High Performance Computing

Photon-Counting Computed Tomography for Evaluation of Coiled Intracranial Aneurysms

BACKGROUND AND PURPOSE: Intracranial aneurysms treated with endovascular embolization often require surveillance imaging using digital subtraction angiography, an invasive, risky, and expensive procedure. Existing non-invasive imaging modalities (standard computed tomography [CT] or magnetic resonance [MR] angiography) are often unsatisfactory for evaluating treated aneurysm due to artifacts from embolization devices. The objective of the present study was…

Read More Photon-Counting Computed Tomography for Evaluation of Coiled Intracranial Aneurysms
Digital twins for noninvasively measuring predictive markers of right heart failure

Digital twins offer a promising approach to advancing healthcare by providing precise, noninvasive monitoring and early detection of diseases. In heart failure (HF), a leading cause of mortality worldwide, they can improve patient monitoring and clinical outcomes by simulating hemodynamic changes indicative of worsening HF. Current techniques are limited by their invasiveness and lack of…

Read More Digital twins for noninvasively measuring predictive markers of right heart failure
Blood Flow Animation

Read More Blood Flow Animation
Amanda Randles: A Check-Engine Light for the Heart
Posted onJuly 18, 2025July 18, 2025

Listen to a podcast interview with Center Director Amanda Randles.

Read More Amanda Randles: A Check-Engine Light for the Heart
Center Members Win Award at ICCS25
Posted onJuly 11, 2025July 11, 2025

Congratulations to Center director Amanda Randles and members Aristotle Martin and William Ladd for winning Best Main Track Paper Award at the International Conference on Computational Science (ICCS25)!

Read More Center Members Win Award at ICCS25
Adaptive Physics Refinement for Anatomic Adhesive Dynamics Simulations

Explicitly simulating the transport of circulating tumor cells (CTCs) across anatomical scales with submicron precision—necessary for capturing ligand-receptor interactions between CTCs and endothelial walls—remains infeasible even on modern supercomputers. In this work, we extend the hybrid CPU-GPU adaptive physics refinement (APR) method to couple a moving finely resolved region capturing adhesive dynamics between a cancer…

Read More Adaptive Physics Refinement for Anatomic Adhesive Dynamics Simulations
Microfluidic Digital Twin for Enhanced Single-Cell Analysis

Advancing single-cell analysis requires tools that not only enable precise experimental measurements but also offer predictive capabilities to guide device optimization and expand experimental possibilities. This study addresses this need by developing a digital twin framework for mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform for single-cell analysis. By creating a virtual replica that integrates models…

Read More Microfluidic Digital Twin for Enhanced Single-Cell Analysis
A systematic quantification of hemodynamic differences persisting after aortic coarctation repair

Aortic coarctation (CoA) comprises 6%–8% of all congenital heart diseases and is the second most common cardiovascular disease requiring neonatal surgical correction. However, patients remain at high risk for long-term complications, notably recoarctation. Methods: Hemodynamic simulations were performed in a group of six patients following CoA repair, as compared to a group of age and sex-matched…

Read More A systematic quantification of hemodynamic differences persisting after aortic coarctation repair
Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning

Many human-targeted medications have been found to impact the composition of patients’ gastrointestinal microbiomes, which has been proposed as an unrecognized source of drug side effects, comorbidities, and reduced treatment efficiencies. However, current methods to detect such drug effects on the microbiome largely rely on patient sample analysis or in vitro high-throughput screening – which…

Read More Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning
BraTS orchestrator: Democratizing and Disseminating state-of-the-art brain tumor image analysis

The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically relevant tasks. However, despite its success and popularity, algorithms and models developed through BraTS have seen limited adoption in both scientific and clinical communities. To accelerate their dissemination, we introduce BraTS orchestrator,…

Read More BraTS orchestrator: Democratizing and Disseminating state-of-the-art brain tumor image analysis