摘要
目的阿尔茨海默症(Alzheimer’s disease,AD)作为一种常见的老年性痴呆疾病,近年来已成为全球公共卫生面临的重大挑战,设计一种有效且精确的阿尔茨海默症早期诊断模型具有重要的临床意义和迫切需求。目前,阿尔茨海默症的临床诊断通常依赖于正电子发射断层扫描(positron emission tomography,PET)和核磁共振成像(magnetic resonance imaging,MRI)两种医学影像数据。然而,由于这两种模态间存在信息差异大、未精确配准等问题,现有的基于人工智能(artificial intelligence,AI)的诊断模型大多仅使用单一的MRI数据。这在一定程度上限制了多模态影像信息的充分利用和分类性能的进一步提升,制约了其临床实用性。针对上述问题,提出一种结合DenseNet和Mamba的多模态医学脑影像阿尔茨海默症早期诊断模型DenseMamba。方法该方法以经过标准预处理流程后的PET和MRI数据为输入,预处理流程包括:颅骨剥离、配准、偏置场校正和归一化。MRI和PET级联后首先经过卷积层和激活层进行初步特征提取,提取到的特征再依次经过若干个交替的Denseblock和TransMamba模块分别进行局部和全局的特征提取。Denseblock内的密集连接结构,增强了局部特征的提取和传播,从而能够捕捉影像中的细节信息;而TransMamba模块则基于状态空间模型,高效地建模全局依赖关系。交替的Denseblock和TransMamba使得模型能够更全面地理解多模态数据信息,充分挖掘多模态数据在临床诊断任务上的潜力。最后,将提取到的特征送入分类器得到疾病预测结果。结果为验证方法的有效性,在公开的ADNI(Alzheimer’s disease neuroimaging initiative)数据集上进行了评估。最终模型的准确率(accuracy)、精确度(precision)、召回率(recall)和F1值分别为92.42%、92.5%、92.42%和92.21%。DenseMamba在阿尔茨海默症分类任务中较对比算法表现优异,比次优算法准确率提升0.42%。结论实验结果表明,DenseMamba能够充分发挥PET和MRI数据的潜力,显著提升分类性能,为阿尔茨海默症的早期诊断提供更精准的支持。
Objective Alzheimer’s disease(AD)is a common form of dementia in the elderly and has become a major challenge in global public health in recent years.With the acceleration of global aging,the incidence of AD continues to rise,which places tremendous pressure on medical treatment and caregiving.The main characteristic of AD is the gradual decline in cognitive functions,particularly memory,language,and decision-making abilities,which are often accompa⁃nied by irreversible neuronal loss and brain atrophy.As the disease progresses,it becomes increasingly difficult to differen⁃tiate AD from other types of dementia,which makes early diagnosis more challenging.Therefore,early diagnosis is crucial for timely intervention and personalized treatment plans,as it can effectively delay disease progression.Currently,clinical diagnosis of AD typically relies on neuropsychological assessments,biomarker detection,and neuroimaging techniques.Among these approaches,positron emission tomography(PET)and magnetic resonance imaging(MRI)are the most com⁃monly used imaging methods to evaluate the brain structure and function of AD patients.PET provides information on brain metabolism and amyloid plaques,while MRI offers high-resolution structural images that can reveal brain atrophy and other neuroanatomical changes related to the disease.However,the two imaging methods significantly differ in various aspects,such as resolution,contrast,and spatial alignment issues.As a result,existing artificial intelligence(AI)-based diagnos⁃tic models often rely on single-modality data,particularly MRI images.This reliance limits the full utilization of multi⁃modal information and restricts further improvements in classification performance.To address this issue,this study pro⁃poses an early diagnosis model for AD based on multimodal imaging data(PET and MRI)——DenseMamba.This model combines the advantages of both imaging modalities,which overcomes the limitations of traditional single-modality meth⁃ods.The model adopts a state-space model-based framework(Mamba)to effectively extract and fuse heterogeneous modal features,which achieves more robust classification performance.Method The DenseMamba model uses multimodal PET and MRI imaging data as input.To effectively integrate the two types of imaging data,the model first performs initial fea⁃ture extraction through a convolutional layer and an activation layer.The extracted features are then passed through several alternating Denseblock and TransMamba modules for further local and global feature extraction.The Denseblock module,with its dense connection design,enhances feature learning capability by facilitating feature reuse across layers,which effectively mitigates the vanishing gradient problem in deep neural networks.This dense connectivity helps capture com⁃plex patterns and dependencies within the data.By contrast,the TransMamba module focuses on capturing global feature representations,modeling long-range dependencies within the data,and effectively integrating cross-space and-time related information.After multiple rounds of local and global feature extraction,the final features are processed by a classi⁃fication head(composed of fully connected layers and a softmax output layer)to perform the multi-class classification task.The final output is the predicted category for each input image,which indicates whether the patient is classified as healthy control,mild cognitive impairment,or AD.Result To validate the effectiveness of the DenseMamba model,extensive experiments were conducted using the publicly available Alzheimer’s disease neuroimaging initiative(ADNI)dataset.This dataset includes multimodal imaging data(PET and MRI),and the imaging data have been preprocessed to ensure alignment between PET and MRI images.Experimental results show that the DenseMamba model significantly outperforms traditional single-modality methods;specifically,it achieves an accuracy of 92.42%,precision of 92.5%,recall of 92.42%,and an F1 score of 92.21%.Conclusion DenseMamba provides an efficient and accurate solution for the early diagnosis of AD.By combining the complementary advantages of PET and MRI,DenseMamba overcomes some of the limi⁃tations of traditional single-modality methods,such as insufficient feature representation and data misalignment issues.Experimental results demonstrate that DenseMamba performs excellently on the ADNI dataset,which significantly improves classification performance and far surpasses existing single-modality imaging classification methods.This approach exhibits strong clinical application potential,which provides precise support for the early diagnosis of AD and facilitates earlier interventions and personalized treatments.In the future,further improvements to the DenseMamba model may include introducing advanced fusion techniques and expanding the dataset to include a broader patient population and additional imaging modalities.These enhancements will further improve the generalization ability and clinical applicability of the model,which offer more effective tools for the early diagnosis and treatment of AD and other neurodegenerative diseases.
作者
胡馨悦
石雨
刘羽
Hu Xinyue;Shi Yu;Liu Yu(Department of Biomedical Engineering,Hefei University of Technology,Hefei 230009,China;Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,Hefei University of Technology,Hefei 230009,China)
出处
《中国图象图形学报》
北大核心
2025年第10期3230-3241,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(62176081,U23A20294)。
