BAYESIAN IMAGE RECONSTRUCTION FROM REDUCED K-SPACE DATA,

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者的研究机构。 该项目的总体目标是开发和评估一种新的贝叶斯重建方法，用于低分辨率MRI模态，减少伪影并有效提高相对于标准离散傅立叶变换方法的分辨率。新的重建方法充分利用k-空间数据，以减少伪影和分辨率的增加是通过将高分辨率的信息，从分段的结构MRI扫描在同一个扫描会话中获得。资源研究应用程序中的工作重点将是直接应用，扩展和验证贝叶斯重建方法。灌注加权MR成像（PWI）将是选择用于应用的特定模式。然而，该方法将直接应用于广泛的MR模式，如磁共振波谱成像（MRSI），扩散张量成像（DTI）和功能磁共振成像（fMRI）。 目的1：将贝叶斯低分辨率重建算法应用于PWI。 低分辨率贝叶斯重建算法已被开发为用于重建低分辨率MRI模态的通用程序。该算法将适用于PWI数据集，其中k空间数据已与相应的结构MRI一起沿着。贝叶斯模型将应用于描述标记和未标记灌注扫描之间变化的数据，即标记和未标记条件的复杂差异。 目的2：验证贝叶斯算法相对于标准DFT重建。 将对模拟和真实的数据进行确认。模拟数据将基于物理和生物学知识尽可能准确地模拟PWI。将以比标准采集更高的分辨率采集真实的数据。目前，在实验室中实施的体积PWI序列需要34秒来采集。通过在采集核心中开发的改进的采集序列，沿着在重建核心中的其他地方描述的并行成像的开发，固有PWI分辨率将增加两倍，该相对高分辨率的数据然后可以被用作黄金标准，其将被下采样以给出低分辨率数据。分辨率数据通过切割出k空间的中心区域。 目的3：研究贝叶斯重建方法对误配准和分割错误的鲁棒性。 结构和灌注MRI之间的配准误差以及分割误差都是贝叶斯重建算法的潜在混杂因素。由于这些误差传播的性质在很大程度上是未知的，因此将在存在这些误差的情况下检查贝叶斯重建，并将基于总误差的度量（如均方根误差）进行评估。将对模拟和真实的数据进行测试。 目的4：将贝叶斯重建方法应用于完整的临床研究。 贝叶斯重建算法以及DFT将应用于一小组受试者，目的是比较病理条件下的灌注，例如创伤后应激障碍（PTSD）患者，相对于健康对照，这些患者可能存在某些脑区的失活。将根据每种重建方法的数据进行统计分析，以确定组间差异。这将允许定量评估通过贝叶斯算法方法重建的数据是否比通过DFT重建的数据在表征灌注变化方面提供更高的灵敏度和特异性。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The overall goal of this project is to develop and evaluate a new Bayesian reconstruction method for low-resolution MRI modalities that reduce artifacts and effectively increase resolution relative to standard Discrete Fourier Transform approaches. The new reconstruction method fully utilizes k-space data to reduce artifacts and the increase in resolution is achieved by incorporating high-resolution information from segmented structural MRI scans acquired in the same scanning session. The focus of the work within the Resource Research application will be to directly apply, extend and validate the Bayesian reconstruction methodology. Perfusion-Weighted MR Imaging (PWI) will be the particular modality chosen for application. However, the methodology will have direct application to a wide range of MR modalities such as magnetic resonance spectroscopic imaging (MRSI), diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Aim 1: To apply the Bayesian low-resolution reconstruction algorithm to PWI. The low-resolution Bayesian reconstruction algorithm has been developed as a general procedure for reconstructing low-resolution MRI modalities. The algorithm will be adapted and applied to PWI datasets for which k-space data has been saved along with corresponding structural MRIs. The Bayesian model will be applied to data describing the change between tagged and untagged perfusion scans, i.e. the complex difference of the tagged and untagged conditions. Aim 2: To validate the Bayesian algorithm relative to standard DFT reconstruction. Validation will be performed on both simulated and real data. The simulated data will mimic PWI as accurately as possible based on physical and biological knowledge. The real data will be acquired at higher-resolution than standard acquisition. Currently the volumetric PWI sequence implemented in the laboratory takes 34s to acquire. With improved acquisition sequences to be developed in the acquisition core, along with developments for parallel imaging described elsewhere in the reconstruction core, the inherent PWI resolution will be increased by a factor of two, i.e. to approximately 2x2x2 mm. This relatively high-resolution data can then be utilized as a gold standard that will be down-sampled to give low-resolution data by cutting out the central region of k-space. Aim 3: To study the robustness of the Bayesian reconstruction method to miss-registration and segmentation error. Co-registration error between the structural and perfusion MRIs, and segmentation error, are both potential confounding factors for the Bayesian reconstruction algorithm. Since the nature of the propagation of these errors is largely unknown, the Bayesian reconstructions will be examined in the presence of these errors and will be assessed based on metrics of overall error such as root mean square error. Both simulated and real data will be tested. Aim 4: To apply the Bayesian reconstruction methodology to a full clinical study. The Bayesian reconstruction algorithm as well as DFT will be applied to a small set of subjects with the objective of comparing perfusion in pathological conditions, e.g. Post traumatic Stress Disorder (PTSD) patients, who may present deactivation of certain brain regions relative to healthy controls. Statistical analysis to determine group differences will be performed based on the data from each reconstruction method. This will allow a quantitative assessment as to whether data reconstructed by the Bayesian algorithm method provides greater sensitivity and specificity in characterizing perfusion changes than does data reconstructed via DFT.