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

06/16/2021
by   Hidetoshi Matsuo, et al.
12

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.

READ FULL TEXT

page 8

page 13

page 14

page 15

page 16

page 17

page 19

page 22

research
05/11/2020

Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation

Abdominal fat quantification is critical since multiple vital organs are...
research
01/16/2019

MRI to CT Translation with GANs

We present a detailed description and reference implementation of prepro...
research
05/14/2021

SA-GAN: Structure-Aware Generative Adversarial Network for Shape-Preserving Synthetic CT Generation

In medical image synthesis, model training could be challenging due to t...
research
07/03/2012

Anatomical Structure Segmentation in Liver MRI Images

Segmentation of medical images is a challenging task owing to their comp...
research
08/22/2018

Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission ...
research
04/21/2018

Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training

Multiple sclerosis (MS) is a demyelinating disease of the central nervou...
research
03/31/2023

Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding

Advanced minimally invasive neurosurgery navigation relies mainly on Mag...

Please sign up or login with your details

Forgot password? Click here to reset