Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-01-002 ISMRM Abstract

MeshD²R：Mesh-guided Dual-resolution Diffusion Reconstruction of Visual Stimuli from BOLD-fMRI Signals

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Analysis Methods - Generative Models
364-01-002 · fMRI: Analyses · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row E
Keywords: Image Reconstruction Feature fusion Generative diffusion model Fmri Brain decoder
Accepted
liu ming1,2, Shiyi Zhang 1,3, Qihui Ye4, Shuo Zhou4, Yihang Zhou1, Dong Liang1,4, Yanjie Zhu4, Hairong Zheng4,5, Haifeng Wang4
1Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2University of Chinese Academy of Sciences, Beijing, China
3Department of Computer Science, North Carolina State University, Raleigh, United States of America
4Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
5Electronic Secience and Engineering, Nanjing University, Najing, China
Presenting Author: Shiyi Zhang

Synopsis

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References

1. Scotti P, Banerjee A, Goode J, et al. Reconstructing the mind's eye: fmri-to-image with contrastive learning and diffusion priors[J]. Advances in Neural Information Processing Systems, 2023, 36: 24705-24728.
2. Scotti P S, Tripathy M, Villanueva C K T, et al. Mindeye2: Shared-subject models enable fmri-to-image with 1 hour of data[J]. arXiv preprint arXiv:2403.11207, 2024. https://doi.org/10.48550/arXiv.2403.11207 [doi]
3. Yablonski M, Karipidis I I, Kubota E, et al. The transition from vision to language: Distinct patterns of functional connectivity for subregions of the visual word form area[J]. Human Brain Mapping, 2024, 45(4): e26655. https://doi.org/10.1002/hbm.26655 [doi]
4. Gu Z, Jamison K, Kuceyeski A, et al. Decoding natural image stimuli from fmri data with a surface-based convolutional network[J]. arXiv preprint arXiv:2212.02409, 2022. https://doi.org/10.48550/arXiv.2212.02409 [doi]
5. Huang J, Kashinath K, Marcus P, et al. Spherical CNNs on unstructured grids[C]//International conference on learning representations (Poster). 2019.
6. Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PmLR, 2021: 8748-8763.
7. Kim S, Lee G, Bae S, et al. Mixco: Mix-up contrastive learning for visual representation[J]. arXiv preprint arXiv:2010.06300, 2020.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-01-002