arXiv subjects

Brandon Hardy

Publications and source records attributed to Brandon Hardy.

2 recordsLinked to original sources

arXiv2025

Comprehensive Analysis of Relative Pressure Estimation Methods Utilizing 4D Flow MRI

Magnetic resonance imaging (MRI) can estimate three-dimensional (3D) time-resolved relative pressure fields using 4D-flow MRI, thereby providing rich pressure field information. Clinical alternatives include catheterization and Doppler echocardiography, which only provide one-dimensional pressure drops. The accuracy of one-dimensional pressure drops derived from 4D-flow has been explored previously, but additional work is needed to evaluate the accuracy of 3D relative pressure field estimates. This work presents an analysis of three state-of-the-art relative pressure estimators: virtual Work-Energy Relative Pressure (vWERP), the Pressure Poisson Estimator (PPE), and the Stokes Estimator (STE). The spatiotemporal characteristics and sensitivity to noise were determined in silico. Estimators were then validated using a type B aortic dissection (TBAD) flow phantom with varying tear geometry and twelve catheter pressure measurements. Finally, the performance of each estimator was evaluated across eight patient cases. In silico pressure field errors were lower in STE compared to PPE, although PPE pressures were less noise sensitive. High velocity gradients and low spatial resolution contributed most significantly to local variations in 3D pressure field errors. Low temporal resolution lead to systematic underestimation of highly transient peak pressure events. In the flow phantom analysis, vWERP was the most accurate method, followed by STE and PPE. Each pressure estimator was strongly correlated with ground truth pressure values, despite the tendency to underestimate peak pressures. Patient case results demonstrated that each pressure estimator could be feasibly integrated into a clinical workflow.

physics.flu-dyn↗

arXiv2023

Generalized super-resolution 4D Flow MRI $\unicode{x2013}$ using ensemble learning to extend across the cardiovascular system

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinical level input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.

eess.IV↗