Unified Multi-Modal Image Synthesis for Missing Modality Imputation

TL;DR

This paper introduces a unified generative adversarial network for imputing missing multi-modal medical images, effectively synthesizing missing modalities from any available combination, thus improving clinical data completeness.

Contribution

The proposed method uniquely integrates a commonality- and discrepancy-sensitive encoder with a dynamic feature unification module for robust multi-modal image synthesis.

Findings

Outperforms previous methods on public MRI datasets

Handles various synthesis tasks with high accuracy

Robust to random missing modalities

Abstract

Multi-modal medical images provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-modal images, thus limiting the usage of multi-modal data for clinical purposes. To address this issue, in this paper, we propose a novel unified multi-modal image synthesis method for missing modality imputation. Our method overall takes a generative adversarial architecture, which aims to synthesize missing modalities from any combination of available ones with a single model. To this end, we specifically design a Commonality- and Discrepancy-Sensitive Encoder for the generator to exploit both modality-invariant and specific information contained in input modalities. The incorporation of both types of information facilitates the generation of images with consistent anatomy and realistic details of the desired distribution. Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities. The module performs both hard integration and soft integration, ensuring the effectiveness of feature combination while avoiding information loss. Verified on two public multi-modal magnetic resonance datasets, the proposed method is effective in handling various synthesis tasks and shows superior performance compared to previous methods.