A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis.

Autor: Lee JD; Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA., Richter J; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA., Pfaller MR; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA., Szafron JM; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA., Menon K; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA., Zanoni A; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA., Ma MR; Department of Cardiothoracic Surgery, Stanford University, Stanford, California, USA., Feinstein JA; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA.; Department of Bioengineering, Stanford University, Stanford, California, USA., Kreutzer J; Department of Pediatrics, University of Pittsburgh School of Medicine and UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA., Marsden AL; Department of Pediatrics (Cardiology), Stanford University, Stanford, California, USA.; Department of Bioengineering, Stanford University, Stanford, California, USA.; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA., Schiavazzi DE; Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
Jazyk: angličtina
Zdroj: International journal for numerical methods in biomedical engineering [Int J Numer Method Biomed Eng] 2024 May; Vol. 40 (5), pp. e3820. Date of Electronic Publication: 2024 Mar 27.
DOI: 10.1002/cnm.3820
Abstrakt: The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in data-driven architectures and optimization techniques for fast surrogate modeling provide an exciting opportunity to overcome these limitations, enabling the use of such technology for time-critical decisions. We discuss an application to the repair of multiple stenosis in peripheral pulmonary artery disease through either transcatheter pulmonary artery rehabilitation or surgery, where it is of interest to achieve desired pressures and flows at specific locations in the pulmonary artery tree, while minimizing the risk for the patient. Since different degrees of success can be achieved in practice during treatment, we formulate the problem in probability, and solve it through a sample-based approach. We propose a new offline-online pipeline for probabilistic real-time treatment planning which combines offline assimilation of boundary conditions, model reduction, and training dataset generation with online estimation of marginal probabilities, possibly conditioned on the degree of augmentation observed in already repaired lesions. Moreover, we propose a new approach for the parametrization of arbitrarily shaped vascular repairs through iterative corrections of a zero-dimensional approximant. We demonstrate this pipeline for a diseased model of the pulmonary artery tree available through the Vascular Model Repository.
(© 2024 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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