Deep Learning 4D flow MR Pipeline for the Automated Assessment of Aortic Hemodynamics in BAV

About This Grant

Project Summary Bicuspid aortic valve (BAV) is the most common congenital heart defect, and it predisposes patients to complications such as aortic stenosis (AS, most common cause) and aortic aneurysm. BAV patients can develop highly divergent outcomes (e.g., rapid progressive aortic dilatation vs. no long-term complications) but the underlying mechanisms that determine the individual risk for complications are not well understood. There is growing evidence that BAV-based changes in aortic hemodynamics are drivers of aortic wall remodeling and subsequent aortic dilation. A number of studies by our group and others have shown that 4D flow MRI can measure altered aortic 3D hemodynamics in-vivo and has potential to provide better assessments of risk for aortic dilatation in BAV patients. However, current implementations of 4D flow MRI are hampered by long acquisition times (8-15 minutes) and cumbersome manual processing, such as eddy current corrections, noise masking, and 3D segmentations. The goal of this project is to develop an deep learning-based acquisition, image reconstruction, and analyses pipeline for efficient and highly accelerated aortic 4D flow MRI. The first aim of this proposal is development and validation of a highly accelerated 2-point velocity encoding 4D flow MRI with deep learning reconstruction. This will allow enable a 4D flow sequences with low scan times (<2 mins) without sacrificing image quality or hemodynamic accuracy. The second aim will be the development of a deep learning-based automated processing pipeline that will enable rapid processing of aortic hemodynamics and calculation of the wall shear stress dynamics and relative area changes. In the third aim, 20 BAV patients and 20 healthy controls will be recruited from the patient population at Northwestern, then imaged and analyzed using the new protocol. This will demonstrate the utility of using the highly optimized method for the acquisition and analysis of 4D flow MR in a clinical setting. Clinical collaborators will help guide the project to fulfil the ultimate goal of improving clinical imaging and analysis of these complex patients. And Siemen support will help with pulse sequence development and the direct integration of our deep learning network on the scanner, so that this project can be easily integrated in clinical workflows.