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Restoration for RF Induced Spatial Intensity Non-uniformities in MRI Data with Statistical, Structural, and Physical Constraints

具有统计、结构和物理约束的 MRI 数据中射频引起的空间强度不均匀性的恢复

基本信息

批准号：
237079734
负责人：
Professor Dr. Michael Bock, since 3/2014
金额：
--
依托单位：
Deutsches Krebsforschungszentrum (DKFZ)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/237079734?language=en
关键词：
Restoration RF Induced Spatial Intensity

项目摘要

MRI reconstruction assumes uniform Radio-Frequency (RF) fields. Modern MRI systems generate the RF fields with integrated coil arrays that give rise to higher resolution as well as higher Signal to Noise Ratio (SNR). However, at the same time they give rise to an inhomogeneous RF field that leads to intensity non-uniformities across an image. Non-uniformity artifacts are also more pronounced in high field MRI and complicate reconstruction, confound tissue properties, compromise diagnostic quality, and any further automated image analysis. This proposal intends to address this problem with the development of methods for the estimation of primarily the receive non-uniform sensitivity of a single coil as well as of multiple coils in phased arrays. It will remove non-uniformity artifacts in single contrast images as well as jointly for images of multiple contrasts. The effect of this artifact on the statistics of intensity co-occurrences as well as on cross-co-occurrences between images will be removed non-parametrically. The statistics will be restored both with Wiener filtering as well as together with sparsity constraints. Constraints from statistics of intensity differences in space will also be considered both parametrically as well as non-parametrically. The single contrast parametric constraint will be in terms of the total variation. The multi-contrast parametric constraint will be in terms of the Laplace-Beltrami operator. The non-parametric constraint based on intensity differences will be expressed with general image features and will be performed with Wiener filtering. The restored statistics will be forced to the imaging data. The accuracy of the statistical restoration will also consider the valid signal regions of the images and partial volume artifacts. The restoration will be constrained with RF non-uniformity field mappings that will be obtained during the acquisition phase. The non-uniformity will be assumed to be smooth in space.The combinations of the various constraints including the one from the RF field map will be validated extensively. They will also be compared with other post-acquisition restoration methods. The validation will be performed with data that includes the BrainWeb 1,5 T phantom as well as brain anatomic data acquired at 3,0 T and 7,0 T at various MRI systems. The brain data will be from healthy volunteers as well as from patients suffering from multiple sclerosis and Alzheimers. Validation will also be performed with 3,0 T chemical shift imaging for fat/water reconstruction with data obtained from both a physical phantom and from obese volunteers over an extensive body region. The numerical implementation of the restoration will be iterative and will be expedited with a parallel implementation.

MRI重建采用均匀射频（RF）场。现代MRI系统通过集成线圈阵列产生射频场，从而产生更高的分辨率和更高的信噪比（SNR）。然而，与此同时，它们会产生不均匀的射频场，从而导致图像上的强度不均匀。在高场MRI和复杂的重建中，非均匀性伪影也更加明显，混淆组织特性，降低诊断质量，以及任何进一步的自动化图像分析。本提案旨在通过发展相控阵中单线圈和多线圈的接收非均匀灵敏度估计方法来解决这一问题。它可以去除单对比度图像中的非均匀性伪影，也可以去除多对比度图像中的非均匀性伪影。该伪影对强度共现统计的影响以及图像之间的交叉共现的影响将被非参数地去除。统计数据将通过维纳滤波和稀疏性约束恢复。来自空间强度差异统计的约束也将被参数化和非参数化地考虑。单一对比参数约束将是根据总变化。多对比参数约束将根据拉普拉斯-贝尔特拉米算子。基于强度差异的非参数约束将用一般图像特征表示，并使用维纳滤波进行处理。恢复的统计信息将被强制到成像数据中。统计恢复的准确性还需要考虑图像的有效信号区域和部分体积伪影。恢复将受到在采集阶段获得的射频非均匀场映射的约束。假定非均匀性在空间上是光滑的。各种约束的组合，包括来自射频场图的约束，将得到广泛的验证。它们还将与其他采集后修复方法进行比较。验证将使用的数据包括BrainWeb 1,5 T幻像以及在各种MRI系统中以3,0 T和7,0 T获得的脑解剖数据。大脑数据将来自健康志愿者以及患有多发性硬化症和阿尔茨海默症的患者。验证还将使用3000 T化学位移成像进行脂肪/水重建，数据来自物理幻影和肥胖志愿者的广泛身体区域。恢复的数值实现将是迭代的，并将加速与并行实现。