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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.

Magnetic Resonance Engineering Laboratory

University of Southern California

Code Structure

Main Function


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:

  1. 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.

  2. 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.


  1. 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).
  2. Consistent B1+ field : We assume that the uniformity of RF transmission is consistent across field strengths.
  3. Consistent B1- field : We assume that the receiver coils have the same geometry and noise covariance at different field strengths.
  4. 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.
  5. Single species dominance : We use a single global relaxation correction function to account for the signal change at different field strengths.
  6. 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.


  1. 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
  2. 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


Creative Commons License
Minimum Field Strength Simulator by Weiyi Chen, Ziyue Wu, Krishna Nayak is licensed under a Creative Commons Attribution 4.0 International License.