论文标题
使用扩散和得分匹配模型之间CT和MRI图像之间的转换
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models
论文作者
论文摘要
MRI和CT是最广泛使用的医学成像方式。通常有必要获取用于诊断和治疗的多模式图像，例如放射疗法计划。但是，多模式成像不仅是昂贵的，而且还引入了MRI和CT图像之间的错位。为了应对这一挑战，计算转换是MRI和CT图像之间的可行方法，尤其是从MRI到CT图像。在本文中，我们建议在这种情况下使用一个称为扩散和得分匹配模型的新兴深度学习框架。具体而言，我们适应了deno的扩散概率和得分匹配模型，使用四种不同的抽样策略，并使用卷积神经网络和生成性对抗网络模型将其性能指标与该模型进行比较。我们的结果表明，扩散和得分匹配模型比CNN和GAN模型产生更好的合成CT图像。此外，我们使用Monte-Carlo方法研究了与扩散和得分匹配网络相关的不确定性，并通过平均其Monte-Carlo输出来改善结果。我们的研究表明，扩散和得分匹配模型具有强大的功能，可以生成以使用互补成像方式获得的图像来调节的高质量图像，在分析上具有严格的解释性，并具有清晰的解释性，并且具有CNNS和GAN的高度竞争，以进行图像合成。
MRI and CT are most widely used medical imaging modalities. It is often necessary to acquire multi-modality images for diagnosis and treatment such as radiotherapy planning. However, multi-modality imaging is not only costly but also introduces misalignment between MRI and CT images. To address this challenge, computational conversion is a viable approach between MRI and CT images, especially from MRI to CT images. In this paper, we propose to use an emerging deep learning framework called diffusion and score-matching models in this context. Specifically, we adapt denoising diffusion probabilistic and score-matching models, use four different sampling strategies, and compare their performance metrics with that using a convolutional neural network and a generative adversarial network model. Our results show that the diffusion and score-matching models generate better synthetic CT images than the CNN and GAN models. Furthermore, we investigate the uncertainties associated with the diffusion and score-matching networks using the Monte-Carlo method, and improve the results by averaging their Monte-Carlo outputs. Our study suggests that diffusion and score-matching models are powerful to generate high quality images conditioned on an image obtained using a complementary imaging modality, analytically rigorous with clear explainability, and highly competitive with CNNs and GANs for image synthesis.
