Multi-modal brain MRI synthesis based on SwinUNETR

TL;DR

This paper introduces SwinUNETR, a hybrid neural network combining Swin Transformer and CNNs, to synthesize missing brain MRI modalities, improving image quality and diagnostic utility.

Contribution

The paper presents a novel SwinUNETR architecture that effectively integrates global and local features for accurate MRI modality synthesis.

Findings

SwinUNETR outperforms existing methods in image quality

It achieves higher anatomical consistency in generated images

Demonstrates improved diagnostic value of synthesized MRIs

Abstract

Multi-modal brain magnetic resonance imaging (MRI) plays a crucial role in clinical diagnostics by providing complementary information across different imaging modalities. However, a common challenge in clinical practice is missing MRI modalities. In this paper, we apply SwinUNETR to the synthesize of missing modalities in brain MRI. SwinUNETR is a novel neural network architecture designed for medical image analysis, integrating the strengths of Swin Transformer and convolutional neural networks (CNNs). The Swin Transformer, a variant of the Vision Transformer (ViT), incorporates hierarchical feature extraction and window-based self-attention mechanisms, enabling it to capture both local and global contextual information effectively. By combining the Swin Transformer with CNNs, SwinUNETR merges global context awareness with detailed spatial resolution. This hybrid approach addresses the challenges posed by the varying modality characteristics and complex brain structures, facilitating the generation of accurate and realistic synthetic images. We evaluate the performance of SwinUNETR on brain MRI datasets and demonstrate its superior capability in generating clinically valuable images. Our results show significant improvements in image quality, anatomical consistency, and diagnostic value.