clinical field strengths are 1.5T and 3.0T and research efforts are underway to
use higher field strengths such as 7.0T and above. The demand for higher field
strengths is driven mainly by neuroimaging applications. There are, however,
other applications where high field strengths have been recognized and a
potential limitation or even a safety liability. Such applications include
imaging near the lungs and airways and interventional MRI where potential device
heating is a major obstacle.
In order to explore these applications, it is useful to be able to simulate what images may look like at lower field strength. The Low Field Simulator is a simple set of Matlab scripts that will allow users to generate a first order approximation of low field images from a high field acquisition. The simulation is based on a rescaling of the signal relative to the noise level. This rescaling takes a number of parameters into consideration: 1) sequence type, 2) relative field strength, 3) T1, and T2 values, and 4) TR and TE. The simulation is a simplification in that it assumes a single set of relaxation properties for the entire image and it assumes imaging is done under steady state conditions. As such, it is not necessarily adequate for predicting contrast ratios at lower field strength, but it will give an impression of what the expected signal to noise level would look like. This is an important first step in evaluating the feasibility of moving an application to lower field. This toolbox could be a very valuable starting point groups considering low field imaging.
Without any prior experience with the toolbox, this reviewer managed to easily download the source code and run through the two simulation examples that are provided. The review was done with Matlab version 22.214.171.124613 (R2015a) on a Macbook Pro running OSX 10.10.5 (Yosemite).
The simulation toolbox is described in:
Minimum Field Strength Simulator for Proton Density Weighted MRI .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