Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN

TL;DR

This paper introduces SAMR, a GAN-based framework that synthesizes diverse anatomic and molecular MRI images with manipulated lesion information, addressing data scarcity in neuro-oncology diagnostics.

Contribution

The novel SAMR model enables simultaneous synthesis of multiple MRI sequences with lesion manipulation, surpassing existing methods in quality and diversity.

Findings

Outperforms state-of-the-art synthesis methods

Generates diverse MRI data with manipulated lesions

Improves data augmentation for neuro-oncology applications

Abstract

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional and advanced molecular MR images. However, the lack of sufficient annotated MRI data has vastly impeded the development of such automatic methods. Conventional data augmentation approaches, including flipping, scaling, rotation, and distortion are not capable of generating data with diverse image content. In this paper, we propose a method, called synthesis of anatomic and molecular MR images network (SAMR), which can simultaneously synthesize data from arbitrary manipulated lesion information on multiple anatomic and molecular MRI sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and amide proton transfer-weighted (APTw). The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators. Extensive experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.