Unified multi-protocol MRI for Alzheimer’s disease diagnosis: Dual-decoder adversarial autoencoder and ensemble residual shrinkage attention network

Magnetic Resonance Imaging (MRI) has emerged as a critical tool in Alzheimer’s Disease (AD) clinical research, owing to its exceptional soft tissue contrast and high-resolution 3D imaging capabilities. Despite its advantages, current diagnostic models often overlook the potential of multi-protocol M...

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Vydáno v:Biomedical signal processing and control Ročník 105; s. 107660
Hlavní autoři: Li, Shiyao, Lin, Shukuan, Tu, Yue, Qiao, Jianzhong, Xiao, Shenao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2025
Témata:
Alzheimer’s disease
Computer-aided diagnosis
Soft shrinkage threshold attention
Unified multi-protocol MRI
Soft shrinkage threshold attention
Unified multi-protocol MRI
Alzheimer’s disease
Computer-aided diagnosis
ISSN:1746-8094
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Shrnutí:Magnetic Resonance Imaging (MRI) has emerged as a critical tool in Alzheimer’s Disease (AD) clinical research, owing to its exceptional soft tissue contrast and high-resolution 3D imaging capabilities. Despite its advantages, current diagnostic models often overlook the potential of multi-protocol MRI imaging, leading to limited clinical applicability and practical challenges in generalizing to diverse data protocols. Furthermore, existing multi-protocol models lack a robust method for effectively aligning MRI images, resulting in model inefficient due to inconsistencies across protocols. To address these limitations, we propose a novel approach utilizing unified multi-protocol MRIs for AD diagnosis. Specifically, we introduce a double decoder adversarial autoencoder (DDAAE) to align MRIs from different protocols. The aligned MRI images are then integrated into our proposed ensemble residual soft shrinkage threshold attention (ERS2TA) diagnostic network for disease diagnosis. This framework not only leverages multi-protocol MRI images but also emphasizes disease-relevant regions while minimizing the impact of noise on diagnostic accuracy. Experimental evaluations on the ADNI dataset demonstrate superior performance in both the AD vs. Normal Controls (NC) classification task and the stable mild cognitive impairment (sMCI) vs. progressive mild cognitive impairment (pMCI) classification task, surpassing existing state-of-the-art methods. •Unified MRI Network Model improves AD classification and clinical utility.•DDAAE aligns diverse MRI protocols for accurate and standardized image generation.•ERS2TA focuses on specific disease regions and reduces imaging noise interference.•Model excels at distinguishing AD and MCI, boosting clinical diagnosis efficiency.
ISSN:1746-8094
DOI:10.1016/j.bspc.2025.107660