• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Scientific reports. >Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches
【24h】

Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches

机译：基于深度学习的图像质量增强血管壁压缩传感磁共振成像：自我监督和无监督方法比较

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms—self-supervised learning and unsupervised learning—are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.
机译：虽然颅内血管壁的高分辨率质子密度加权磁共振成像（MRI）对于精确诊断颅内动脉疾病的精确诊断，但其长的采购时间是临床负担。压缩传感MRI是一种预期技术，具有可能降低扫描时间的加速因素。然而，高加速度因素导致图像质量降级。尽管基于深度学习的图像恢复算法的最近进步可以缓解这个问题，但是深度学习训练中使用的临床图像对通常不会对准像素方面。因此，在这项研究中，提出了两个不同的深度学习的去噪算法 - 自我监督的学习和无监督的学习 - 被提出;这些算法适用于未对准像素的临床数据集。定量和定性比较这两种方法。两种方法在图像去噪和视觉分级方面产生了有希望的结果。虽然自我监督学习的图像噪声和信噪比优于无监督的学习，但在辐射特征再现性方面，优于自我监督学习的无监督学习。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号