# Practical Nonlinear Gradient Encoding for Enhanced Accelerated Imaging

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $376,875

## Abstract

Project Summary/Abstract
This project will benefit public health by ushering in a new technology to reduce MRI scan times. FRONSAC
adds nonlinear gradients of modest amplitude in dynamic waveforms to standard Cartesian MRI protocols.
This small perturbation to the standard Cartesian MRI scan allows for greater undersampling of data, and
faster acquisition times. This proposal is innovative because it marries the reliability of Cartesian
imaging with a small amount of nonlinear gradient encoding to greatly enhance parallel imaging
acceleration. The proposal is significant because it will demonstrate that a single well-characterized
nonlinear gradient waveform can improve undersampled imaging for a broad range of clinical
sequences and scan prescriptions, potentially doubling overall scan throughput for busy clinical sites.
The aims will demonstrate that, for many scan prescriptions, and even in the presence of common
experimental imperfections or other parallel imaging strategies, FRONSAC further multiplies acceleration by
an additional 2-4x.
1) Sequence development and optimization.
 a) Develop a FRONSAC waveform, with 3 NLG channels, for a GRE sequence that maximizes
 clinically acceptable acceleration.
 b) Using this FRONSAC waveform, develop a set of widely used clinical sequences implementing
 FRONSAC acceleration: 3D MP-RAGE, bSSFP, TSE and T2w-FLAIR.
2) Demonstrate FRONSAC imaging in vivo.
 a) Acquire human brain images and compare contrast with conventional encoding.
 b) Compare undersampling performance between FRONSAC and Cartesian encoding.
3) Show that undersampled images acquired using a FRONSAC gradient optimized for a single
 geometry shows persistent improvements over Cartesian encoding for human brain imaging (for
 different geometries and under various common experimental imperfections), and retains
 compatibility with other acceleration approaches.
 a) Test for undersampling artifacts when changing FOV, resolution, and slice orientation.
 b) Test sequences with known (introduced) imperfections: gradient timing errors, imperfect shim, or
 off-resonance spins.
 c) Demonstrate compatibility with both kz undersampling and multislice CAIPIRINHA.

## Key facts

- **NIH application ID:** 9982310
- **Project number:** 5R01EB022030-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** GIGI GALIANA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $376,875
- **Award type:** 5
- **Project period:** 2017-09-30 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982310

## Citation

> US National Institutes of Health, RePORTER application 9982310, Practical Nonlinear Gradient Encoding for Enhanced Accelerated Imaging (5R01EB022030-04). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/9982310. Licensed CC0.

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