ArticleFrontiers in digital health2021
An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis.
Article in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Interpretable multimodal MRI fusion in Alzheimer's disease classification using extended parallel multilink joint ICA and 3D ResNet.iScience · 2026Article
- A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.Annals of nuclear medicine · 2026Article
- Convolutional-transformer fusion of ultrasound and diffuse optical tomography for breast lesion classification.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026Article
- Large language model-augmented offline reinforcement learning framework for sepsis management in critical care.NPJ digital medicine · 2026Article
- Feature Integration of [medRxiv : the preprint server for health sciences · 2026Article
- Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection.PloS one · 2026Article
- Multimodal AI fusion: integrating MRI with PET/CT, histopathology, and liquid biopsy for bone tumor diagnosis.Frontiers in oncology · 2026Review
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Deep learning for multi-modal medical image segmentation: a survey and comparative study.Brain imaging and behavior · 2025Article
- An unsupervised XAI framework for dementia detection with context enrichment.Scientific reports · 2025Article
- Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications.International journal of computer assisted radiology and surgery · 2025Article
- Diagnosis of Alzheimer's disease using brain [Formula: see text]-FDG PET imaging based on a state space model.Scientific reports · 2025Article
- Development of Pd-Loaded Hf-Based Metal-Organic Framework as a Dual-Modal Contrast Agent for Photoacoustic Imaging and Computed Tomography.ACS biomaterials science & engineering · 2025Article
- Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.Scientific reports · 2025Article
- X-FASNet: cross-scale feature-aware with self-attention network for cognitive decline assessment in Alzheimer's disease.Frontiers in neurology · 2025Article
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6 authors.
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Abstract
Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called "GM-PET." The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.
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