Martin Gajdošík1, Karl Landheer1, and Christoph Juchem1,2
1Department of Biomedical Engineering, Columbia University, New York City, NY, United States, 2Department of Radiology, Columbia University Medical Center, New York, NY, United States
Synopsis
Pocket MRS is a mobile application that offers simple and
easy access to simulated spectra from human brain and its detailed analysis. Spectra
available from 0 to 300 ms TE were simulated using realistic quantum mechanical
density operator simulations, and scaled using known concentration, T1
and T2 values across 19 different metabolites and a sum of 10
macromolecules. Spectra can be manipulated with respect to echo time, magnetic
field quality and noise levels, providing quick and convenient visualization of
the impact of typical experimental conditions on spectral appearance.
Motivation
The
successful application of in vivo
magnetic resonance spectroscopy (MRS) methods requires the understanding of its
principles, yet due to their complexity, MRS spectra and the underlying
metabolic information can be difficult to grasp. Moreover, many spectra
acquired with MRS consist of multiple overlapping signals, which are hard to
disentangle without proper prior knowledge.
Today,
these tasks are answered by specialized software running on desktop computers.
Although these software packages are irreplaceable when it comes to spectral
analysis and quantification, they are not suitable for on-the-fly visual
investigation of spectra. Here we employ the
computational power of currently available smartphones, i.e., mobile computers,
to visualize and manipulate simulated spectra to resemble proton (1H) MR spectra measure in vivo.
The motivation to develop an interactive and accessible tool for MRS was driven by situations in the scanner control room, class room or when viewing spectra from the PACS system, when, for example, chemical shifts, T2 relaxation and effects of J-evolution for a chemical compound needed to be explained. A visual comparison of the spectrum from Pocket MRS to the spectrum at hand can provide deep and convenient insight into the presence, absence or shape of particular peaks of interest across customizable echo time, linewidth and noise level.
The
proposed MRS mobile application (app), Pocket MRS, was designed to be a fast, interactive
and educational tool for students, medical professionals, researchers and
anybody else engaged in the field of in vivo MRS.Software Description
The
spectra are based on simulations of typical spin-echo based acquisition
performed in MAgnetic Resonance Spectrum Simulator (MARSS)
1,2 for brain at 3 T.
19 brain metabolites and macromolecules were simulated from 0 to 300 ms T
E with
T
R of 2 s, literature T
1 and T
2 relaxation times
3–8 and concentrations
5,9 in vivo at 3 T, and measured J-coupling
constants in case of metabolites
10,11. Macromolecule
signal was treated as the sum of 10 individual macromolecule resonances. All signals
were exponentially line broadened by $$$ (\frac{1}{\pi T_{2,m}}) $$$ where $$$ T_{2,m} $$$ is the T
2 value for the particular metabolite, and macromolecules used previously measured linewidths
4.
Data
is stored in memory as free induction decays; hence
no simulations are required on the fly. Signal processing is performed by
using sliders or other controls on the display. The spectra can then be easily manipulated with respect to the static
magnetic field (
B0) homogeneity
and noise levels, providing quick and convenient visualization of the impact of
typical experimental conditions on spectral appearance. This enables real-time adjustment
of spectral parameters that can take minutes or longer to simulate on a desktop
computer. Current version of the app was developed for Apple iOS
14.2.
The
app consists of several tools for easy identification of metabolites:
- Brain
MRS - Adjustments: Current version contains simulated
spectra with ideal spin echo sequence brain metabolites at 3 T (Fig. 1A). This
tool allows to adjust three parameters: echo time (TE), line broadening and
noise levels. All three parameters can be changed with sliders and the spectrum
will update in real-time. User can create realistic spectrum with combination of
different TE (Fig. 1B), line broadening (Fig. 1C) and noise levels (Fig. 1D). Because
spectra already contain natural linewidth broadening due to T2
relaxation, a broadening of 0 Hz reflects a perfectly homogenous magnetic field
B0, not 0 Hz linewidths of metabolites.
- Brain
MRS – Analysis: After all parameters are set, for
example reflecting short TE spectrum (Fig. 2A), the brain spectrum can be then
analyzed by selective visualization of a metabolite (Fig. 2B) or group of
metabolites (Fig. 2C). This could be used to identify an “unknown” peak by
comparing the spectrum at hand to the particular spectrum with matched
parameters in Pocket MRS.
- Metabolites:
All metabolites which are part of the brain MRS spectrum can be individually
observed with this tool. User can adjust the TE (0 – 300 ms) and line
broadening. This tool is especially useful for observing J-modulation of
coupled spin systems (Fig. 3).
- Library:
Information about each metabolite used in the simulations and most common MRS
acquisition methods can be found here. The library contains information stored online
or offline. Online information is linked via uniform resource locator (URL) and
can be viewed in a web browser (Fig. 4A). Offline information is stored in the
app and provides more MRS specific information (Fig. 4B). All off-line
information is cited and the reference is linked via the publisher’s URL (Fig. 4C).
Discussion
The
presented version of Pocket MRS offers data for analysis of proton spectra of the
brain tissue at 3 T. Other field strengths (e.g., 1.5 and 7 T), nuclei (e.g., 13C,
31P) and tissues (e.g. liver, muscle, breast) will be included in future
versions. Pocket MRS will be available for iOS devices in the beginning of 2021
on Apple App Store.Conclusion
Pocket MRS app is presented that provides fast, simple and
easy access to simulated spectra from human tissues and its analysis. The app can
be a quick digital reference and valuable help for the MRS community when
desktop computers and extended MRS software tools are not available.Acknowledgements
The authors wish to thank
Joan José Martínez from Columbia Technology Ventures and Ivan Lozuk from
Columbia University Information Technology for their help with the project.References
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