Grants

NIH/NIBIB/T32 EB032755 (MPI Ennis/Butts Pauly)

05.01.2023 to 04.30.2028

Stanford’s Translational Biomedical Imaging Instrumentation (TBI2) Training Program

This multidisciplinary predoctoral training program will train the next generation of researchers and inventors of biomedical imaging technology. Trainees will gain expertise in multiple medical imaging modalities, rigorous and reproducible experimental design, and translational applications. Trainees that complete our training program will have a unique skill set that fulfills a distinct national need for researchers and leaders with expertise in advanced biomedical imaging instrumentation. 

NIH/NHLBI/R01 HL171515 (MPI Ennis/Vasanawala)

01.01.2024 to 12.31.2027

Fast and Accurate Cardiovascular MRI with Hyper 4D-Flow

This proposal aims to enable fast, accurate, and robust 4D-Flow image acquisitions; fast and accurate 4D-Flow image reconstruction; and to evaluate our 4D-Flow methods in children with repaired tetralogy of Fallot (rTOF).

NIH/NHLBI/R01 HL173845 (MPI Ennis/Marsden/Ma)

05.03.2024 to 04.30.2029

A Multi-physics Simulator For Pediatric Cardiac Surgical Planning

This project aims to develop, validate, and clinically evaluate a patient-specific heart simulator that will allow surgeons to compare repair techniques for children with single-ventricle physiology.

National Science Foundation (PI Ennis)

09.01.2022 to 08.31.2026

Collaborative Research: SCH: Quantifying Cardiac Performance by Measuring Myofiber Strain with Routine MRI

The objectives of this proposal are: 1) Computing myofiber strains in healthy volunteers from MRI data routinely acquired in the clinic; 2) Quantify patient specific uncertainty in myofiber strain predictions based on imaging data noise and model assumptions. 3) Characterize cardiac function in patients affected by aortic stenosis by measuring myofiber strains; 4) Accelerate transition to practice by deploying the proposed framework as a cloud computing platform.

GE Healthcare Inc. (PI Ennis)

07.01.2025 to 07.01.2027

Single Beat 2D PC-MRI

The purpose of this proposal is to develop and refine methods for fast and accurate 2D phase-contrast MRI. This project leverages our prior experience applying deep learning-based reconstruction methods to 2D PC-MRI measurements and estimation of gradient impulse response functions (GIRFs) to improve corrections of gradient system imperfections. By incorporating these novel methods, we aim to enable single-beat 2D PC-MRI and in-line background phase correction, thereby generating a fast (single heartbeat) and accurate (<0.6 cm/s bias) 2D PC-MRI exam that can be readily used in the clinic.

Siemens Healthineers AG (PI Ennis)

08.01.2025 to 08.01.2028

Gradient Waveform Optimization for Ultrahigh Performance MRI Systems

The overall objective of this collaboration is to develop and evaluate software tools that enable fast and flexible prospective design of gradient waveforms subject to various constraints for a range of applications using the GrOpt toolbox. This collaboration will develop core software tools and prototype applications to demonstrate the effectiveness of GrOpt on the Siemens MRI systems.

NIH/NHLBI/R01 HL162260 (Subaward PI Ennis)

07.01.2023 to 06.30.2027

Machine Learning for Ventricular Arrhythmias

This project will develop a computer-based framework to personalize therapy for ventricular tachycardia, using machine learning and personalized computer models.

ARPA-H/1AY1AX000002-01 (PI Skylar-Scott, Co-I Ennis)

09.25.2023 to 09.24.2028

Health Enabling Advancements through Regenerative Tissue Printing (HEART)

The goal of this project is to integrate automated and organ-scale biomanufacturing capacity, entirely new and rapid 3D collaborative printing robots, and continuous layer-free bioprinters for rapid-production of complex tissue models to begin the era of organ biofabrication. 

Link to NIH Reporter