LVADNet3D: A Deep Autoencoder for Reconstructing 3D Intraventricular Flow from Sparse Hemodynamic Data
This addresses the challenge of assessing hemodynamic conditions in LVAD patients, where clinical imaging is limited, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of reconstructing 3D intraventricular blood flow from sparse data in LVAD patients, proposing LVADNet3D, which outperformed a baseline UNet3D model with lower reconstruction error and higher PSNR.
Accurate assessment of intraventricular blood flow is essential for evaluating hemodynamic conditions in patients supported by Left Ventricular Assist Devices (LVADs). However, clinical imaging is either incompatible with LVADs or yields sparse, low-quality velocity data. While Computational Fluid Dynamics (CFD) simulations provide high-fidelity data, they are computationally intensive and impractical for routine clinical use. To address this, we propose LVADNet3D, a 3D convolutional autoencoder that reconstructs full-resolution intraventricular velocity fields from sparse velocity vector inputs. In contrast to a standard UNet3D model, LVADNet3D incorporates hybrid downsampling and a deeper encoder-decoder architecture with increased channel capacity to better capture spatial flow patterns. To train and evaluate the models, we generate a high-resolution synthetic dataset of intraventricular blood flow in LVAD-supported hearts using CFD simulations. We also investigate the effect of conditioning the models on anatomical and physiological priors. Across various input configurations, LVADNet3D outperforms the baseline UNet3D model, yielding lower reconstruction error and higher PSNR results.