Enhancing the predictive capability of magnetic resonance imaging using medical data-supervised cardiovascular flow simulations: A case study for analyzing patient-specific flow in the human aorta.

Manideep Roy¹, Qingdi Wang², Xiaojing Guo³, Daniel Stäb⁴, Ning Jin⁵, Ruth P Lim⁶, Andrew Ooi³, Suman Chakraborty⁷

¹School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.

²Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia.

³Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia.

⁴MR Research Collaborations, Siemens Healthcare Pty Limited, Melbourne, VIC, 3153, Australia.

⁵Siemens Medical Solutions Inc. Malvern, PA, 19355, USA.

⁶Departments of Radiology and Surgery, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, 3010, Australia; Department of Radiology, Austin Health, Heidelberg, VIC, 3084, Australia.

⁷School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India. Electronic address: suman@mech.iitkgp.ac.in.

PubMed DOI

Abstract

BACKGROUND: Detailed hemodynamic parameters are essential for managing cardiovascular diseases, as they reveal blood flow dynamics that affect disease progression and treatment. However, even such advanced techniques as 4D Phase Contrast MRI face challenges in providing accurate, high-resolution data due to limitations in spatial and temporal resolution and image artifacts. Computational Fluid Dyn…

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