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.

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