Abstract
Parkinson’s disease is a neurodegenerative disorder that often leads to abnormal atrophy in certain brain regions. Due to the ability to display brain structures non-invasively, magnetic resonance imaging (MRI) is widely used in the diagnosis of Parkinson’s disease. However, existing methods have limited exploration of the rich information present in multi-modality MRI, such as T1 and T2 weighted MRI, and rarely utilized the correlation between them. Therefore, we propose a dual interaction network (DINet) for Parkinson’s disease diagnosis using T1 and T2 MRI. Specifically, considering modality specificity, two separate convolutional neural networks are employed for feature extraction from T1 and T2 MRI, respectively. Taking into account the correlation between multi-modality MRI, we introduce a novel modality interaction module DINet to extract cross-modality complementary information at multiple scales. To capture disease-related local and global information, we also propose a scale interaction module to explore the consistency information across adjacent scales. Finally, full-scale features are fused for Parkinson’s disease diagnosis. We validate our proposed DINet on the Parkinson’s Progression Markers Initiative dataset. Experimental results show our proposed DINet achieves accuracy of 93.11% and 89.85% in Parkinson’s disease diagnosis and early diagnosis, respectively. Our proposed DINet not only enables accurate diagnosis of Parkinson’s disease but also effectively explores feature interaction mechanisms in modality and scale views.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amoroso, N., La Rocca, M., Monaco, A., Bellotti, R., Tangaro, S.: Complex networks reveal early MRI markers of Parkinson’s disease. Med. Image Anal. 48, 12–24 (2018)
Bloem, B.R., Okun, M.S., Klein, C.: Parkinson’s disease. Lancet 397(10291), 2284–2303 (2021)
Zhang, C., Dou, B., Sami, M.U., Xiao, Q., Chen, N., Li, K.: Dynamic alterations of spontaneous neural activity in Parkinson’s disease: a resting-state fMRI study. Front. Neurol. 10, 474569 (2019)
Tang, X., Zhang, C., Guo, R., Yang, X., Qian, X.: A causality-aware graph convolutional network framework for rigidity assessment in Parkinsonians. IEEE Trans. Med. Imaging 43(1), 229–240 (2024)
Suuronen, I., Airola, A., Pahikkala, T., Murtojärvi, M., Kaasinen, V., Railo, H.: Budget-based classification of Parkinson’s disease from resting state EEG. IEEE J. Biomed. Health Inform. 27(8), 3740–3747 (2023)
Cigdem, O., Beheshti, I., Demirel, H.: Effects of different covariates and contrasts on classification of Parkinson’s disease using structural MRI. Comput. Biol. Med. 99, 173–181 (2018)
Lei, B., et al.: Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med. Image Anal. 61, 101632 (2020)
Cui, X., et al.: A multi-scale hybrid attention networks based on multi-view images for the diagnosis of Parkinson’s disease. IEEE Trans. Instrum. Meas. 73, 1–11 (2024)
Sivaranjini, S., Sujatha, C.: Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimedia Tools Appl. 79(21), 15467–15479 (2020)
Wang, T., Chen, X., Zhang, J., Feng, Q., Huang, M.: Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases. Med. Image Anal. 88, 102842 (2023)
Kim, M., Won, J.H., Youn, J., Park, H.: Joint-connectivity-based sparse canonical correlation analysis of imaging genetics for detecting biomarkers of Parkinson’s disease. IEEE Trans. Med. Imaging 39(1), 23–34 (2020)
Lei, H., et al.: Parkinson’s disease diagnosis via joint learning from multiple modalities and relations. IEEE J. Biomed. Health Inform. 23(4), 1437–1449 (2019)
Yang, Y., Wei, L., Hu, Y., Wu, Y., Hu, L., Nie, S.: Classification of Parkinson’s disease based on multi-modal features and stacking ensemble learning. J. Neurosci. Meth. 350, 109019 (2021)
Song, X., et al.: Multicenter and multichannel pooling GCN for early ad diagnosis based on dual-modality fused brain network. IEEE Trans. Med. Imaging 42(2), 354–367 (2023)
Fang, F., Yao, Y., Zhou, T., Xie, G., Lu, J.: Self-supervised multi-modal hybrid fusion network for brain tumor segmentation. IEEE J. Biomed. Health Inform. 26(11), 5310–5320 (2021)
Cai, H., Gao, Y., Liu, M.: Graph transformer geometric learning of brain networks using multimodal MR images for brain age estimation. IEEE Trans. Med. Imaging 42(2), 456–466 (2023)
Ma, M., et al.: A multimodal hyper-fusion transformer for remote sensing image classification. Inf. Fus. 96, 66–79 (2023)
He, X., Wang, Y., Zhao, S., Chen, X.: Co-attention fusion network for multimodal skin cancer diagnosis. Pattern Recogn. 133, 108990 (2023)
Gao, X., Shi, F., Shen, D., Liu, M.: Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in Alzheimer’s disease. IEEE J. Biomed. Health Inform. 26(1), 36–43 (2021)
Chen, X., et al.: DuSFE: dual-channel squeeze-fusion-excitation co-attention for cross-modality registration of cardiac SPECT and CT. Med. Image Anal. 88, 102840 (2023)
Pan, Y., Liu, M., Xia, Y., Shen, D.: Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6839–6853 (2022)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 62172444, in part by the Natural Science Foundation of Hunan Province under Grant 2022JJ30753, in part by the Central South University Innovation-Driven Research Programme under Grant 2023CXQD018, and in part by the High Performance Computing Center of Central South University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, J., Du, H., Mao, J., Zhu, J., Tian, X. (2024). A Novel Dual Interactive Network for Parkinson’s Disease Diagnosis Based on Multi-modality Magnetic Resonance Imaging. In: Peng, W., Cai, Z., Skums, P. (eds) Bioinformatics Research and Applications. ISBRA 2024. Lecture Notes in Computer Science(), vol 14955. Springer, Singapore. https://doi.org/10.1007/978-981-97-5131-0_37
Download citation
DOI: https://doi.org/10.1007/978-981-97-5131-0_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5130-3
Online ISBN: 978-981-97-5131-0
eBook Packages: Computer ScienceComputer Science (R0)