WeChat Mini Program
Old Version Features

256层HRCT及重建技术在听骨链损伤术前评估中的应用

Journal of Practical Radiology(2017)

Cited 7|Views4
Abstract
目的 探讨256层高分辨率CT(HRCT)及重建技术在听骨链损伤术前评估中的临床应用价值.方法 对106例颞骨外伤患者进行256层HRCT轴位扫描,收集其中听骨链损伤患者38例.用Philips工作站,行颞骨多平面重建(MPR)、听骨链的曲面重建(CPR)、3D容积再现(3D VR)观察分析听骨链损伤位置、类型及其相邻结构受累的情况,并进行随访.计算原始轴位(AX)、MPR、CPR、3D VR的显示率.结果 106例颞骨外伤患者中,38例(76耳)听骨链损伤,听小骨脱位43耳,其中锤砧关节脱位22耳,锤砧并砧镫关节脱位6耳,砧镫关节脱位3耳,听小骨转位11耳,镫骨前庭脱位1耳;听小骨骨折4耳,其中锤骨骨折2耳,砧骨骨折1耳,镫骨骨折1耳.38例听骨链损伤患者中11例行手术治疗,术后结果与影像诊断结果一致.AX、MPR、CPR、3D VR的显示率分别为97.87%、100%、97.87%、82.98%.结论 256层HRCT及重建技术能清晰显示听骨链损伤的情况,是术前评估的可靠方法.
More
Translated text
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