John Adams1,2, William Handler1,2, and Blaine Chronik1,2
1Department of Physics and Astronomy, Western University, London, ON, Canada, 2xMR Labs, London, ON, Canada
Synopsis
Renewed interest in clinical low-field MR systems has opened up a new
design space for MR pulse sequence design. To explore these opportunities, and
to better inform hardware design, we are developing a flexible simulation tool
based on a numerical simulation of the Bloch equations. This tool will both be
able to model pulse sequences under the influence of realistic applied fields,
and account for changes in relaxation time due to changes in field strength.
This abstract presents our work to date in developing this tool.
Introduction
Research interest in low-field (<1
Tesla) magnetic resonance imaging (MRI) has grown in recent years as a way to
reduce the cost of MRI scans, and to make MR more accessible by reducing or
removing siting requirements for the magnet1,2. However, as we
change field strength, we change how a sample’s physical characteristics
translate into MR image contrast due to altered relaxation times3,4. Furthermore,
at low-field, changes in applied field due to the activation of modern high
strength gradients can alter relaxation times over the course of an MR
sequence. This modifies the contrast provided by existing techniques, and opens
opportunities for novel, low-field specific contrasts.
To account for these effects while designing
new MR hardware, we are developing a flexible MR pulse sequence modeling tool
capable of modeling the results of an MR experiment. In contrast with many
existing solvers, this tool is designed to simulate the system under the
influence of arbitrary magnetic fields in order to allow novel hardware
configurations and non-idealities to be included in the simulation. This
presentation will discuss our development efforts to date, and our ongoing
validation work for this tool.Methods
Development
of this simulation tool is being done in Python 3. A system of python classes
are used to specify the magnetic fields being applied to a sample as
time-dependent functions. Once defined, these field functions are used to solve
the Bloch equations in the rotating reference frame for each voxel using
SciPy’s solve_ivp() function, itself using a Runge-Kutta 853 numerical
integration method5. The sample is
simulated by dividing the sample volume into voxels, which each contain a
defined number of randomly distributed spins. T2* relaxation
is simulated by giving each spin a randomly generated field offset calculated
based off a tissue specific T2* value6. With this
structure, an end user can write a script calling different pulse sequence
elements, which will output a simulated signal that can then be processed using
existing signal analysis techniques to produce the images or other data desired
by the user.
To validate this toolset, we recreated a
number of basic pulse sequences including a free-induction decay, a CPMG train
(TE = 100 ms, 6 echoes), a stimulated echo (TE1 = 50 ms, TE2 = 200 ms), and a simple
1D gradient echo (TE = 8 ms) 3. These
sequences were then applied to a virtual phantom with homogenous, user defined
properties (T1 = 2 s, T2 = 200 ms, T2*
= 30 ms). The resultant output was compared to the initial model used by the
simulation for validation. In this initial set of experiments, no noise was
simulated.Results
A simple free induction decay experiment
was run to test our T2* relaxation results; fitting the signal curve yielded a
relaxation time of 34 ± 1 ms. Simulated signal for the CPMG and stimulated
echoes are presented in Figures 1 and 2. In the CPMG sequence, the peaks of
each echo were used to compute the T2 relaxation time of the
resultant signal, giving a result of 188 ± 5 ms. For the stimulated echo
sequence, we expected and observed 4 echoes of varying amplitude; the primary
stimulated echo at 50 ms after beginning signal acquisition, plus much smaller
echoes at 150, 200, and 250 ms. The gradient echo, shown in Figure 3, also yielded
the expected peak at the midpoint of the frequency encode gradient.Discussion
At this current stage, our simulation tool is reliably reproducing basic
behaviours of an MR experiment while giving results that are roughly in
accordance with the physical parameters assigned to the simulated phantom
values (with percentage differences between the phantom and simulated experimental
values being 13% for T2* and 6.5% for T2 relaxation).Conclusions
Our simulation tool in its
current form is able to replicate the fundamental aspects of an MR pulse
sequence. Future work will focus on developing simulated imaging experiments,
as well as adding features such as field dependent relaxation time calculations,
and multithreading to improve simulation speeds. Ultimately we aim for this simulation
tool to have the flexibility needed to simulate and optimize pulse sequences
for a new generation of low field systems currently in development, novel
hardware geometries and experimental techniques such as dreMR7 which violate the fundamental assumptions typical
simulation tools rely on for their analyses.Acknowledgements
No acknowledgement found.References
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