VARIABLE VOXEL-SIZE INTENSITY CORRECTION

可变体素尺寸强度校正

• 批准号: 8362955
• 负责人: Anderson Nnewihe
• 金额: $ 1.95万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8362955
• 关键词: Algorithms; Annual Reports; Brain imaging; Breast; Cardiac; Data; Data Set; Elements; Exhibits; Funding; Grant; Image; Literature; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Methods; National Center for Research Resources; Noise; Patients; Principal Investigator; Process; Reading; Research; Research Infrastructure; Resolution; Resources; Scanning; Scheme; Source; Surface; Technology; United States National Institutes of Health; Variant; Visit; abstracting; base; cost; density

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. High-density surface coil arrays have been used for attaining highly-accelerated, high resolution images for brain, breast and cardiac MRI studies [1,2]. Due to the layout and size of the coil elements, these arrays usually exhibit sensitivity variations across the field of view. Several intensity correction schemes have been investigated in literature, and many of these algorithms significantly change the spatial noise profile during the process, which is undesirable [3,4]. In this study, we present a variable voxel-size intensity correction method that maintains the original noise profile while reducing intensity variations across the image. The principle idea consists of averaging voxels in regions of low sensitivity and properly scaling the result, thereby boosting the SNR in these regions. We present the results of this variable voxel-size algorithm for an ultrahigh resolution, 3D breast patient scan using a 16-channel surface coil array. We assumed that the coil intensity profile is a slowly varying spatial function and approximated this bias field using a low pass filter on the original data. We normalized this bias field and created a "voxel volume" map where larger voxel sizes correspond to regions of low intensity of the bias field. We passed the original dataset individually through scaled low pass filters of varying bandwidths to achieve the desired resolution in the voxel volume map. In the final corrected dataset, we chose the appropriate dataset for each voxel based on the voxel volume map. To read about other projects ongoing at the Lucas Center, please visit http://rsl.stanford.edu/ (Lucas Annual Report and ISMRM 2011 Abstracts)

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 高密度表面线圈阵列已用于获得大脑、乳房的高加速、高分辨率图像 和心脏 MRI 研究 [1,2]。 由于线圈元件的布局和尺寸，这些阵列通常表现出灵敏度 整个视野的变化。 文献中已经研究了几种强度校正方案，并且许多 这些算法在处理过程中显着改变了空间噪声分布，这是不希望的[3,4]。 在这项研究中， 我们提出了一种可变体素尺寸强度校正方法，该方法可以保持原始噪声轮廓，同时减少 整个图像的强度变化。 其原理思想包括对低灵敏度区域中的体素进行平均并适当缩放结果，从而提高 这些区域的信噪比。 我们展示了这种可变体素大小算法的结果，用于超高分辨率、3D 使用 16 通道表面线圈阵列对患者进行乳房扫描。 我们假设线圈强度分布是缓慢变化的 空间函数并使用原始数据的低通滤波器来近似该偏置场。 我们标准化了这个偏置场 并创建了“体素体积”图，其中较大的体素尺寸对应于偏置场的低强度区域。 我们 将原始数据集分别通过不同带宽的缩放低通滤波器，以达到所需的效果 体素体积图中的分辨率。 在最终校正的数据集中，我们为每个基于体素的数据集选择了适当的数据集 在体素体积图上。 要了解卢卡斯中心正在进行的其他项目，请访问 http://rsl.stanford.edu/（卢卡斯年度报告和 ISMRM 2011 摘要）