MATLAB Scripts for Low SNR Simulation at Low Field Strengths
Under certain assumptions, low-field MRI acquisitions can be simulated from high-field data and minimum field strength requirements can be determined. This package presents a simple framework for simulating low-field MRI acquisitions, which can be used to predict the minimum B0 field strength necessary for MRI techniques. This framework may be particularly useful for the evaluation of de-noising and constrained reconstruction techniques, and the possibilities of translating them to less expensive low-field scanners.
(c) Weiyi Chen, Ziyue Wu, Krishna Nayak, May 2016.
University of Southern California
function [ k_low ] = lowfieldgen( inParam ) % LOWFIELDGEN simulates low field noise % See details inside the m-file
We recommend start using this package by running these 2 demos:
Upper Airway Gridding Reconstruction: demo_airway.m
This example shows gridding reconstruction on simulated low field data, based on data acquired using Golden angle radial FLASH at 3T.
Fat-water Separation: demo_fatwater.m
This example shows fat-water separation on simulated low field data, based on data acquired using product IDEAL GRE sequence at 3T.
- Body noise dominance : We assume that body thermal noise is the dominant noise source at all field strengths under investigation (0.1 – 3.0 T).
- Consistent B1+ field : We assume that the uniformity of RF transmission is consistent across field strengths.
- Consistent B1- field : We assume that the receiver coils have the same geometry and noise covariance at different field strengths.
- Consistent B0 homogeneity : We assume the same off-resonance in parts-per-million (ppm) at different field strengths. This results in less off-resonance in Hertz at lower field.
- Single species dominance : We use a single global relaxation correction function to account for the signal change at different field strengths.
- Steady state acquisition : If the signals are not acquired at steady state, the magnetization relaxation will be determined not only by the sequence parameters but also by the initial state.
- Wu Z, Chen W, Nayak KS (2016) Minimum Field Strength Simulator for Proton Density Weighted MRI. PLoS ONE 11(5): e0154711. doi:10.1371/journal.pone.0154711. JNRL
- Wu Z, Chen W, Nayak KS (2016) Low-Field Simulation and Minimum Field Strength Requirements. ISMRM Workshop on Data Sampling and Image Reconstruction, Sedona, Arizona. TALK
Fat-water separation is implemented using the graph cut field-map estimation method from the ISMRM fat-water toolbox
Minimum Field Strength Simulator by Weiyi Chen, Ziyue Wu, Krishna Nayak is licensed under a Creative Commons Attribution 4.0 International License.