Improved Breast DCE MRI with SWIFT

使用 SWIFT 改进乳房 DCE MRI

• 批准号: 7672970
• 负责人: Curtis Andrew Corum
• 金额: $ 26.14万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7672970
• 关键词: Acceleration; Acoustics; Address; Area; Benign; Biopsy; Breast; Cancer Patient; Contrast Media; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Evaluation; Failure; Fatty acid glycerol esters; Female; Financial compensation; Fourier Transform; Gadolinium DTPA; Goals; Image; Imaging Techniques; Immune; Immunity; Implant; Kinetics; Lesion; Magnetic Resonance Imaging; Malignant - descriptor; Measurement; Measures; Medical Imaging; Meta-Analysis; Methods; Modeling; Motion; Noise; Pathologic; Patients; Performance; Phase; Physiologic pulse; Population; Predisposition; Process; Property; Protons; Radial; Ramp; Relaxation; Research; Resolution; Sampling; Scanning; Scheme; Series; Specificity; Staging; Testing; Three-Dimensional Image; Time; Tissues; Ursidae Family; Validation; Woman; Work; base; breast cancer diagnosis; breast lesion; digital; experience; imaging modality; improved; magnetic field; malignant breast neoplasm; novel; pharmacokinetic model; prospective; public health relevance; reconstruction; volunteer

DESCRIPTION (provided by applicant): Today's medical imaging methods have insufficient specificity for reliable differentiation between benign and malignant breast lesions in patients. Pathologic evaluation is currently the only way to obtain a definitive diagnosis. This research will use a novel method of magnetic resonance imaging (MRI), Sweep Imaging with Fourier Transform (SWIFT), at very high magnetic field (4 Tesla) to distinguish malignant from benign breast lesions. Specificity will be gained from improved temporal resolution and contrast kinetic parameter extraction measured non-invasively with proton (1H) MRI. In addition to high temporal resolution and immunity to T2* effects at even the highest contrast agent concentrations, SWIFT offers several other improvements. The 3D radial sampling scheme is motion-correctable with simple k-space based processing. Due to the smooth rotating gradient trajectory, SWIFT is immune to gradient group delay, gradient ramp based errors, and produces orders of magnitude fewer eddy currents than other rapid radial sequences. Another extremely desirable effect of the smooth gradient is that SWIFT is extremely quiet, leading to an improved patient experience and fewer failures to scan. Breast lesions will be visualized by dynamic contrast-enhanced three-dimensional MRI and simultaneously processed into high-temporal resolution / low-spatial resolution, high-spatial resolution / low temporal-resolution, or mixed image series. Since data from a single scan can be formatted into variable spatial and temporal resolutions, the total imaging time can be reduced compared with standard MRI scanning methods. SWIFT MRI measurements will be correlated with biopsy results to determine whether this MRI sequence accurately identifies and characterizes malignant lesions in breast patients. This research will reveal whether the SWIFT sequence bears new capabilities in medical imaging for breast cancer diagnosis. PUBLIC HEALTH RELEVANCE: The proposed research is aimed at improving the accuracy of detection and determination of extent of disease in women suspected of having breast cancer. It should also help reduce cases where a woman is told she has breast cancer in error.

描述（由申请人提供）：当今的医学成像方法对于可靠区分患者良性和恶性乳腺病变的特异性不够。病理学评估是目前获得明确诊断的唯一方法。这项研究将使用一种新的磁共振成像（MRI）方法，即傅里叶变换扫描成像（SWIFT），在极高的磁场（4特斯拉）下区分恶性和良性乳腺病变。通过改进时间分辨率和使用质子 (1H) MRI 非侵入性测量的对比动力学参数提取，可以获得特异性。除了高时间分辨率和在最高造影剂浓度下不受 T2* 效应影响之外，SWIFT 还提供了其他几项改进。 3D 径向采样方案可通过简单的基于 k 空间的处理进行运动校正。由于旋转梯度轨迹平滑，SWIFT 不受梯度群延迟、基于梯度斜坡的误差的影响，并且产生的涡流比其他快速径向序列少几个数量级。平滑梯度的另一个极其理想的效果是 SWIFT 非常安静，从而改善患者体验并减少扫描失败。乳腺病变将通过动态对比增强三维 MRI 进行可视化，并同时处理成高时间分辨率/低空间分辨率、高空间分辨率/低时间分辨率或混合图像系列。由于单次扫描的数据可以格式化为可变的空间和时间分辨率，因此与标准 MRI 扫描方法相比，可以减少总成像时间。 SWIFT MRI 测量结果将与活检结果相关联，以确定该 MRI 序列是否准确识别和表征乳腺患者的恶性病变。这项研究将揭示 SWIFT 序列是否具有乳腺癌诊断医学成像的新功能。公共卫生相关性：拟议的研究旨在提高疑似患有乳腺癌的女性的检测和确定疾病程度的准确性。它还应该有助于减少女性被错误告知患有乳腺癌的情况。