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PET and MRI image fusion based on a dense convolutional network with dual attention
Bicao Li1, Jenq-Neng Hwang2, Zhoufeng Liu3
1School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China; School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, 450000, China.
This study introduces CSpA-DN, a novel deep learning model for fusing Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) scans. The model effectively integrates complementary information, enhancing diagnostic accuracy in medical imaging.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Deep Learning
Background:
- Multimodal medical image fusion is crucial for integrating complementary information from different imaging techniques like PET and MRI.
- Existing fusion methods often struggle to balance the preservation of functional and structural details.
Purpose of the Study:
- To propose a novel fusion model, CSpA-DN, for enhanced PET and MRI image fusion.
- To improve the integration of local and global features for more accurate fused images.
Main Methods:
- Developed a fusion framework utilizing a dense convolutional network (CNN) encoder-decoder architecture.
- Incorporated a dual-attention module (spatial and channel attention) within the encoder and decoder.
- Designed a comprehensive loss function including image, structural, gradient, and perception losses.
Main Results:
- The CSpA-DN model successfully fused PET and MRI images, preserving functional information from PET and structural details from MRI.
- Experimental results demonstrated superior performance compared to state-of-the-art methods in both qualitative and quantitative assessments.
- The dual-attention mechanism adaptively integrated local and global features, enhancing fusion quality.
Conclusions:
- The proposed CSpA-DN model offers a significant advancement in medical image fusion for PET and MRI data.
- This approach holds promise for improving diagnostic capabilities by providing fused images with rich functional and structural information.
- The model's ability to preserve details and sharpen edges is critical for clinical applications.
