A simplified model for brain activity measurement.
problem Estimating the BOLD response in fMRI data.
method Proposes a lightweight model for HRF with fewer parameters.
result Reduces optimization complexity and facilitates applications.
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14 results for “hemodynamics”
Study analyzes sensitivity of RL algorithm for ICU hemodynamic management.
problem Evaluating safety and reliability of RL in clinical settings.
method Sensitivity analysis of Duel-DDQN on ICU sepsis patients.
result RL policies are sensitive to various implementation factors.
LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamicsstat.ML
Generative model learns shape drift for quantifying domain uncertainty in hemodynamics.
problem Quantifying domain uncertainty in medical image segmentation for biomarker estimation.
method Conditional stochastic interpolant framework based on LDDMM registration.
result Generative model can create random perturbations of shapes for biomarker estimation.
Purpose: Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative to perfusion imaging with contrast agents. Fixing values of certain model parameters in traditional ASL, which actually vary from region to region, may introduce bias in perfusion estimates. Adopting Magnetic Resonance Fingerprinting (MRF…
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learningcs.LG
Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology a…
SBI improves uncertainty analysis of cardiovascular biomarkers.
problem Mapping waveforms back to plausible physiological parameters.
method Simulation-based inference (SBI) for statistical inference.
result Posterior distributions provide a multi-dimensional representation of uncertainty.
Study forecasts aortic pressure with deep learning models.
problem Forecasting noisy, non-stationary aortic pressure.
method Used deep learning models, specifically recurrent neural networks with Legendre Memory Unit, on 25 Hz time series data.
result Recurrent neural networks with Legendre Memory Unit achieved the best performance with an overall forecasting error of 1.8 mmHg.
A Study of Different Modeling Choices For Simulating Platelets Within the Immersed Boundary Methodmath.NA
The Immersed Boundary (IB) method is a widely-used numerical methodology for the simulation of fluid-structure interaction problems. The IB method utilizes an Eulerian discretization for the fluid equations of motion while maintaining a Lagrangian representation of structural objects. Operators are defined for transmit…
Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In real…
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can no…
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels during hundreds of sequential time points. One approach towards illuminating the connection between fMRI and cognitive function is through dec…
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
Multi-view data are increasingly prevalent in practice. It is often relevant to analyze the relationships between pairs of views by multi-view component analysis techniques such as Canonical Correlation Analysis (CCA). However, data may easily exhibit nonlinear relations, which CCA cannot reveal. We aim to investigate …
New algorithms improve vascular flow simulations in aortic aneurysms.
problem Limited accuracy of MRI in hemodynamics, patient-specific flow boundary conditions, and CFD's computational demands.
method Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets) integrated with 3D Navier-Stokes equations.
result Improved computational efficiency and good agreement with CFD simulations.