Pseudo dual-energy CT-derived iodine mapping using single-energy CT data based on a convolution neural network.

BJR open Pub Date : 2023-10-18 eCollection Date: 2023-01-01 DOI:10.1259/bjro.20220059
Yuki Yuasa, Takehiro Shiinoki, Koya Fujimoto, Hidekazu Tanaka
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引用次数: 0

Abstract

Objective: The objectives of this study are: (1) to develop a convolutional neural network model that yields pseudo high-energy CT (CTpseudo_high) from simple image processed low-energy CT (CTlow) images, and (2) to create a pseudo iodine map (IMpseudo) and pseudo virtual non-contrast (VNCpseudo) images for thoracic and abdominal regions.

Methods: Eighty patients who underwent dual-energy CT (DECT) examinations were enrolled. The data obtained from 55, 5, and 20 patients were used for training, validation, and testing, respectively. The ResUnet model was used for image generation model and was trained using CTlow and high-energy CT (CThigh) images. The proposed model performance was evaluated by calculating the CT values, image noise, mean absolute errors (MAEs), and histogram intersections (HIs).

Results: The mean difference in the CT values between CTpseudo_high and CThigh images were less than 6 Hounsfield unit (HU) for all evaluating patients. The image noise of CTpseudo_high was significantly lower than that of CThigh. The mean MAEs was less than 15 HU, and HIs were almost 1.000 for all the patients. The evaluation metrics of IM and VNC exhibited the same tendency as that of the comparison between CTpseudo_high and CThigh images.

Conclusions: Our results indicated that the proposed model enables to obtain the DECT images and material-specific images from only single-energy CT images.

Advances in knowledges: We constructed the CNN-based model which can generate pseudo DECT image and DECT-derived material-specific image using only simple image-processed CTlow images for the thoracic and abdominal regions.

Abstract Image

Abstract Image

Abstract Image

使用基于卷积神经网络的单能量CT数据进行伪双能量CT衍生的碘映射。
目的:本研究的目的是:(1)开发一个卷积神经网络模型,从简单的图像处理的低能量CT(CTlow)图像中产生伪高能CT(CTpseudo_high);(2)创建胸部和腹部的伪碘图(IMpseudo)和伪虚拟非对比度(VNCpseudo)图像。方法:选择80例接受双能CT(DECT)检查的患者。从55名、5名和20名患者身上获得的数据分别用于培训、验证和测试。ResUnet模型用于图像生成模型,并使用CTlow和高能CT(CThigh)图像进行训练。通过计算CT值、图像噪声、平均绝对误差(MAE)和直方图交叉点(HI)来评估所提出的模型性能。结果:所有评估患者的CTpseudo_high和CThigh图像之间的CT值平均差小于6 Hounsfield单位(HU)。CTpseudo_high的图像噪声显著低于CThigh。所有患者的平均MAE小于15HU,HI几乎为1.000。IM和VNC的评价指标表现出与CTpseudo_high和CThigh图像之间的比较相同的趋势。结论:我们的结果表明,所提出的模型能够仅从单能量CT图像中获得DECT图像和材料特异性图像。知识进展:我们构建了基于CNN的模型,该模型可以仅使用胸部和腹部的简单图像处理CTlow图像来生成伪DECT图像和DECT衍生的材料特异性图像。
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