Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks.

ABSTRACT: The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupe

SUBMITTER: Matsuo H 

PROVIDER: S-EPMC9247083 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature