论文标题
通过自动X射线扫描质量增强算法提高临床诊断性能
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms
论文作者
论文摘要
在临床诊断中，从扫描设备获得的诊断图像是在提供质量医疗保健过程中进一步研究的初步证据。但是，医疗图像通常可能包含故障伪像，这些伪影是由于噪音，模糊和故障设备而引入的。这样做的原因可能是低质量或较旧的扫描设备，测试环境或技术人员缺乏培训等；但是，最终结果是，快速可靠的诊断过程受到阻碍。自动解决这些问题可能会在医院的临床工作流程中产生重大的积极影响，在那里，通常别无其他方式来处理错误/较旧的设备或合格的放射学技术人员。在本文中，针对医疗图像超级分辨率的任务进行了自动图像质量改进方法。在对标准开放数据集的实验评估中，观察结果表明，某些算法的表现更好，并且在医疗扫描的诊断质量上显示出显着提高，从而为人类诊断目的提供了更好的可视化。
In clinical diagnosis, diagnostic images that are obtained from the scanning devices serve as preliminary evidence for further investigation in the process of delivering quality healthcare. However, often the medical image may contain fault artifacts, introduced due to noise, blur and faulty equipment. The reason for this may be the low-quality or older scanning devices, the test environment or technicians lack of training etc; however, the net result is that the process of fast and reliable diagnosis is hampered. Resolving these issues automatically can have a significant positive impact in a hospital clinical workflow, where often, there is no other way but to work with faulty/older equipment or inadequately qualified radiology technicians. In this paper, automated image quality improvement approaches for adapted and benchmarked for the task of medical image super-resolution. During experimental evaluation on standard open datasets, the observations showed that certain algorithms perform better and show significant improvement in the diagnostic quality of medical scans, thereby enabling better visualization for human diagnostic purposes.
