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: 8445394
• 负责人: JEFFREY A FESSLER
• 金额: $ 20.91万
• 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 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8445394
• 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; Malignant Neoplasms; 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; radiologist; 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.

see Instructions）： 乳腺癌患者的动态对比增强（DCE）磁共振成像（MRI）已显示 有助于诊断乳房病变并表征治疗反应的巨大希望。这 DCE乳房成像中的挑战是既需要良好的时间分辨率捕获示踪剂动力学 特性和良好的空间分辨率可视化形态。 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） 在傅立叶重建之前，要形成“完整”的K空间数据集。我们建议研究基于模型的 图像重建方法通过估计对象模型参数来避免K空间插值 最适合可用的K空间数据。这些重建方法将包含并行成像 技术。它们还将扩展以考虑由于患者运动在 使用新方法进行运动参数和图像强度参数的联合估计。这 将使用计算机模拟，幻影研究和人DCE-MRI扫描数据评估方法。 人类数据将作为项目1的一部分收集，并包括DCE-MRI乳腺癌扫描 接受新辅助化疗的患者，肿瘤反应的早期预测是临床的 重要性。所提出的方法具有提高乳腺DCE-MRI中图像质量的潜力 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是，通过更多 复杂的数据处理可能会导致更准确的诊断和治疗乳房患者 癌症和其他疾病。