Liu Anqi, Gang Grace J, Stayman J Webster
Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12925. doi: 10.1117/12.3008622. Epub 2024 Apr 1.
Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing. Diffusion models, in particular, have become extremely popular due to their ability to effectively encode rich information about images and to allow for posterior sampling to generate many possible outputs. One drawback of diffusion models is that their recurrent structure tends to be computationally expensive. In this work we apply a new Fourier diffusion approach that permits processing with many fewer time steps than the standard scalar diffusion model. We present an extension of the Fourier diffusion technique and evaluate it in a simulated breast cone-beam CT system with a sparse view acquisition.
稀疏CT重建仍是许多新型成像系统中备受关注的领域。人们尝试了许多不同的方法，包括基于模型的方法、压缩感知方法，以及最近基于深度学习的处理方法。特别是扩散模型，由于其能够有效地编码有关图像的丰富信息并允许进行后验采样以生成许多可能的输出，因此变得极为流行。扩散模型的一个缺点是其循环结构在计算上往往成本高昂。在这项工作中，我们应用了一种新的傅里叶扩散方法，该方法允许使用比标准标量扩散模型少得多的时间步长进行处理。我们提出了傅里叶扩散技术的扩展，并在具有稀疏视图采集的模拟乳腺锥束CT系统中对其进行了评估。
