用SwinUNETR补全缺失的脑部MRI模态，提升诊断准确性。
Multi-modal brain MRI synthesis based on SwinUNETR
- 基于Swin Transformer与CNN融合架构，捕捉局部与全局特征。
- 在脑部MRI数据集上生成图像质量高、解剖结构一致。
- 适合临床缺模态场景，提升影像诊断可用性。
多模态脑部磁共振成像（MRI）在临床诊断中至关重要，可提供不同成像模态的互补信息。然而临床实践中常面临模态缺失问题。本文将SwinUNETR应用于脑部MRI缺失模态的合成。SwinUNETR是一种新型神经网络架构，结合了Swin Transformer与卷积神经网络（CNN）的优势。Swin Transformer作为视觉变换器（ViT）的变体，采用分块自注意力机制和层级特征提取，有效捕捉局部与全局上下文信息。通过融合两者，SwinUNETR兼具全局语义感知与精细空间分辨率。该方法能应对不同模态特性与复杂脑结构带来的挑战，实现高质量、逼真的合成图像。我们在脑部MRI数据集上评估性能，结果表明其在图像质量、解剖一致性及诊断价值方面均有显著提升。
原文摘要 · Abstract (English)
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.
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