• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >Spatially variable Rician noise in magnetic resonance imaging
【24h】

Spatially variable Rician noise in magnetic resonance imaging

机译：磁共振成像中的空间可变Rician噪声

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Magnetic resonance images tend to be influenced by various random factors usually referred to as " noise" The principal sources of noise and related artefacts can be divided into two types: arising from hardware (acquisition coil arrays, gradient coils, field inhomogeneity); and arising from the subject (physiological noise including body motion, cardiac pulsation or respiratory motion). These factors negatively affect the resolution and reproducibility of the images. Therefore, a proper noise treatment is important for improving the performance of clinical and research investigations. Noise reduction becomes especially critical for the images with a low signal-to-noise ratio, such as those typically acquired in diffusion tensor imaging at high diffusion weightings. The standard methods of signal correction usually assume a uniform distribution of the standard deviation of the noise across the image and evaluate a single correction parameter for the whole image. We pursue a more advanced approach based on the assumption of an inhomogeneous distribution of noise in space and evaluate correction factors for each voxel individually. The Rician nature of the underlying noise is considered for low and high signal-to-noise ratios. The approach developed here has been examined using numerical simulations and in vivo brain diffusion tensor imaging experiments. The efficacy and usefulness of this approach is demonstrated here and the resultant effective tool is described.
机译：磁共振图像容易受到通常称为“噪声”的各种随机因素的影响。噪声和相关伪像的主要来源可以分为两种类型：由硬件引起的（采集线圈阵列，梯度线圈，磁场不均匀性）;并由受试者引起（生理噪声，包括身体运动，心脏搏动或呼吸运动）。这些因素会对图像的分辨率和再现性产生负面影响。因此，适当的噪声处理对于提高临床和研究调查的性能很重要。对于具有低信噪比的图像，例如在高扩散权重下的扩散张量成像中通常获取的图像，降噪变得尤为关键。信号校正的标准方法通常假设噪声标准偏差在整个图像上的均匀分布，并评估整个图像的单个校正参数。我们基于空间中噪声不均匀分布的假设，追求更高级的方法，并分别评估每个体素的校正因子。对于低信噪比和高信噪比，都考虑了基础噪声的Rician性质。已使用数值模拟和体内脑扩散张量成像实验检查了此处开发的方法。这里展示了这种方法的有效性和实用性，并描述了所得的有效工具。

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