Key result
Time-series Gaussian process emulator with Kronecker decomposition cuts training iteration time ~99% vs traditional GP.
Why the study?
Inferring patient-specific myocardial stiffness involves substantial methodological challenges and high computational costs that prevent real-time clinical decision support.
A novel time-series Gaussian process emulator using Kronecker product decomposition significantly reduces the computational cost of inferring patient-specific myocardial stiffness from MRI data, enabling potential real-time clinical decision support.
May enable real-time myocardial stiffness assessment from MRI; leaves open prospective clinical validation before workflow adoption.
Cardiac mechanics modelling promises to revolutionize personalized health care; however, inferring patient-specific biophysical parameters, which are critical for understanding myocardial functions and performance, poses substantial methodological challenges. Our work is primarily motivated to determine the passive stiffness of the myocardium from the measurement of the left ventricle (LV) volume at various time points, which is crucial for diagnosing cardiac physiological conditions. Although there have been significant advancements in cardiac mechanics modelling, the tasks of inference and uncertainty quantification of myocardial stiffness remain challenging, with high computational costs preventing real-time decision support. We adapt Gaussian processes to construct a statistical surrogate model for emulating LV cavity volume during diastolic filling to overcome this challenge. As the LV volumes, obtained at different time points in diastole, constitute a time series, we apply the Kronecker product trick to decompose the complex covariance matrix of the whole system into two separate covariance matrices, one for time and the other for biophysical parameters. To proceed towards personalized health care, we further integrate patient-specific LV geometries into the Gaussian process emulator using principal component analysis (PCA). Utilizing a deep learning neural network for extracting time-series left ventricle volumes from magnetic resonance images, Bayesian inference is applied to determine the posterior probability distribution of critical cardiac mechanics parameters. Tests on real-patient data illustrate the potential for real-time estimation of myocardial properties for clinical decision-making. These advancements constitute a crucial step towards clinical impact, offering valuable insights into posterior uncertainty quantification for complex cardiac mechanics models.
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Ge et al. (2024) studied Myocardial stiffness (n=226). Time series Gaussian process emulator with Kronecker product vs. Traditional Gaussian process was evaluated on Computational time per training iteration (seconds). The time-series Gaussian process emulator with Kronecker product decomposition reduced computational time per training iteration to 0.01 seconds compared to 1.21 seconds for traditional GP.
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