Improved 2-D Chest CT Image Enhancement With Multi-Level VGG Loss

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Ayush Chaturvedi;Ritvik Prabhu;Mukund Yadav;Wu-Chun Feng;Guohua Cao
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Abstract

Chest CT scans play an important role in diagnosing abnormalities associated with the lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19. However, because conventional normal-dose chest CT scans require a much larger amount of radiation than x-rays, practitioners seek to replace conventional CT with low-dose CT (LDCT). LDCT often generates a low-quality CT image that poses noise and, in turn, negatively affects the accuracy of diagnosis. Therefore, in the context of COVID-19, due to the large number of affected populations, efficient image-denoising techniques are needed for LDCT images. Here, we present a deep learning (DL) model that combines two neural networks to enhance the quality of low-dose chest CT images. The DL model leverages a previously developed densenet and deconvolution-based network (DDNet) for feature extraction and extends it with a pretrained VGG network inside the loss function to suppress noise. Outputs from selected multiple levels in the VGG network (ML-VGG) are leveraged for the loss calculation. We tested our DDNet with ML-VGG loss using several sources of CT images and compared its performance to DDNet without VGG loss as well as DDNet with an empirically selected single-level VGG loss (DDNet-SL-VGG) and other state-of-the-art DL models. Our results show that DDNet combined with ML-VGG (DDNet-ML-VGG) achieves state-of-the-art denoising capabilities and improves the perceptual and quantitative image quality of chest CT images. Thus, DDNet with multilevel VGG loss could potentially be used as a post-acquisition image enhancement tool for medical professionals to diagnose and monitor chest diseases with higher accuracy.
改进的二维胸部CT图像增强与多级VGG损失
胸部CT扫描在诊断与肺部相关的异常方面发挥着重要作用,例如结核病、结节病、肺炎以及最近的COVID-19。然而,由于常规的正常剂量胸部CT扫描比x射线需要更大的辐射量,从业者寻求用低剂量CT (LDCT)取代常规CT。LDCT通常会产生低质量的CT图像,产生噪音,进而影响诊断的准确性。因此,在COVID-19背景下,由于受影响人群众多,需要对LDCT图像进行高效的图像去噪技术。在这里,我们提出了一个深度学习(DL)模型,结合两个神经网络来提高低剂量胸部CT图像的质量。DL模型利用先前开发的密度网络和基于反卷积的网络(DDNet)进行特征提取,并在损失函数内使用预训练的VGG网络进行扩展,以抑制噪声。从VGG网络(ML-VGG)中选择的多个级别的输出被用于损失计算。我们使用几个CT图像源测试了带有ML-VGG损失的DDNet,并将其性能与没有VGG损失的DDNet以及经验选择的单级VGG损失的DDNet (DDNet- sl -VGG)和其他最先进的DL模型进行了比较。结果表明,DDNet结合ML-VGG (DDNet-ML-VGG)实现了最先进的去噪能力,提高了胸部CT图像的感知和定量图像质量。因此,具有多层VGG损失的DDNet有可能作为医学专业人员更高准确性诊断和监测胸部疾病的采集后图像增强工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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