Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
465-04-010 ISMRM Abstract

PDDF-Net: A deep neural network for diagnosing Parkinson's Disease using QSM and T1w images

Accepted
Ang Gao1, Yanshuo Liu1, Yin Liu2, Shanshan Shan3, Ruixi Zhou4, Peng Wu5,6, Feng Liu7, G. Bruce Pike8, Hongfu Sun9, Yang Gao 1
1School of Computer Science and Engineering, Central South University, Changsha, China
2The Third Xiangya Hospital of Central South University, Changsha, China
3School of Radiological and Interdisciplinary Sciences, Soochow University, Suzhou, China
4Beijing University of Posts and Telecommunications, Beijing, China
5Philips Healthcare, Guangzhou, China
6Clinical & Technical Support, Philips Healthcare, Guangzhou, China
7The University of Queensland, Brisbane, Australia
8University of Calgary, Calgary, Canada
9University of Newcastle, Newcastle, Australia
Presenting Author: Yang Gao

Synopsis

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References

1. Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Mamun, S.A. and Mahmud, M., 2020. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain informatics, 7(1), p.11. doi: 10.1186/s40708-020-00112-2 [doi]
2. Welton, T., Hartono, S., Lee, W., Teh, P.Y., Hou, W., Chen, R.C., Chen, C., Lim, E.W., Prakash, K.M., Tan, L.C. and Tan, E.K., 2024. Classification of Parkinson’s disease by deep learning on midbrain MRI. Frontiers in Aging Neuroscience, 16, p.1425095. DOI: 10.3389/fnagi.2024.1425095 [doi]
3. Wang, Y., He, N., Zhang, C., Zhang, Y., Wang, C., Huang, P., Jin, Z., Li, Y., Cheng, Z., Liu, Y., Wang, X., Chen, C., Cheng, J., Liu, F., Haacke, E. M., Chen, S., Yang, G., & Yan, F. (2023). An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images. Human Brain Mapping, 44(12), 4426–4438. doi: 10.1002/hbm.26399. [doi]
4. Ding, R., Lu, H., & Liu, M. (2025). DenseFormer-MoE: A dense transformer foundation model with mixture of experts for multi-task brain image analysis. IEEE Transactions on Medical Imaging. doi: 10.1109/TMI.2025.3551514 [doi]
5. Patel SB, Goh V, FitzGerald JF, Antoniades CA. 2D and 3D Deep Learning Models for MRI-based Parkinson’s Disease Classification: A Comparative Analysis of Convolutional Kolmogorov-Arnold Networks, Convolutional Neural Networks, and Graph Convolutional Networks [EB/OL]. arXiv:2407.17380, 2024-07-24 [2025-10-29]. Available from: https://arxiv.org/abs/2407.17380
6. Yang, Y., Hu, L., Chen, Y., Gu, W., Lin, G., Xie, Y., & Nie, S. (2025). Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach. doi: 10.3389/fnagi.2025.1510192 [doi]
7. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269. doi: 10.1109/TIT.1967.1054010 [doi]
8. Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W. and Smith, S.M., 2012. Fsl. Neuroimage, 62(2), pp.782-790.doi: 10.1016/j.neuroimage.2011.09.015 [doi]
9. Tustison, N.J., Cook, P.A., Holbrook, A.J., Johnson, H.J., Muschelli, J., Devenyi, G.A., Duda, J.T., Das, S.R., Cullen, N.C., Gillen, D.L. and Yassa, M.A., 2021. The ANTsX ecosystem for quantitative biological and medical imaging. Scientific reports, 11(1), p.9068.doi: 10.1038/s41598-021-87564-6 [doi]
10. Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2999–3007. doi: 10.1109/ICCV.2017.324. [doi]

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