Prospective Slice Tracking for Cardiac MRI

心脏 MRI 的前瞻性切片跟踪

• 批准号: 9762101
• 负责人: Rizwan Ahmad
• 金额: $ 22.37万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762101
• 关键词: Anatomy; Breathing; Cardiac; Cardiovascular system; Clinical; Complex; Coronary Angiography; Data; Dependence; Diagnosis; Evaluation; Financial compensation; Freezing; Gadolinium; Heart Diseases; Image; Imaging Techniques; Lead; Learning; Location; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measures; Methods; Modeling; Morphologic artifacts; Motion; Patients; Perfusion; Positioning Attribute; Protocols documentation; Recovery; Research; Resolution; Respiration; Scanning; Signal Transduction; Slice; Techniques; Technology; Testing; Time; Training; Validation; artificial neural network; base; computerized data processing; data acquisition; healthy volunteer; heart motion; imaging modality; improved; prospective; reconstruction; respiratory; technology development; temporal measurement; volunteer

Project Summary/Abstract Cardiac Magnetic Resonance (CMR) provides arguably the most comprehensive evaluation of the cardiovascular system; however, respiratory motion continues to adversely impact CMR, causing artifacts that lead to poor image quality, repeated scans, and decreased throughput, and thus represents a significant obstacle to clinical utility. For single-shot CMR, cardiac and breathing motions are “frozen” by limiting the acquisition to an end-diastolic window less than 200 ms. For first pass perfusion, breathing motion cannot be eliminated because data from 50 to 60 consecutive heartbeats are required to capture contrast dynamics. For other single-shot applications such as late gadolinium enhancement (LGE) and parameter mapping, respiratory motion is introduced when the acquisition is repeated across several heartbeats to improve spatial and temporal resolution. To eliminate respiratory motion from single-shot images, non-rigid motion correction (MOCO) has been promoted as an attractive option that provides 100% acquisition efficiently. MOCO can be used either after the reconstruction or during the reconstruction. Such techniques, however, cannot account for through-plane motion, which can only be corrected prospectively, and can fail depending on image quality and the extent of motion. Prospective compensation of the respiratory motion has been recognized as an attractive alternative to existing gating and MOCO methods. Proposed methods use one or more navigator echoes—incompatible with or inefficient for many CMR protocols—to capture the respiratory motion and rely on simple parametric models that are inadequate to describe complex respiratory-induced cardiac motion. Due to these limitations, prospective methods have found limited applicability even in research settings. We propose a new framework to prospectively compensate respiratory motion. The proposed method, called PROspective Motion compensation using Pilot Tone (PROMPT), employs Pilot Tone technology and leverages machine learning principles to first learn complex respiratory-induced cardiac motion on a patient-specific basis and then prospectively compensate the motion by tracking the imaging plane, in real time, as a function of a Pilot Tone based respiratory signal. If successful, this synergistic combination of Pilot Tone and machine learning will lead to 100% efficiency for single-shot CMR exams performed under free-breathing conditions, will eliminate the need to setup navigator echoes, respiratory bellows, or other inefficient prospective gating measures, will minimize through-plane motion that can render the images non-diagnostic for CMR applications including fast-pass perfusion, parameter mapping, LGE, and coronary angiography, will provide a reliable surrogate measure of respiratory motion, and will facilitate highly accelerated compressive recovery.

项目总结/摘要 心脏磁共振（CMR）可以提供最全面的评估， 然而，呼吸运动继续对CMR产生不利影响，导致伪影， 导致图像质量差、重复扫描和吞吐量降低，因此代表了显著的 临床应用的障碍。对于单次激发CMR，心脏和呼吸运动通过限制 对于首过灌注，呼吸运动不能 因为需要来自50到60次连续心跳的数据来捕获对比度动态，所以被消除。为 其他单次激发应用如后期钆增强（LGE）和参数映射， 当在几个心跳之间重复采集时引入呼吸运动， 和时间分辨率。为了从单次激发图像中消除呼吸运动，非刚性运动校正 （MOCO）已被推广为一个有吸引力的选择，提供100%的收购效率。MOCO可以是 在重建之后或重建期间使用。然而，这些技术不能解释 通过平面的运动，这只能前瞻性地校正，并且根据图像质量可能失败， 运动的程度。 呼吸运动的前瞻性补偿已经被认为是现有的呼吸运动补偿的有吸引力的替代方案。 门控和MOCO方法。建议的方法使用一个或多个导航器回显-与或不兼容 对于许多CMR协议来说，捕获呼吸运动并依赖于简单的参数模型是低效的 不足以描述复杂的心脏运动。由于这些限制， 即使在研究环境中，前瞻性方法的适用性也有限。 我们提出了一个新的框架，前瞻性地补偿呼吸运动。所提出的方法称为 使用导频音的前瞻性运动补偿（PROMPT），采用导频音技术并利用 机器学习原理，首先在患者特定的基础上学习复杂的心脏运动 然后通过在真实的时间内跟踪成像平面来前瞻性地补偿运动， 基于导频音的呼吸信号。如果成功的话，这种导频音和机器的协同组合 学习将使在自由呼吸条件下进行的单次CMR检查的效率达到100%， 无需设置导航回波、呼吸波纹管或其他低效的前瞻性门控 措施，将最大限度地减少可能使图像无法诊断CMR应用的跨平面运动 包括快速通道灌注、参数标测、LGE和冠状动脉造影，将提供可靠的 呼吸运动的替代测量，并将促进高度加速的压缩恢复。