| CPC G06T 5/20 (2013.01) [G06T 5/50 (2013.01); G06T 5/70 (2024.01); G06T 5/73 (2024.01); G06T 2207/10016 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20104 (2013.01); G06T 2207/20192 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30004 (2013.01)] | 19 Claims |

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1. A method comprising:
receiving a medical image;
applying a filter to the medical image to produce a filtered medical image, wherein a subregion of the filtered medical image includes a filtering artifact;
displaying the filtered medical image via a display device;
receiving a selection of the subregion of the filtered medical image from a user input device;
blending, in the subregion, pixel intensity values from the filtered medical image with pixel intensity values from the medical image, to produce a blended image, wherein a visibility of the filtering artifact is attenuated in the blended image;
displaying the blended image via the display device;
storing the medical image and the blended image as a training data pair;
training a machine learning model using the training data pair;
dividing the medical image into a first plurality of patches;
dividing the blended image into a second plurality of patches, wherein a number of the first plurality of patches is equal to a number of the second plurality of patches;
pairing each patch of the first plurality of patches with a spatially corresponding patch from the second plurality of patches, to form a plurality of training data pairs; and
storing the plurality of training data pairs in non-transitory memory.
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