WeChat Mini Program
Old Version Features

应用呼吸双相CT定量评估COPD患者肺气肿及空气潴留状况

China Medical Devices(2022)

中国中医科学院广安门医院

Cited 0|Views30
Abstract
目的 评估呼吸双相CT定量技术评价慢性阻塞性肺疾病(Chronic Obstructive Pulmonary Disease,COPD)肺气肿及小气道功能障碍程度的价值.方法 研究纳入35例COPD患者(病例组)和32例非吸烟肺功能(Pulmonary Function Test,PFT)正常者(对照组)作为研究对象.在呼吸双相CT上测量肺气肿指数(Emphysema Index,EI)、空气潴留指数(Air Trapping,AT)、吸气相平均肺密度(Inspiration Mean Lung Density,MLDin)和呼气相平均肺密度(Expiation Mean Lung Density,MLDex)及二者的比值(E/IMLD)、吸气相肺体积(Inspiration Lung Volume,LVin)和呼气相肺体积(Expiation Lung Volume,LVex)及二者的比值(E/ILV).比较对照组与病例组、轻中度病例组与重度病例组CT定量参数,并与PFT参数:用力肺活量(Furced Vital Capacity,FVC);1 s用力呼气容积(Forced Expiratory Voluime in One Second,FEV1)、1 s用力呼气容积占用力肺活量的比值(FEV1/FVC)、最大呼气中期流速(Mid Expiratory Phase of Forced Expiratory Flow,FEF25%~75%)、残气容积(Residual Volume,RV)、肺总量(Total Lung Capacity,TLC)、残气容积与肺总量的比值(RV/TLC)进行相关性分析.结果 与对照组相比,病例组患者全肺的LV、EI、AT、E/ILV、E/IMLD升高,MLD降低(P<0.05).轻中度病例组与重度病例组间的LV、EI、AT、E/ILV、E/IMLD、MLD有显著差异(P<0.05),反映肺气肿程度的指标EI与PFT指标FVC%、FEVi%、FEVi/FVC有较高相关性,反映空气潴留指标AT、E/ILV、E/IMLD与PFT中小气道功能指标FEF25%~75%、RV和RV/TLC密切相关.结论 COPD患者呼吸双相CT的密度和体积变化与肺气肿的程度及小气道功能障碍密切相关,其中呼气相指标可以更好地反映小气道功能.
More
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined