  The integrated positron emission tomography/magnetic resonance imaging
(PET/MRI) scanner facilitates the simultaneous acquisition of metabolic
information via PET and morphological information with high soft-tissue
contrast using MRI. Although PET/MRI facilitates the capture of high-accuracy
fusion images, its major drawback can be attributed to the difficulty
encountered when performing attenuation correction, which is necessary for
quantitative PET evaluation. The combined PET/MRI scanning requires the
generation of attenuation-correction maps from MRI owing to no direct
relationship between the gamma-ray attenuation information and MRIs. While
MRI-based bone-tissue segmentation can be readily performed for the head and
pelvis regions, the realization of accurate bone segmentation via chest CT
generation remains a challenging task. This can be attributed to the
respiratory and cardiac motions occurring in the chest as well as its
anatomically complicated structure and relatively thin bone cortex. This paper
presents a means to minimise the anatomical structural changes without human
annotation by adding structural constraints using a modality-independent
neighbourhood descriptor (MIND) to a generative adversarial network (GAN) that
can transform unpaired images. The results obtained in this study revealed the
proposed U-GAT-IT + MIND approach to outperform all other competing approaches.
The findings of this study hint towards possibility of synthesising clinically
acceptable CT images from chest MRI without human annotation, thereby
minimising the changes in the anatomical structure.
