Image Reconstruction for Dymanic Contrast-Enhanced MR Imaging of

请参阅说明)： 乳腺癌患者的动态增强(DCE)磁共振成像(MRI)显示 在帮助诊断乳腺病变和确定治疗反应方面有相当大的希望。这个 DCE乳腺成像的挑战是既需要良好的时间分辨率来捕获示踪剂动力学 用于可视化形态的属性和良好的空间分辨率。磁共振成像中的传统动态方法 获取每个时间点的不完整k空间数据，并使用k空间时间内插(或数据共享) 以在傅立叶重建之前形成“完整的”k-空间数据集。我们建议研究基于模型的 一种通过估计目标模型参数避免k空间内插的图像重建方法 这最符合可用的k空间数据。这些重建方法将结合平行成像。 技巧。它们还将扩展到考虑由于患者在手术过程中的运动而导致的非刚性变形 使用运动参数和图像强度参数联合估计的新方法进行扫描。这个 这些方法将使用计算机模拟、模体研究和人类DCE-MRI扫描数据进行评估。 人类数据将作为项目1的一部分收集，并将包括乳腺癌的DCE-MRI扫描 接受新辅助化疗的患者，早期预测肿瘤反应的临床意义 重要性。所提出的方法有可能改善乳腺DCE-MRI AS的图像质量 以及其他动态MR应用。 相关性(请参阅说明)： 这项研究与公共卫生的相关性在于，通过更多的 复杂的数据处理可能会导致对乳房患者更准确的诊断和治疗 癌症和其他疾病。

Seeinstructions): Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of breast cancer patients has shown considerable promise in aiding diagnoses of breast lesions and characterizing treatment response. The challenge in DCE breast imaging is the need for both good temporal resolution to capture tracer kinetic properties and good spatial resolution for visualizing morphology. Traditional dynamic methods in MRI acquire incomplete k-space data at each time point, and use k-space temporal interpolation (or data sharing) to form "complete" k-space datasets prior to Fourier reconstruction. We propose to investigate model-based image reconstruction methods that avoid k-space interpolation by estimating the object model parameters that best fit the available k-space data. These reconstruction methods will incorporate parallel imaging techniques. They will also be extended to account for nonrigid deformations due to patient motion during the scan using novel methods for joint estimation of motion parameters and image intensity parameters. The methods will be evaluated using computer simulations, phantom studies, and human DCE-MRI scan data. The human data will be collected as part of Project 1 and will include DCE-MRI scans of breast cancer patients undergoing neoadjuvant chemotherapy, where early prediction of tumor response is of clinical importance. The proposed methods have the potential to improve image quality both in breast DCE-MRI as well as other dynamic MR applications. RELEVANCE (See instructions): The relevance of this research to public health is that improving the quality of MR images through more sophisticated data processing may lead to more accurate diagnosis and treatment of patients with breast cancer and other diseases.