Boston College researchers developed a generative AI method that removes noise from fMRI brain scans, achieving over 200% improvement compared to prior techniques.
Key Details
- 1Boston College team created DeepCor, an AI-based denoising method for fMRI images.
- 2DeepCor improved performance by over 200% compared to previous methods such as CompCor.
- 3In real fMRI data, DeepCor achieved a 215% improvement in noise removal from face responses and 339% for clarifying synthetic data.
- 4The study was published in Nature Methods on November 28, 2025 (DOI: 10.1038/s41592-025-02967-x).
- 5Generative AI distinguishes between noise patterns in neural and non-neural brain regions.
- 6Accessible deployment and denoising large public datasets are targeted next steps.
Why It Matters
Effective removal of noise from fMRI data facilitates more accurate brain imaging, potentially accelerating neuroscience discoveries and clinical research. This improvement in image quality could set a new standard for preprocessing in neuroimaging and radiology AI workflows.
Source
EurekAlert
Related News
•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.
•EurekAlert
USC Unveils Joint Biomedical Engineering Department Bridging Medicine, Engineering, and Imaging
USC's medical and engineering schools launch a joint biomedical engineering department to accelerate interdisciplinary research and innovation, including imaging and AI.
•EurekAlert
AI-Enhanced CT Heart Fat Measurement Boosts Cardiovascular Risk Prediction
AI-derived measurement of heart fat from CT scans significantly improves long-term cardiovascular disease risk prediction.