Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Using Generative Adversarial Networks to eliminate RF and gradient interference in neurophysiology signals recorded simultaneously with functional MRI. |
| Συγγραφείς: |
Li Y, Bortel A, Ayad F, Shmuel A |
| Πηγή: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2025 Jul; Vol. 2025, pp. 1-5. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: [IEEE] Country of Publication: United States NLM ID: 101763872 Publication Model: Print Cited Medium: Internet ISSN: 2694-0604 (Electronic) Linking ISSN: 23757477 NLM ISO Abbreviation: Annu Int Conf IEEE Eng Med Biol Soc Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: [Piscataway, NJ] : [IEEE], [2007]- |
| Ιατρικοί όροι (MeSH): |
Magnetic Resonance Imaging*/methods , Radio Waves* , Signal Processing, Computer-Assisted* , Neurophysiology*/methods , Neural Networks, Computer*, Humans ; Artifacts ; Algorithms ; Brain/physiology ; Principal Component Analysis ; Generative Adversarial Networks |
| Περίληψη: |
Simultaneous functional MRI and neurophysiology recordings provide insights into the relationship between neural activity and hemodynamic responses. However, gradient switching and radiofrequency (RF) pulse transmission induce large artifacts in neurophysiology signals, severely masking neural activity. Existing artifact removal methods, such as average artifact subtraction (AAS) and principal component analysis (PCA)-based approaches, result in significant residual artifacts and potential signal loss. In this study, we propose a Generative Adversarial Network (GAN) based blind source separation model to remove gradient and RF artifacts without requiring ground truth denoised data. The model incorporates identity loss to preserve neural signals, while GAN loss and frequency loss constrain the denoising process in both time and frequency domains. We validated our approach using both simulated and empirical data. Results demonstrate that our method effectively removes artifacts while maintaining neural signal integrity, outperforming existing approaches. |
| Entry Date(s): |
Date Created: 20251203 Date Completed: 20251203 Latest Revision: 20251203 |
| Update Code: |
20251204 |
| DOI: |
10.1109/EMBC58623.2025.11254459 |
| PMID: |
41335767 |
| Βάση Δεδομένων: |
MEDLINE |