Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
507-02-009 ISMRM Abstract

Joint Diffusion and Classification to Learn Deep Brain Stimulation Outcomes from Presurgical Targets

Primary: Neuro - Neuromodulation
Secondary: Analysis Methods - Generative Models
507-02-009 · Generative Modeling: A Unifying Lens on MRI · Wednesday, 13 May, 8:20 AM–10:10 AM · Meeting Room 1.40
Keywords: Parkinson's Disease Deep Brain Stimulation Preoperative diagnosis Conditional Diffusion Generative diffusion model
Accepted
Alexandra G Roberts 1,2, Dominick Romano2,3, Mert Sisman1,2, Maneesh John1,2, Sema Akkus4, Jip de Bruin5, Jinwei Zhang6, Ceren Tozlu2, Alexey Dimov2, Thanh D Nguyen2, Pascal Spincemaille 2, Ki Sueng Choi7, Brian H Kopell4, Yi Wang2
1Electrical & Computer Engineering, Cornell University, Ithaca, United States of America
2Radiology, Weill Cornell Medicine, New York, United States of America
3Biomedical Engineering, Cornell University, Ithaca, United States of America
4Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, United States of America
5Icahn School of Medicine at Mount Sinai, New York, United States of America
6Electrical & Computer Engineering, Johns Hopkins University, Baltimore, United States of America
7Radiology, Icahn School of Medicine at Mount Sinai, New York, United States of America
Presenting Author: Alexandra G Roberts

Synopsis

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References

1. Groiss SJ, Wojtecki L, Südmeyer M, Schnitzler A. Review: Deep brain stimulation in Parkinson’s disease. Therapeutic Advances in Neurological Disorders. 2009;2(6):379-391. doi:10.1177/1756285609339382 [doi]
2. Saranza G, Lang AE. Levodopa challenge test: indications, protocol, and guide. Journal of Neurology. 2020;doi:10.1007/s00415-020-09810-7 [doi]
3. Lachenmayer L, Mürset M, Antih N, et al. Subthalamic and pallidal deep brain stimulation for Parkinson's disease-meta-analysis of outcomes. npj Parkinson s Disease. 09/06 2021;7doi:10.1038/s41531-021-00223-5 [doi]
4. Bronstein JM, Tagliati M, Alterman RL, et al. Deep Brain Stimulation for Parkinson Disease: An Expert Consensus and Review of Key Issues. Archives of Neurology. 10/19/2025 2011;68(2):165. doi:10.1001/archneurol.2010.260 [doi]
5. Liu T, Eskreis-Winkler S, Schweitzer AD, et al. Improved subthalamic nucleus depiction with quantitative susceptibility mapping. Radiology. Oct 2013;269(1):216-23. doi:10.1148/radiol.13121991 [doi]
6. Roberts AG, Kovanlikaya I, Kopell B, Spincemaille P, Nguyen T, Wang Y. Improved Visualization of the Medial Medullary Lamina via Phase Priors in Quantitative Susceptibility Mapping. presented at: International Society of Magnetic Resonance in Medicine; 2023; Toronto, Canada.
7. Dimov AV, Gupta A, Kopell BH, Wang Y. High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping. Journal of Neurosurgery. 2019;131(2):360-367. doi:10.3171/2018.3.jns172145 [doi]
8. Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. NeuroImage. 2024/04/01/ 2024;289:120547. doi:https://doi.org/10.1016/j.neuroimage.2024.120547 [doi]
9. Roberts AG, Zhang J, Tozlu C, et al. Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson Disease. Neurosurgery. Sep 18 2025;doi:10.1227/neu.0000000000003721 [doi]
10. Schoen D, Deutsch S, Mehta J, et al. Boundary complexity of cortical and subcortical areas predicts deep brain stimulation outcomes in Parkinson’s disease. Nature Communications. 2025/07/01 2025;16(1):5590. doi:10.1038/s41467-025-60695-4 [doi]
11. Roberts AG, Romano D, Zhang J, et al. QRadAR: An Open-source Toolbox for Quantitative Magnetic Resonance Radiomics Analysis and Reproducibility. presented at: International Society of Magnetic Resonance in Medicine; 2024; Singapore.
12. Dehbozorgi P, Ryabchykov O, Bocklitz TW. A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities. Computers in Biology and Medicine. 2025/03/01/ 2025;187:109768. doi:https://doi.org/10.1016/j.compbiomed.2025.109768 [doi]
13. Demircioğlu A. Are deep models in radiomics performing better than generic models? A systematic review. European Radiology Experimental. 2023/03/15 2023;7(1):11. doi:10.1186/s41747-023-00325-0 [doi]
14. Chang B, Geng Z, Mei J, et al. Application of multimodal deep learning and multi-instance learning fusion techniques in predicting STN-DBS outcomes for Parkinson's disease patients. Neurotherapeutics. Oct 2024;21(6):e00471. doi:10.1016/j.neurot.2024.e00471 [doi]
15. Peralta M, Haegelen C, Jannin P, Baxter JSH. PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes. Int J Comput Assist Radiol Surg. Aug 2021;16(8):1361-1370. doi:10.1007/s11548-021-02435-9 [doi]
16. Roberts AG, Luu HM, Şişman M, et al. Synthetic generation and latent projection denoising of rim lesions in multiple sclerosis. arXiv [eessIV]. 2025/5/29 2025;
17. Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Models. arXiv [csLG]. 2020/6/19 2020;
18. Dhariwal P, Nichol A. Diffusion models beat GANs on image synthesis. arXiv [csLG]. 2021/5/11 2021;
19. Frenay B, Verleysen M. Classification in the Presence of Label Noise: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 2014;25(5):845-869. doi:10.1109/tnnls.2013.2292894 [doi]
20. Roberts AG, Romano DJ, Şişman M, et al. Maximum spherical mean value filtering for whole-brain QSM. Magn Reson Med. Apr 2024;91(4):1586-1597. doi:10.1002/mrm.29963 [doi]
21. Dymerska B, Eckstein K, Bachrata B, et al. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magnetic Resonance in Medicine. 2021;85(4):2294-2308. doi:10.1002/mrm.28563 [doi]
22. Liu T, Khalidov I, De Rochefort L, et al. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR in Biomedicine. 2011;24(9):1129-1136. doi:10.1002/nbm.1670 [doi]
23. Dimov AV, Nguyen TD, Spincemaille P, et al. Global cerebrospinal fluid as a zero‐reference regularization for brain quantitative susceptibility mapping. Journal of Neuroimaging. 2022;32(1):141-147. doi:10.1111/jon.12923 [doi]
24. De Rochefort L, Liu T, Kressler B, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging. Magnetic Resonance in Medicine. 2010;63(1):194-206. doi:10.1002/mrm.22187 [doi]
25. Palmer JL, Coats MA, Roe CM, Hanko SM, Xiong C, Morris JC. Unified Parkinson’s Disease Rating Scale‐Motor Exam: inter‐rater reliability of advanced practice nurse and neurologist assessments. Journal of Advanced Nursing. 2010;66(6):1382-1387. doi:10.1111/j.1365-2648.2010.05313.x [doi]
26. Lin Z, Zhang X, Wang L, et al. Revisiting the L-Dopa Response as a Predictor of Motor Outcomes After Deep Brain Stimulation in Parkinson’s Disease. Frontiers in Human Neuroscience. 2021;15doi:10.3389/fnhum.2021.604433 [doi]
27. Baudendistel ST, Earhart GM. Characteristics of responders to interventions for Parkinson disease: a scoping systematic review. Neurodegener Dis Manag. Aug 2025;15(4):173-186. doi:10.1080/17582024.2025.2493465 [doi]
28. Roberts AG, Avecillas-Chasin J, Spadaccia M, et al. χ-DBS: An Open-Source Susceptibility Atlas Tool for Deep Brain Stimulation Target Visualization and Segmentation. International Congress of Parkinson’s Disease and Movement Disorders. https://www.researchgate.net/publication/392929343_ch-DBS_An_Open-Source_Susceptibility_Atlas_Tool_for_Deep_Brain_Stimulation_Target_Visualization_and_Segmentation
29. Wu J, Fu R, Fang H, et al. MedSegDiff: Medical image segmentation with diffusion probabilistic model. arXiv [csCV]. 2022/11/1 2022;
30. Liu Y, Xiao B, Zhang C, et al. Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study. Front Neurosci. 2021;15:731109. doi:10.3389/fnins.2021.731109 [doi]
31. Huang W, Ogbuji R, Zhou L, Guo L, Wang Y, Kopell BH. Motoric impairment versus iron deposition gradient in the subthalamic nucleus in Parkinson's disease. J Neurosurg. Jul 1 2021;135(1):284-290. doi:10.3171/2020.5.JNS201163 [doi]
32. Roberts A, Tozlu C, Akkus S, Spincemaille P, Kopell B, Wang Y. Dual-rater noise compensation in UPDRS improvements from deep brain stimulation via quantitative susceptibility mapping outcome prediction. presented at: International Society of Magnetic Resonance in Medicine; 2025 2025; http://dx.doi.org/10.58530/2025/4698 Concord, CA [doi]
33. Roberts AG, Zhang J, Ceren T, et al. Radiomic Prediction of Parkinson’s Disease Deep Brain Stimulation Surgery Outcomes using Quantitative Susceptibility Mapping and Label Noise Compensation. presented at: Neuromodulation; 2024; New York City.
34. Roberts AG, Zhang J, Kim H, et al. Radiomics for Deep Brain Stimulation outcome prediction using Quantitative Susceptibility Mapping (RadDBS-QSM). presented at: International Society of Magnetic Resonance in Medicine; 2023; Singapore.
35. Roberts AG, Zhang J, Tozlu C, et al. Radiomic Prediction of Parkinson's Disease Deep Brain Stimulation Surgery Outcomes using Quantitative Susceptibility Mapping and Label Noise Compensation. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation. 2025;18(4):1286-1288. doi:10.1016/j.brs.2025.05.062 [doi]
36. Roberts AG, Zhang J, Tozlu C, et al. Quantitative Susceptibility Mapping Radiomics with Label Noise Compensation for Predicting Deep Brain Stimulation Outcomes in Parkinson’s Disease. medRxiv. 2024:2024.12.26.24319663. doi:10.1101/2024.12.26.24319663 [doi]
37. Zhao W, Yang C, Tong R, et al. Relationship Between Iron Distribution in Deep Gray Matter Nuclei Measured by Quantitative Susceptibility Mapping and Motor Outcome After Deep Brain Stimulation in Patients With Parkinson's Disease. Journal of Magnetic Resonance Imaging. 2023;58(2):581-590. doi:10.1002/jmri.28574 [doi]
38. Brown G, Du G, Farace E, et al. Subcortical Iron Accumulation Pattern May Predict Neuropsychological Outcomes After Subthalamic Nucleus Deep Brain Stimulation: A Pilot Study. J Parkinsons Dis. 2022;12(3):851-863. doi:10.3233/jpd-212833 [doi]
39. Luo W, Li Y, Urtasun R, Zemel R. Understanding the effective receptive field in deep convolutional neural networks. arXiv [csCV]. 2017/1/15 2017;
40. Roberts AG, Akkus S, Spadaccia M, et al. Joint Prediction of Motor and Non-motor Deep Brain Stimulation Outcomes using Quantitative Susceptibility Mapping. presented at: International Congress of Parkinson’s Disease and Movement Disorders; 2025; Hawaii.
41. Alagapan S, Choi KS, Heisig S, et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature. 2023/10/01 2023;622(7981):130-138. doi:10.1038/s41586-023-06541-3 [doi]
42. Geraedts VJ, van Vugt JPP, Marinus J, et al. Predicting motor outcome and Quality of life after subthalamic deep brain stimulation for Parkinson's disease: The role of standard screening measures and wearable-data. J Parkinsons Dis. 2023 2023;13(4):575-588. doi:10.3233/JPD-225101 [doi]
43. Loehrer PA, Bopp MHA, Dafsari HS, et al. Microstructure predicts non-motor outcomes following deep brain stimulation in Parkinson’s disease. npj Parkinson's Disease. 2024;10(1)doi:10.1038/s41531-024-00717-y [doi]
44. Roberts AG, Zhang J, Akkus S, et al. Radiomic Prediction of Parkinson’s Disease Deep Brain Stimulation Surgery Motor and Non-motor Outcomes using Quantitative Susceptibility Mapping. presented at: Magnetic Resonance Phase, Susceptibility, and Electrical Properties Mapping; 2024;
45. Roberts AG, Sisman M, Dimov A, et al. Whole Brain Source Separation for Neurodegeneration. presented at: International Society of Magnetic Resonance in Medicine; 2024; Singapore.

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http://echo.ismrm.org/p/ISMRM2026/507-02-009