Recently, Self-supervised learning methods able to perform image denoising without ground truth labels have been proposed. These methods create low-quality images by adding random or Gaussian noise to images and then train a model for denoising. Ideally, it would be beneficial if one can generate high-quality CT images with only a few training samples via self-supervision. However, the performance of CT denoising is generally limited due to the complexity of CT noise. To address this problem, we propose a novel self-supervised learning-based CT denoising method. In particular, we train pre-train CT denoising and noise models that can predict CT noise from Low-dose CT (LDCT) using available LDCT and Normal-dose CT (NDCT) pairs. For a given test LDCT, we generate Pseudo-LDCT and NDCT pairs using the pre-trained denoising and noise models and then update the parameters of the denoising model using these pairs to remove noise in the test LDCT. To make realistic Pseudo LDCT, we train multiple noise models from individual images and generate the noise using the ensemble of noise models. We evaluate our method on the 2016 AAPM Low-Dose CT Grand Challenge dataset. The proposed ensemble noise model can generate realistic CT noise, and thus our method significantly improves the denoising performance existing denoising models trained by supervised- and self-supervised learning.
翻译:最近,有人提议采用自我监督的学习方法,以便能够在没有地面真相标签的情况下进行图像分解,而不用地面真相标签而进行图像分解。这些方法通过在图像中添加随机或高斯噪音,然后培养一个脱色模型,从而产生低剂量CT(LDCT)和普通剂量CT(NDCT)配对的高质量CT图像,最理想的情况是,如果一个人能够通过自我监督只用几个培训样本产生高质量的CT图像,则会有好处。然而,由于CT噪音的复杂性能,CT的脱色性能一般有限。为了解决这个问题,我们建议采用一种全新的自我监督的基于学习的CT脱色方法,从而创造出低剂量CT的低质量图像。特别是,我们培训了预树前的CT分解和噪音模型,以便使用低剂量CTT(LDCT) 来预测低剂量CT(LDCT) 和普通剂量CT(NDCT)配对的CT。对于一个特定的测试,我们利用事先经过训练的脱色模型和噪音模型,然后用这些对脱色模型进行更新脱色模型的分解噪音模型。