461-02-014 ISMRM Abstract

MeanFlow Perceptual Loss (MFPL) for Low-Field MR Image Enhancement via Complementary VAE and SiT Features

Primary: Acquisition & Reconstruction - AI methods

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

461-02-014 · AI To Make Protocols, Plan, QC, and Correct Motion · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row B

Keywords: Deep learning Image enhacement Rectified flow Low field mri 0.3T

Accepted

Zechen Zhou ¹, Long Wang¹, Ajit Shankaranarayanan¹

¹Subtle Medical Inc, Menlo Park, United States of America

Presenting Author: Zechen Zhou

Synopsis

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References

1. 1. Ding, K., Ma, K., & Bovik, A. C. (2020). Image Quality Assessment: Uniting Structure and Texture Similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2), 781–792. https://doi.org/10.1109/TPAMI.2020.2984094 [doi]

2. 2. Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. In European Conference on Computer Vision (ECCV), pp. 694–711. https://doi.org/10.1007/978-3-319-46475-6_43 [doi]

3. 3. Geng, Z., Deng, M., Bai, X., et al. (2025). Mean Flows for One-step Generative Modeling. arXiv preprint arXiv:2505.13447. https://arxiv.org/abs/2505.13447

4. 4. Liu, Z., Mao, H., Wu, C. Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A ConvNet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11976–11986. https://doi.org/10.1109/CVPR52688.2022.01167 [doi]

5. 5. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. arXiv preprint arXiv:1312.6114. https://arxiv.org/abs/1312.6114

6. 6. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684–10695. https://doi.org/10.1109/CVPR52688.2022.01042 [doi]

7. 7. Lyu, M., Mei, L., Huang, S., et al. (2023). M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Scientific Data, 10, 264. https://doi.org/10.1038/s41597-023-02181-4 [doi]

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http://echo.ismrm.org/p/ISMRM2026/461-02-014