Patient-specific prediction of arterial wall elasticity using medical image-informed in-silico simulations.

Manideep Roy¹, Xiaojing Guo², Qingdi Wang³, Daniel Stäb⁴, Ning Jin⁵, Ruth P Lim⁶, Andrew Ooi², 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.

³Department 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.

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

Limitations in clinical cardiovascular research have driven the development of advanced simulations for patient-specific insights into arterial elasticity. However, uncertainties in model inputs, data resolution, and parameter estimation can compromise accuracy. Our research aimed to provide reliable estimates of the arterial wall elasticity non-invasively, where direct clinical measurement is dif…

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