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DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images
Praitayini Kanakaraj1, Tianyuan Yao1, Leon Y Cai2
1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
Research Square
|November 28, 2023
Summary
Deep learning approximates N4ITK bias field correction for T1w MRI, offering a portable and flexible alternative. This method ensures consistent image interpretation across diverse platforms and workflows.
Area of Science:
- Medical Imaging
- Neuroimaging
- Artificial Intelligence
Background:
- T1-weighted (T1w) MRI images suffer from low-frequency intensity artifacts caused by magnetic field inhomogeneities.
- Bias field correction is crucial for accurate spatial interpretation of T1w MRI data.
- Current state-of-the-art N4ITK bias correction lacks portability and flexibility across different computational pipelines.
Approach:
- Developed DeepN4, a deep learning model to approximate N4ITK bias field correction.
- Trained DeepN4 using N4ITK-corrected T1w MRI and bias fields from diverse cohorts and scanners.
- The network was trained in log space for enhanced accuracy in bias field approximation.
Key Points:
- DeepN4 closely approximates N4ITK bias field correction, achieving a median PSNR of 47.96 dB on test datasets.
- The model demonstrates generalizability across eight external datasets, validating its robustness.
- DeepN4 offers a portable, flexible, and fully differentiable alternative to N4ITK.
Conclusions:
- Deep neural networks can effectively approximate complex image preprocessing steps like N4ITK bias correction.
- This deep learning approach enhances flexibility and reproducibility in neuroimaging analysis.
- The release of code and models facilitates wider adoption and further methodological development.
