4D Flow MRI in Assessment of True Severe Low-Gradient Aortic Stenosis

4D Flow MRI 评估真正的严重低梯度主动脉瓣狭窄

• 批准号: 10735953
• 负责人: AMIR A AMINI
• 金额: $ 23.48万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735953
• 关键词: 3-Dimensional; 4D MRI; Adopted; Affect; Anatomy; Aortic Diseases; Aortic Valve Stenosis; Area; Breathing; Catheterization; Cessation of life; Chest; Chronic; Classification; Clinical; Correlation Studies; Data; Diagnostic; Diagnostic Procedure; Dobutamine; Dobutamine Stress Echocardiography; Doppler Echocardiography; Drops; Echocardiography; Enrollment; Equation; Goals; Heart; Heart Valve Diseases; Heart failure; Image; Learning; Left Ventricular Ejection Fraction; Liquid substance; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Modality; Morphologic artifacts; Nature; Noise; Operative Surgical Procedures; Patients; Protocols documentation; Rest; Risk; Safety; Scanning; Severities; Severity of illness; Stratification; Stress; Subgroup; Survival Rate; Symptoms; Syncope; Therapeutic Embolization; Training; Work; adverse outcome; aging population; anatomic imaging; aortic valve; aortic valve disorder; aortic valve replacement; data space; deep learning; hemodynamics; human subject; imaging modality; improved; pressure; respiratory; simulation; tomography; ultrasound

Degenerative aortic stenosis is a progressive valvular heart disease affecting the aging population and when hemodynamically significant is associated with serious adverse outcomes, such as heart failure, syncope, and death. Although, symptomatic classical severe aortic stenosis (AS), wherein aortic valve anatomical or effective area is ≤ 1.0 𝑐𝑚2 and mean gradient ≥ 40 mmHg raises no diagnostic dilemma, patients with low gradient aortic stenosis (i.e., mean gradient < 40 mmHg) that may be severe (i.e., aortic valve area ≤ 1.0 𝑐𝑚2) remain a challenge for determining the true severity of aortic stenosis and present a significant unmet clinical need. The discordance between aortic valve area of ≤ 1.0 𝑐𝑚2 and mean gradient < 40 mmHg is encountered in as many as 30 to 40% of patients with aortic valve area ≤ 1.0 𝑐𝑚2 by transthoracic echocardiography (TTE). In patients with these discordant findings, the true severity is uncertain and have led to the use of dobutamine stress echocardiography (DSE). Based on DSE, one can classify the potentially severe low-gradient subjects into 4 distinct subgroups a) Low gradient severe aortic stenosis (LGS): aortic valve area of ≤ 1.0 𝑐𝑚2 and a mean gradient of ≥ 40 mmHg with DSE. b) Low gradient pseudo-severe aortic stenosis (LGPS): aortic valve area of > 1.0 𝑐𝑚2 and a mean gradient of < 40 mmHg with DSE. c) Low gradient indeterminate aortic stenosis severity (LGI): aortic valve area of ≤ 1.0 𝑐𝑚2 and a mean gradient of < 40 mmHg with DSE. d) Finally, aortic valve area of > 1.0 𝑐𝑚2 and a mean gradient of ≥ 40 mmHg with DSE are classified as moderate or less severe aortic stenosis (LSA). Nearly one-third of patients classified as LGS are classified as LGPS with DSE (6). Further, compounding the concern in patients classified as LGPS, LGI, LSA is that they often manifest with symptoms potentially attributable to severe AS suggesting an initial misclassification perhaps due to measurement errors. Compared to echo, with MRI, the velocities can be measured in all 3D directions and due to its tomographic nature, no geometric assumptions are required. The hypothesis of the study is that our planned CMR methods which we will develop based on previous work will be able to better stratify these AS subjects. Our specific aims are: 1) We will develop efficient rest and dobutamine stress 4D Spiral flow imaging protocols based on k-space dependent respiratory gating. 2) We have recently developed a Deep Learning framework which based on Computational Fluid Dynamics (CFD) simulations of training data learns to directly map velocities to pressures. This will be adapted to measure the transvalvular pressure gradients (TVPG) in human subjects and validated. 3) In 40 subjects with potentially severe low gradient AS (10 in each of the LGS, LGPS, LGI, LSA), a TTE study, and a CMR study will be performed both at rest and under dobutamine stress. To validate, TVPG will be measured with cath in a group with n=10 of moderate AS (MAS) subjects undergoing cath for other purposes. This group will also undergo TTE and CMR studies. Subjects with severe AS were not selected for cath for safety reasons. Classification across modalities, flow, velocity, pressure, and orifice area will be statistically correlated.

退行性主动脉瓣狭窄是一种进行性心脏瓣膜病，影响老龄人口， 血流动力学显著与严重不良结局相关，如心力衰竭、晕厥和 死亡虽然，有症状的经典严重主动脉瓣狭窄（AS），其中主动脉瓣解剖或有效 面积≤ 1.0 mmHg 2且平均压差≥ 40 mmHg不会引起诊断困难，低压差主动脉瓣患者 狭窄（即，平均压差< 40 mmHg）可能是严重的（即，主动脉瓣面积≤ 1.0 mm 2）保持a 确定主动脉瓣狭窄的真实严重程度是一个挑战，并且存在显著未满足的临床需求。的 主动脉瓣面积≤ 1.0 mmHg 2和平均跨瓣压差< 40 mmHg之间的不一致性在许多 经胸超声心动图（TTE）主动脉瓣面积≤ 1.0μ m/2者占30 ~ 40%。患者 由于这些不一致的发现，真正的严重性是不确定的，并导致使用多巴酚丁胺应激 超声心动图（DSE）。基于DSE，可以将潜在严重的低梯度受试者分为4类 不同亚组a）低梯度重度主动脉瓣狭窄（LGS）：主动脉瓣面积≤ 1.0 mm 2，平均 DSE梯度≥ 40 mmHg。B）低梯度假性重度主动脉瓣狭窄（LGPS）：主动脉瓣面积> 1.0𝑐𝑚DSE的平均压差< 40 mmHg。c）低梯度不确定的主动脉瓣狭窄严重程度 (LGI)：DSE显示主动脉瓣面积≤ 1.0 mmHg 2，平均跨瓣压差< 40 mmHg。d）最后，主动脉瓣面积 > 1.0 mmHg 2且平均压差≥ 40 mmHg且DSE被归类为中度或不太严重的主动脉瓣 狭窄（LSA）。近三分之一的LGS患者被归类为LGPS伴DSE（6）。此外，本发明还 加重LGPS、LGI、LSA患者的担忧是，他们经常表现出症状， 可能归因于严重的AS，表明可能由于测量误差导致的初始错误分类。 与回波相比，利用MRI，可以在所有3D方向上测量速度，并且由于其断层扫描， 自然，不需要几何假设。这项研究的假设是，我们计划的CMR方法 我们将在以前工作的基础上开发能够更好地分层这些AS主题。我们的具体目标 主要有：1）我们将开发基于k空间的高效静息和多巴酚丁胺负荷4D螺旋血流成像方案 依赖性呼吸门控2)我们最近开发了一个深度学习框架， 训练数据的计算流体动力学（CFD）模拟学习直接将速度映射到压力。 这将适用于测量人类受试者的跨瓣压差（TVPG）并得到确认。 3)在40例潜在重度低梯度AS受试者中（LGS、LGPS、LGI、LSA各10例），一项TTE研究， 并且CMR研究将在静息和多巴酚丁胺应激下进行。为了验证，TVPG将 在n=10名因其他目的接受导管插入术的中度AS（MAS）受试者中进行导管插入术测量。 该组还将接受TTE和CMR研究。出于安全性考虑，未选择重度AS受试者进行导管插入术 原因不同模态、流量、流速、压力和瓣口面积的分类将具有统计学相关性。

项目成果

4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI.

• DOI: 10.1002/mrm.29791
• 发表时间: 2023-11
• 期刊: MAGNETIC RESONANCE IN MEDICINE
• 影响因子: 3.3
• 作者: Nath, Ruponti;Kazemi, Amirkhosro;Callahan, Sean;Stoddard, Marcus F.;Amini, Amir A.
• 通讯作者: Amini, Amir A.