Image Reconstruction for Dymanic Contrast-Enhanced MR Imaging of

动态对比增强 MR 成像的图像重建

• 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
• 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

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表征治疗反应方面具有相当大的前景。的 VCE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 性能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 在每个时间点获取不完整的k空间数据，并使用k空间时间插值（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 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。这些重建方法将包括并行成像 技术.它们也将被扩展，以考虑由于患者在手术过程中的运动而引起的非刚性变形。 扫描使用新的方法联合估计运动参数和图像强度参数。的 将使用计算机模拟、体模研究和人体DCE-MRI扫描数据对方法进行评价。 人类数据将作为项目1的一部分收集，包括乳腺癌的DCE-MRI扫描 接受新辅助化疗的患者，其中肿瘤缓解的早期预测具有临床意义 重要性所提出的方法有可能提高乳腺DCE-MRI和 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，通过更多的方法来提高MR图像的质量。 复杂的数据处理可能会导致乳腺癌患者的更准确的诊断和治疗， 癌症和其他疾病。