CPC G06T 11/60 (2013.01) [A61B 5/0033 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); G01R 33/5608 (2013.01); G06F 18/214 (2023.01); G06T 2210/41 (2013.01); G06V 2201/033 (2022.01)] | 15 Claims |
1. A method, comprising:
acquiring a magnetic resonance (MR) image;
generating, with a multi-task neural network, a pseudo CT image, a bone mask, and a bone Hounsfield unit (HU) image corresponding to the MR image, wherein the pseudo CT image comprises a set of three density classes, the density classes comprising air, tissue, and bone, and wherein the bone HU image includes image values within a bone region of interest in terms of HU; and
outputting the MR image and the pseudo CT image,
wherein the multi-task neural network is trained with a whole image regression loss for the pseudo CT image, a segmentation loss for the bone mask, and a regression loss focused on bone segments for the bone HU image.
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