computer-readable medium of claim 11 , wherein generating the training data comprises generating a metadata embedding encoding the information.
14 . The non-transitory computer-readable medium of claim 13 , wherein the training data comprises an embedding encoding the metadata and time associated with the first image or the second image.
15 . The non-transitory computer-readable medium of claim 14 , wherein the diffusion model is a U-Net model comprising one or more downsampling blocks and one or more upsampling blocks.
16 . The non-transitory computer-readable medium of claim 15 , wherein the embedding is fused with the first image or the second image in the one or more downsampling blocks or the one or more upsampling blocks.
17 . The non-transitory computer-readable medium of claim 11 , further comprising during an inference stage, supplying an input comprising an input high-quality image and a corresponding metadata to the diffusion model trained in (c) and outputting a synthesized low-quality image.
18 . The non-transitory computer-readable medium of claim 17 , wherein the input high-quality image is a 2.5D stack of slices.
19 . The non-transitory computer-readable medium of claim 18 , further comprising chunking the 2.5D stack of slices into a plurality of chunks.
20 . The non-transitory computer-readable medium of claim 19 , further comprising randomly sampling an overlapping volume of two consecutive output chunks to aggregate a plurality of output chunks to form the synthesized low-quality image.