Academic Journal
Fast zero-shot deep learning-based denoising method for low-field MR images.
| Τίτλος: | Fast zero-shot deep learning-based denoising method for low-field MR images. |
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| Συγγραφείς: | Ayde R; Center for Adaptable MRI Technology, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK. reina.ayde@gmail.com.; Department of Radiology, University of Michigan, Ann Arbor, USA. reina.ayde@gmail.com., Zihlmann G; Center for Adaptable MRI Technology, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK., Salameh N; Center for Adaptable MRI Technology, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK., Sarracanie M; Center for Adaptable MRI Technology, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK. |
| Πηγή: | Magma (New York, N.Y.) [MAGMA] 2026 Aug; Vol. 39 (4), pp. 531-544. Date of Electronic Publication: 2025 Dec 22. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: Germany NLM ID: 9310752 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1352-8661 (Electronic) Linking ISSN: 09685243 NLM ISO Abbreviation: MAGMA Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2003- : Heidelberg : Springer Original Publication: New York, NY : Chapman & Hall, c1993- |
| Ιατρικοί όροι (MeSH): | Magnetic Resonance Imaging*/methods , Image Processing, Computer-Assisted*/methods , Deep Learning*, Brain/diagnostic imaging ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods ; Humans ; Signal-To-Noise Ratio ; Algorithms |
| Περίληψη: | Objective: Denoising low-field MR images is often essential to obtain image quality that is adequate for clinical diagnosis while keeping scan time patient-friendly. The recently introduced zero-shot self-supervised approach shows great promise, requiring no prior data collection for training, which is particularly challenging at low-field. Here, this scan-specific denoising approach is adapted to low-field MR data and optimized to accelerate the training process. Material and Method: We extended the zero-shot noise-as-clean method by modifying the training process to achieve faster training times. The proposed method was compared to BM4D and the recent zero-shot noise2noise methods. Denoising performance was first evaluated quantitatively on high-field data where high SNR images are available, then assessed qualitatively on prospective low-field data (0.1 T). Ultimately, we studied the denoising performance with respect to training on portions of the original data matrix as a potential strategy for further training acceleration. Results: The proposed method achieved high denoising performance across different SNR levels within a few seconds on a GPU for typical low-field data dimensions. Additionally, training on portion of the data showed potential for further training acceleration. Discussion: In the context of low-field MRI, this denoising method shows great potential, as it could be integrated into acquisition workflows relatively seamlessly to improve image quality. Code: https://github.com/reinaayde7/zs-nac.git. (© 2025. The Author(s).) |
| Competing Interests: | Declarations. Competing interest: The authors have no competing interests to declare that are relevant to the content of this article. Ethical approval: All MRI experiments were conducted following the local ethics regulations and informed consent was obtained from all subjects. The study was approved by the Ethikkommission Nordwest- und Zentralschweiz (EKNZ) (project-ID 2022-00348) and the University of Aberdeen School Ethics Review Board (School of Medicine, Medical Sciences and Nutrition, REC reference number 2144417). |
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| Contributed Indexing: | Keywords: Deep learning; Denoising; Low-field; MRI; Self-supervised; Zero-shot |
| Entry Date(s): | Date Created: 20251222 Date Completed: 20260807 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13447554 |
| DOI: | 10.1007/s10334-025-01311-w |
| PMID: | 41428276 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1352-8661 |
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| DOI: | 10.1007/s10334-025-01311-w |