A Bayesian Model for MRI-based Accelerated 4D Flow Imaging of Aortic Valve Stenosis

 描述(申请人提供)：每年有500多万美国人被诊断出患有心脏瓣膜疾病。在瓣膜疾病中，最常见的是主动脉瓣狭窄(AVS)。随着人口老龄化，AVS的患病率预计将上升。介入治疗的时机在很大程度上取决于狭窄的严重程度和症状的存在。因此，在做出介入治疗的临床决策时，准确评估狭窄的功能严重程度是很重要的。目前，最常见的无创评估AVS严重程度的方法是经胸多普勒超声心动图(TTE)。然而，许多患者可能由于声窗不佳、主动脉瓣严重钙化或血流明显加速而对超声心动图的评估不佳。 左心室流出道，这可能会模糊对主动脉瓣的评估。对于这类患者，基于MRI的2D血流成像(MRI-2DF)不受声学窗的影响，提供了一种可行的替代方案。然而，MRI-2DF方法只对速度向量的一个方向分量敏感；因此，速度编码方向相对于血流方向的任何错误对齐都会导致对血流和速度的低估，进而可能导致对疾病严重程度的错误分类。基于PC-MRI的4D血流成像(MRI-4DF)克服了TTE和MRI-2DF的缺点，以其体积空间覆盖率和对血流速度的全方位编码能力，改善了AVS严重程度的评估。然而，MRI-4DF的前景因过长的扫描时间而落空，扫描时间可能超过30分钟。尽管最近在利用平行成像、非笛卡尔轨迹和压缩传感(CS)启发的图像恢复方面做出了努力，但MRI-4DF仍然是一种需要进一步开发以找到临床应用的研究工具。这项工作的目标是在一小部分AVS患者中启用并证明单次屏气MRI-4DF的可行性。在具体的目标1中，我们提出了一种新的技术，称为基于近似消息传递算法的重建速度编码MRI(REPLAGE)，以将MRI-4DF的捕获时间减少到单次屏气。与利用底层图像稀疏性的现有CS技术不同，Display直接对PC-MRI数据中固有的强物理关系进行建模。特别是，所提出的贝叶斯方法利用了几种速度编码之间在幅度和相位上的关系。为了解决由此产生的贝叶斯推理问题，提出了一种基于因子图上消息传递的迭代图像恢复方法，得到了一种自动调整所有自由参数的快速算法。在具体目标2中，我们将使用来自机械流动体模和30名AVS患者的MRI-4DF数据来验证所提出的方法。初步结果表明，REVIEW可以将MRI-2DF加速12倍；由于增加了冗余，MRI-4DF有望获得更高的加速。这一发展将导致对心脏瓣膜疾病的更准确的描述，而不是现有的临床方法。

 DESCRIPTION (provided by applicant): Each year, more than 5 million Americans are diagnosed with cardiac valve disease. Among the valvular diseases, aortic valve stenosis (AVS) is the most common. With the aging population, the prevalence of AVS is expected to rise. Timing of intervention for AVS is largely based on the severity of stenosis and the presence of symptoms. Therefore, accurate assessment of the functional severity of stenosis is important when making clinical decisions regarding intervention. Currently, the most common non-invasive method for the assessment of AVS severity is Transthoracic Doppler echocardiography (TTE). However, many patients may have suboptimal evaluation with echocardiography due to poor acoustic windows, heavy calcification of the aortic valve, or significant flow acceleration in the left ventricular outflow tract which may obscure assessment of the aortic valve. For such patients, MRI-based 2D flow imaging (MRI-2DF), which is not impacted by acoustic windows, provides a viable alternative. MRI-2DF methods, however, are only sensitive to one directional component of the velocity vector; therefore, any misalignment of the velocity encoding direction with respect to the blood flow direction results in underestimation of the flow and velocity and, i turn, potential misclassification of disease severity. PC-MRI-based 4D flow imaging (MRI-4DF), with its volumetric spatial coverage and ability to encode all directions of the velocity, circumvents the shortcoming associated with TTE and MRI-2DF and thus can improve evaluation of AVS severity. The promise of MRI-4DF, however, is undone by prohibitively long scan times, which can be over 30 min. Despite recent efforts in utilizing parallel imaging, non-Cartesian trajectories, and compressive sensing (CS) inspired image recovery, MRI-4DF remains a research tool that is in need of further development to find clinical application. The goal of this work is to enable and demonstrate the feasibility of single breath-hold MRI-4DF in a small cohort of patients with AVS. In Specific Aim 1, we propose a novel technique, called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL), to reduce the acquisition time for MRI-4DF to a single breath-hold. In contrast to the existing CS techniques that utilize the underlying image sparsity, ReVEAL directly models the strong physical relationships inherent in the PC-MRI data. In particular, the proposed Bayesian approach capitalizes on the relationships in both magnitude and phase among the several velocity encodings. To solve the resulting Bayesian inference problem, an iterative image recovery method using message passing on a factor graph is proposed, yielding a fast algorithm with auto-tuning of all free parameters. In Specific Aim 2, we will use MRI-4DF data from a mechanical flow phantom and thirty AVS patients to validate the proposed approach. Preliminary results show that ReVEAL can accelerate MRI-2DF by a factor of 12; higher accelerations are expected for MRI-4DF due to added redundancy. This development will lead to more accurate characterization of cardiac valve disease than is possible with existing clinical methods.