论文标题
基于任务的深层网络评估正式降级的深层网络，并用基于变压器的观察者评估
Task-based Assessment of Deep Networks for Sinogram Denoising with A Transformer-based Observer
论文作者
论文摘要
可以在正式域中使用多种监督学习方法用于低剂量CT deno。传统的模型观察者被广泛用于评估这些方法。但是，辛克图领域的评估仍然是深度学习的低剂量CT denoising的开放挑战。由于医疗CT图像中的每个病变都对应于正弦图域中的狭窄正弦条带，因此我们在这里提出了一个基于变压器的模型观察者来评估正弦图域监督的学习方法。数值结果表明，我们的基于变压器的模型可以很好地对Laguerre-Gauss通道的酒店观察者（LG-CHO）进行信号著名的（SKE）（SKE）和背景知名（BKS）任务。提出的模型观察者用于评估两种经典的基于CNN的正式纹状体结构域Deoing方法。结果证明了这种基于变压器的观察者模型在开发辛克图域中的深层低剂量CT降解方法中的实用性和潜力。
A variety of supervise learning methods are available for low-dose CT denoising in the sinogram domain. Traditional model observers are widely employed to evaluate these methods. However, the sinogram domain evaluation remains an open challenge for deep learning-based low-dose CT denoising. Since each lesion in medical CT images corresponds to a narrow sinusoidal strip in sinogram domain, here we proposed a transformer-based model observer to evaluate sinogram domain supervised learning methods. The numerical results indicate that our transformer-based model well-approximates the Laguerre-Gauss channelized Hotelling observer (LG-CHO) for a signal-known-exactly (SKE) and background-known-statistically (BKS) task. The proposed model observer is employed to assess two classic CNN-based sinogram domain denoising methods. The results demonstrate a utility and potential of this transformer-based observer model in developing deep low-dose CT denoising methods in the sinogram domain.
