Zero-shot Bias Correction: Efficient MR Image Inhomogeneity Reduction Without Any Data
This provides a zero-shot solution for medical imaging professionals to correct bias in MRI images without costly data collection, though it is incremental over prior data-free approaches.
The paper tackles the problem of MRI image inhomogeneity reduction without requiring any training data, achieving better efficiency and accuracy than existing data-free methods.
In recent years, deep neural networks for image inhomogeneity reduction have shown promising results. However, current methods with (un)supervised solutions require preparing a training dataset, which is expensive and laborious for data collection. In this work, we demonstrate a novel zero-shot deep neural networks, which requires no data for pre-training and dedicated assumption of the bias field. The designed light-weight CNN enables an efficient zero-shot adaptation for bias-corrupted image correction. Our method provides a novel solution to mitigate the biased corrupted image as iterative homogeneity refinement, which therefore ensures the considered issue can be solved easier with stable convergence of zero-shot optimization. Extensive comparison on different datasets show that the proposed method performs better than current data-free N4 methods in both efficiency and accuracy.