Image Reconstruction for Dymanic Contrast-Enhanced MR Imaging of

动态对比增强 MR 成像的图像重建

基本信息

批准号：
8037107
负责人：
JEFFREY A FESSLER
金额：
$ 26.41万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-07-15 至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8037107
关键词：
Accounting Algorithms Biopsy Specimen Blinded Breast Cancer Patient Characteristics Clinical Computer Simulation Data Data Set Diagnosis Disease Drug Formulations Financial compensation Freedom Goals Histopathology Human Image Imaging Techniques Instruction Joints Kinetics Lead Magnetic Resonance Imaging Measures Methods Metric Modeling Modification Morphologic artifacts Morphology Motion Neoadjuvant Therapy Patients Pattern Performance Pilot Projects Property Public Health Radial Reader Research Research Personnel Resolution Rotation Sampling Scanning Series Techniques Time Tracer Translations base breast lesion chemotherapy computerized data processing cost data sharing data space digital human data image reconstruction image registration improved malignant breast neoplasm novel object motion operation reconstruction response simulation treatment response tumor

项目摘要

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.

参见说明）：乳腺癌患者的动态对比增强 (DCE) 磁共振成像 (MRI) 显示在帮助诊断乳腺病变和描述治疗反应方面具有相当大的前景。这 DCE 乳腺成像面临的挑战是需要良好的时间分辨率来捕获示踪剂动力学特性和良好的空间分辨率，用于可视化形态。 MRI 中的传统动态方法获取每个时间点不完整的k空间数据，并使用k空间时间插值（或数据共享）在傅里叶重建之前形成“完整”的 k 空间数据集。我们建议研究基于模型的通过估计对象模型参数来避免 k 空间插值的图像重建方法最适合可用的 k 空间数据。这些重建方法将结合并行成像技术。它们还将被扩展以考虑由于患者运动期间的非刚性变形。使用联合估计运动参数和图像强度参数的新方法进行扫描。这将使用计算机模拟、模型研究和人体 DCE-MRI 扫描数据来评估方法。人体数据将作为项目 1 的一部分进行收集，其中包括乳腺癌的 DCE-MRI 扫描接受新辅助化疗的患者，早期预测肿瘤反应具有临床意义重要性。所提出的方法有可能提高乳腺 DCE-MRI 的图像质量以及其他动态 MR 应用。相关性（参见说明）：这项研究与公共卫生的相关性在于，通过更多措施提高 MR 图像的质量复杂的数据处理可能会给乳腺患者带来更准确的诊断和治疗癌症和其他疾病。