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

 描述（由申请人提供）：每年，超过500万美国人被诊断患有心脏瓣膜疾病。在瓣膜疾病中，主动脉瓣狭窄（AVS）是最常见的。随着人口老龄化，预计AVS的患病率将上升。AVS的介入时机主要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非常重要。目前，用于评估AVS严重程度的最常见的非侵入性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学窗口差、主动脉瓣严重钙化或主动脉瓣内显著的血流加速而使用超声心动图进行次优评价。 左心室流出道可能会模糊主动脉瓣的评估。对于此类患者，基于MRI的2D血流成像（MRI-2DF）不受声窗影响，提供了一种可行的替代方案。然而，MRI-2DF方法仅对速度矢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未对准都会导致对流量和速度的低估，进而导致对疾病严重程度的潜在错误分类。基于PC-MRI的4D血流成像（MRI-4DF）具有体积空间覆盖范围和编码所有方向速度的能力，避免了TTE和MRI-2DF相关的缺点，因此可以改善对AVS严重程度的评估。然而，MRI-4DF的承诺是由过长的扫描时间，这可能是超过30分钟，尽管最近的努力，利用并行成像，非笛卡尔轨迹，压缩感知（CS）启发的图像恢复，MRI-4DF仍然是一个研究工具，需要进一步发展，找到临床应用。这项工作的目标是在一小群患有AVS的患者中启用并证明单次屏气MRI-4DF的可行性。在特定目标1中，我们提出了一种新的技术，称为重建速度编码MRI与近似消息传递aLtaxms（ReVEAL），以减少采集时间MRI-4DF到一个单一的屏气。与利用底层图像稀疏性的现有CS技术相比，ReVEAL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个速度编码之间的幅度和相位的关系。为了解决由此产生的贝叶斯推理问题，提出了一种迭代的图像恢复方法，使用因子图上的消息传递，产生一个快速的算法与自动调整的所有自由参数。在特定目标2中，我们将使用来自机械流体模和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，ReVEAL可以将MRI-2DF加速12倍;由于增加了冗余，预计MRI-4DF会有更高的加速。这一发展将导致比现有临床方法更准确的心脏瓣膜疾病的表征。

 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.