Prospective Slice Tracking for Cardiac MRI

项目摘要/摘要 心脏磁共振(CMR)可以提供最全面的评估 心血管系统；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，因此对 临床应用的障碍。对于单次激发的CMR，心脏和呼吸运动通过限制 采集到舒张期末期窗小于200毫秒。对于第一次通过灌流，呼吸运动不能 由于需要50到60次连续心跳的数据来捕捉对比度动态，因此消除了这一过程。为 诸如晚期Gd增强(LGE)和参数映射的其他单次激发应用， 当在几次心跳中重复采集时，会引入呼吸运动，以改善空间 和时间分辨率。为了消除单次拍摄图像中的呼吸运动，非刚性运动校正 (MOCO)已被宣传为一个有吸引力的选择，可以100%高效地进行收购。MOCO可以是 在重建之后或重建期间使用。然而，这种技术不能解释 穿透平面运动，只能前瞻性地校正，并且可能失败，具体取决于图像质量和 运动的程度。 预期的呼吸运动补偿已被认为是现有呼吸运动的一种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，效率低下-无法捕获呼吸运动并依赖简单的参数模型 这不足以描述复杂的呼吸诱导的心脏运动。由于这些限制， 即使在研究环境中，预期的方法也发现适用性有限。 我们提出了一个新的框架来前瞻性地补偿呼吸运动。建议的方法称为 使用引导音(PROMPT)的预期运动补偿，采用引导音技术并利用 机器学习原理首先根据患者的具体情况学习复杂的呼吸诱发心脏运动 然后通过实时跟踪成像平面来预期地补偿该运动 基于导频音的呼吸信号。如果成功，这种引导音和机器的协同组合 对于在自由呼吸条件下进行的单次CMR检查，学习将导致100%的效率 无需设置导航器回声、呼吸波纹管或其他低效的预期门控 测量，将最大限度地减少可能使图像对CMR应用程序具有非诊断性的穿透平面运动 包括快速通过灌注、参数标测、LGE和冠状动脉造影术，将提供可靠的 替代呼吸运动测量，并将促进高度加速的压缩恢复。

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.