Biology subjects

Schollenberger, J.

Publications and source records attributed to Schollenberger, J..

4 recordsLinked to original sources

bioRxiv2026

Evidence of tornadic phenomena in cerebral aneurysms

Cerebral aneurysms are abnormal outpouchings of arteries within the brain and occur in [~]1 in 30 adults. Their initiation, growth, and rupture have been linked to focal blood flow abnormalities--often termed "disturbed" or "hostile" hemodynamics--but commonly-used hemodynamic metrics yield conflicting associations with pathology and lack a unifying mechanistic interpretation. Building on a theoretically-grounded link between wall shear stress and near-wall vorticity, we hypothesized that a topology-based description of near-wall flow can operationalize the concept of hostile hemodynamics in a reproducible way. Inspired by atmospheric tornadic phenomena, we sought a principled taxonomy of coherent near-wall fluid structures with potential mechanobiological and clinical implications. Using high-fidelity computational fluid dynamics simulations in anatomically realistic geometries, we identified coherent near-wall fluid structures whose organization mirrors well-studied atmospheric phenomena: tornado-like columnar rotating cores; downburst-like nonrotating wall-impinging jets with tangential outflow, roll-cloud-like tangential vortices; and mixed configurations. These tornadic events on the aneurysm luminal surface were identified from wall shear stress topology, consistent with its theoretical connection to near-wall vorticity kinematics. The presence of tornadic phenomena--and their imprints on the aneurysm wall--was independently observed in vivo using 4D flow magnetic resonance imaging. By translating concepts from atmospheric physics into vascular biomechanics, this topology-based framework yields a unified mechanistic language for describing near-wall hemodynamics, resolving blood flow complexity into interpretable and reproducible coherent fluid structures, enabling standardized hemodynamic phenotyping, and supporting hypothesis-driven studies of aneurysms and other cardiovascular diseases where greater fluid-mechanical specificity and interpretability may strengthen links between mechanobiology and clinical risk.

bioengineering↗

bioRxiv2021

Cerebrovascular super-resolution 4D Flow MRI - using deep learning to non-invasively quantify velocity, flow, and relative pressure

The development of cerebrovascular disease is tightly coupled to changes in cerebrovascular hemodynamics, with altered flow and relative pressure indicative of the onset, development, and acute manifestation of pathology. Image-based monitoring of cerebrovascular hemodynamics is, however, complicated by the narrow and tortuous vasculature, where accurate output directly depends on sufficient spatial resolution. To address this, we present a method combining dedicated deep learning and state-of-the-art 4D Flow MRI to generate super-resolution full-field images with coupled quantification of relative pressure using a physics-driven image processing approach. The method is trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 12.0 {+/-} 0.1%, mean absolute error (MAE): 0.07 {+/-} 0.06 m/s at peak velocity), flow (relative error: 6.6 {+/-} 4.7%, root mean square error (RMSE): 0.5 {+/-} 0.1 mL/s at peak flow), and with maintained recovery of relative pressure through the circle of Willis (relative error: 11.0 {+/-} 7.3%, RMSE: 0.3 {+/-} 0.2 mmHg). Furthermore, the method is applied to an in-vivo volunteer cohort, effectively generating data at <0.5mm resolution and showing potential in reducing low-resolution bias in relative pressure estimation. Our approach presents a promising method to non-invasively quantify cerebrovascular hemodynamics, applicable to dedicated clinical cohorts in the future.

bioengineering↗

bioRxiv2021

A combined computational fluid dynamics and MRI Arterial Spin Labeling modeling strategy to quantify patient-specific cerebral hemodynamics in cerebrovascular occlusive disease

Cerebral hemodynamics in the presence of cerebrovascular occlusive disease (CVOD) are influenced by the anatomy of the intracranial arteries, the degree of stenosis, the patency of collateral pathways, and the condition of the cerebral microvasculature. Accurate characterization of cerebral hemodynamics is a challenging problem. In this work, we present a strategy to quantify cerebral hemodynamics using computational fluid dynamics (CFD) in combination with arterial spin labeling MRI (ASL). First, we calibrated patient-specific CFD outflow boundary conditions using ASL-derived flow splits in the Circle of Willis. Following, we validated the calibrated CFD model by evaluating the fractional blood supply from the main neck arteries to the vascular territories using Lagrangian particle tracking and comparing the results against vessel-selective ASL (VS-ASL). Finally, cerebral hemodynamics were assessed in two patients with CVOD and a healthy control subject. We demonstrated that the calibrated CFD model accurately reproduced the fractional blood supply to the vascular territories, as obtained from VS-ASL. The two patients revealed significant differences in pressure drop over the stenosis, collateral flow, and resistance of the distal vasculature, despite similar degrees of clinical stenosis severity. Our results demonstrated the advantages of a patient-specific CFD analysis for assessing the hemodynamic impact of stenosis.

bioengineering↗

bioRxiv2020

CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization. CRIMSON binaries for Microsoft Windows 10, documentation and example input files are freely available for download from www.crimson.software, and the source code with compilation instructions is available on GitHub https://github.com/carthurs/CRIMSONFlowsolver (CRIMSON Flowsolver) under the GPL v3.0 license, and https://github.com/carthurs/CRIMSONGUI (CRIMSON GUI), under the AGPL v3.0 license. Support is available on the CRIMSON Google Groups forum, located at https://groups.google.com/forum/#!forum/crimson-users.

bioengineering↗