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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| Vydané v: | Biomedical signal processing and control Ročník 105; s. 107660 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.07.2025
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| ISSN: | 1746-8094 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 107660 |
| Author | Lin, Shukuan Tu, Yue Li, Shiyao Xiao, Shenao Qiao, Jianzhong |
| Author_xml | – sequence: 1 givenname: Shiyao surname: Li fullname: Li, Shiyao – sequence: 2 givenname: Shukuan surname: Lin fullname: Lin, Shukuan email: linshukuan@cse.neu.edu.cn – sequence: 3 givenname: Yue surname: Tu fullname: Tu, Yue – sequence: 4 givenname: Jianzhong surname: Qiao fullname: Qiao, Jianzhong email: qiaojianzhong@mail.neu.edu.cn – sequence: 5 givenname: Shenao surname: Xiao fullname: Xiao, Shenao |
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| Keywords | Soft shrinkage threshold attention Unified multi-protocol MRI Alzheimer’s disease Computer-aided diagnosis |
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| Snippet | Magnetic Resonance Imaging (MRI) has emerged as a critical tool in Alzheimer’s Disease (AD) clinical research, owing to its exceptional soft tissue contrast... |
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| StartPage | 107660 |
| SubjectTerms | Alzheimer’s disease Computer-aided diagnosis Soft shrinkage threshold attention Unified multi-protocol MRI |
| Title | Unified multi-protocol MRI for Alzheimer’s disease diagnosis: Dual-decoder adversarial autoencoder and ensemble residual shrinkage attention network |
| URI | https://dx.doi.org/10.1016/j.bspc.2025.107660 |
| Volume | 105 |
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