Multimodal MRI synthesis using unified generative adversarial networks.

ABSTRACT:

Purpose

Complementary information obtained from multiple contrasts of tissue facilitates physicians assessing, diagnosing and planning treatment of a variety of diseases. However, acquiring multiple contrasts magnetic resonance images (MRI) for every patient using multiple pulse sequences is time-consuming and expensive, where, medical image synthesis has been demonstrated as an effective alternative. The purpose of this study is to develop a unified framework for multimodal MR image synthesis.

Methods

A unified generative adversarial network consisting of only a single generator and a single discriminator was developed to learn the mappings among images of four different modalities. The generator took an image and its modality label as inputs and learned to synthesize the i

SUBMITTER: Dai X 

PROVIDER: S-EPMC7796974 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature