Towards Test Time Adaptation in Low Dose Computed Tomography Denoising Via Bias Modulation

Test-time adaptation (TTA) is crucial for robust medical image analysis, particularly in low-dose CT denoising where models trained on one noise level often fail to generalize to unseen noise intensities. This work proposes a novel TTA method that adapts the parameters of any pre-trained denoising model to denoise unknown test images. We demonstrate that surprisingly, fine-tuning only the convolutional layer biases achieves optimal TTA performance for this task. Building upon this, we introduce a novel pseudo-labeling strategy: re-corrupting the initially restored images to generate targets for further adaptation. This pseudo-labeling is combined with a knowledge distillation framework to efficiently update the model parameters during testing. Evaluations on low-dose CT scans demonstrate significant improvements in denoising performance, with peak signal-to-noise ratio (PSNR) gains of up to 3 dB observed.