Arizona State University

Mayo Foundation for Medical Education and Research

Other

David H Frakes

Andreas Spanias

Jayaraman Jayaraman Thiagarajan

Karthikeyan Natesan Ramamurthy

Mohamed Haithem Babiker

Brian W Chong

Ajit P Yoganathan

Ross Maciejewski

Mark Spano

Joseph Monaco

Alison Van Putten

Joseph Sansone

Christine M Zwart

Matthew Mortensen

Mark Smith

Nathaniel Kirkpatrick

Ryan Hess

Jonah Thomas

Hooman Yadollahi-Farsani

Priya Nair

Dustin Plaas

Marcus Herrmann

Systems and methods provide a novel computational approach to planning the endovascular treatment of cardiovascular diseases. In particular, the invention simulates medical device deployment and hemodynamic outcomes using a virtual patient-specific anatomical model of the area to be treated, high-fidelity finite element medical device models and computational fluid dynamics (CFD). In an embodiment, the described approach investigates the effects of coil packing density, coil shape, aneurysmal neck size and parent vessel flow rate on aneurysmal hemodynamics. A processor may receive patient clinical data used to construct the relevant anatomical structure model. The processor may access medical device models constructed using finite element analysis and three dimensional beam analysis, and simulates the deployment of selected medical devices in the anatomical structure model. The selected medical device models and the anatomical structure model mesh, allowing the processor to simulate hemodynamic outcomes using computational fluid dynamics.

Device specific finite element models for simulating endovascular treatment

Patient-specific 3D complex coils and methods of making and using such coils, including custom fixtures for the manufacture of such coils. Such patient-specific 3D complex coils improve treatment outcomes for cerebral aneurysm repair. The aneurysm may be an aneurysm in a specific patient and/or population of patients having a similar aneurysm shape and size.

Customized endovascular devices and methods pertaining thereto

Some systems include a memory, and a processor coupled to the memory, wherein the processor is configured to: identify one or more spatial markers in a medical data-based image of a patient, identify one or more spatial markers in a real-time perceived image of the patient, wherein the one or more spatial markers in the medical data-based image correspond to an anatomical feature of the patient and the one or more spatial markers in the real-time perceived image correspond to the anatomical feature of the patient, superimpose the medical data-based image of the patient with the real-time perceived image of the patient, and align the one or more spatial markers in the medical data-based image with the respective one or more spatial markers in the real-time perceived image.

Real-time medical image visualization systems and related methods

A profile of porosities and permeabilities calculated from several sample volumes in a system can speed up computational fluid dynamics (CFDs). Heterogeneous fluid flow paths can be calculation intense, limiting the accuracy of fluid-path models. Further, allowing a user to define a number of sample volumes in a model system allows pre-calculation of porosities and permeabilities for use in Navier-Stokes formulas for modeling fluid flow and gives the user control over calculation time and accuracy. This is helpful, for example, in modeling endovascular interventions where fluid dynamics are determinative in the efficacy or method of treatment for various vascular disorders, such as aneurysms, and heart disease. This is also beneficial in other healthcare contexts, like blood filters, embolic gels, endografts, web devices, and atrial appendage occluders, among others. This disclosure is also relevant to fluid dynamics generally, such as in consumer products; and oil and gas exploration, recovery, and production.

Geometry based method for simulating fluid flow through heterogeneous porous media

Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.

Measuring glomerular number from kidney MRI images

A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

Kernel sparse models for automated tumor segmentation

A system for generating an output image based on an original image via an imaging application can comprise a parsing module, an optical flow displacement module, and a new pixel generation module. The parsing module can establish a magnified resolution lattice from an original resolution lattice of the original image. The optical flow displacement module can establish optical displacement vectors extending, within the magnified resolution lattice, between original pixels and optical displacement locations, the optical displacement vectors based on one-dimensional optical flow brightness constraint calculations for the original pixels. The new pixel generation module can generate new pixels in the magnified resolution lattice via one or more interpolations based on the optical displacement vectors. Other embodiments and related methods are also disclosed herein.

Image processing systems and methods

Approaches to three-dimensional (3D) data reconstruction are presented. The 3D data comprises 2D images. In some embodiments, the 2D images are directionally interpolated to generate directionally-interpolated 3D data. The directionally-interpolated 3D data are then segmented to generate segmented directionally-interpolated 3D data. The segmented directionally-interpolated 3D data is then meshed. In other embodiments, a 3D data set, which includes 2D flow images, is accessed. The accessed 2D flow images are then directionally interpolated to generate 2D intermediate flow images.

Data reconstruction using directional interpolation techniques

Growing community of inventors

Scottsdale, AZ, United States of America

David H Frakes