Christierson Lea, Frieberg Petter, Lala Tania, Töger Johannes, Liuba Petru, Revstedt Johan, Isaksson Hanna, Hakacova Nina
Department of Clinical Sciences Lund, Pediatric Heart Center, Skåne University Hospital, Lund University, Lund, Sweden.
Department of Biomedical Engineering, Lund University, Lund, Sweden.
Cardiovasc Eng Technol. 2024 Oct;15(5):572-583. doi: 10.1007/s13239-024-00732-3. Epub 2024 May 23.
Fluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for cardiac-inspired FSI models, based on clinically important parameters, such as the pressure, velocity, and valve opening, with an in vitro phantom setup.
An in vitro setup was developed with a 3D-printed phantom mimicking the left heart, including a deforming mitral valve. A range of pulsatile flows were created with a computer-controlled motor-and-pump setup. Catheter pressure measurements, magnetic resonance imaging (MRI), and echocardiography (Echo) imaging were used to measure pressure and velocity in the domain. Furthermore, the valve opening was quantified based on cine MRI and Echo images.
The experimental setup, with 0.5% cycle-to-cycle variation, was successfully built and six different flow cases were investigated. Higher velocity through the mitral valve was observed for increased cardiac output. The pressure difference across the valve also followed this trend. The flow in the phantom was qualitatively assessed by the velocity profile in the ventricle and by streamlines obtained from 4D phase-contrast MRI.
A multi-modal set of data for validation of FSI models has been created, based on parameters relevant for diagnosis of heart valve disease. All data is publicly available for future development of computational heart valve models.
随着计算能力的提高，流固耦合（FSI）模型在医学研究中的应用越来越普遍。然而，了解FSI模型的准确性至关重要，特别是在针对特定患者的心脏瓣膜病模型的背景下。因此，本研究旨在基于临床上重要的参数，如压力、速度和瓣膜开口，通过体外模型设置，为受心脏启发的FSI模型创建一个多模态基准数据集。
开发了一种体外模型，使用3D打印的模型模拟左心，包括一个可变形的二尖瓣。通过计算机控制的电机和泵装置产生一系列脉动流。使用导管压力测量、磁共振成像（MRI）和超声心动图（Echo）成像来测量该区域内的压力和速度。此外，基于电影MRI和Echo图像对瓣膜开口进行量化。
成功构建了具有0.5%逐周期变化的实验装置，并研究了六种不同的血流情况。观察到二尖瓣处的速度随着心输出量的增加而升高。瓣膜两端的压差也呈现出这种趋势。通过心室中的速度剖面和从4D相位对比MRI获得的流线对模型中的血流进行了定性评估。
基于与心脏瓣膜病诊断相关的参数，创建了一组用于验证FSI模型的多模态数据。所有数据均可公开获取，以供未来计算心脏瓣膜模型的开发使用。
