Ovidiu Cristian Andronesi1
1Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
Synopsis
Accurate
localization is key for MR spectra quality and metabolites quantification. Metabolites
low concentration and multiple frequencies pose more challenges in-vivo MRS than
MRI, due to B0 inhomogeneity, insufficient B1, chemical shift displacement, and
artifacts from lipids. Volume selection methods based on overlapping slices
improves MRS quality by limiting the region of interest to areas where B0 and
B1 can be better controlled. Spatial coverage can be improved by more modern
approaches where arbitrary volumes can be shaped with parallel transmit,
multiple volumes disentangled by parallel imaged, and different contributions
to the MRS signal can be modeled in the reconstruction
Purpose
To
improve spectral quality for better metabolite quantification. To familiarize
the audience with the details of localized MRS acquisition.Methods
Localization methods (PRESS, STEAM, LASER) based on
traditional excitation schemes use the overlap of three orthogonal slices to
select a volume at the intersection of the orthogonal slices (Figure 1). The
slice localization is proportional to the chemical shift of metabolites ($$$\nu$$$)
and inverse proportional to the bandwidth (BW) of the RF pulse excitation (the
so called chemical shift displacement error, $$$CSDE\sim\nu\diagup BW$$$, Figure 2). Localization
methods can be classified based on their excitation bandwidth in non-adiabatic
(small BW) and adiabatic (large BW). While the former methods use only
amplitude modulation of the RF pulses to achieve slice selection, the later use
both amplitude and frequency modulation to effectively increase the RF
bandwidth of slice selection. Adiabatic pulses can achieve larger bandwidths
when limited by a given maximum amplitude of the RF field (Figure 3). In
addition, gradient can also be modulated to achieve even larger bandwidth such
as frequency offset independent adiabaticity
(FOCI) pulses and gradient offset independent adiabaticity (GOIA)
pulses, which in the case of GOIA are able to achieve a BW of 20 kHz with 3.5ms
pulse and 0.8 kHz (18 $$$\mu T$$$) maximum amplitude of B1 field by a complex
frequency modulation $$$\nu (t) = 1/k\cdot G(t)\int_{0}^{T_{p}}B_1^2(\tau)\diagup
G(\tau) d\tau$$$. In addition, adiabatic pulses are effective to compensate for B1
inhomogeneity and can provide more SNR at high magnetic fields.
Results
Comparison of localization methods are shown in phantoms and
human subjects. The PRESS sequence that uses refocusing pulses of 1.2 kHz has
10%/ppm CSDE at 3T and 23%/ppm CSDE at 7T. A significant reduction of CSDE (0.6%/ppm at 3T, 1.4%/ppm at 7T) and SNR increase
can be achieved with LASER using 20 kHz adiabatic pulses (Figure 4). Uniform excitation, sharper transition bands, and no
side bands can be demonstrated for adiabatic pulses (Figure 5). This can be
performed in human subjects including patients on clinical scanners that are
equipped with standard transmit hardware. With more advanced transmit hardware
including parallel transmit coils and amplifiers additional degrees of freedom
can be exploited in shaping the excitation profile. Compared to the rectangular
excitation of PRESS/STEAM/LASER voxels, more conformal excitation can be shaped
to an arbitrary anatomical region of interest when parallel transmit is
employed.Discussion
MRS acquisition using rectangular volume selection methods
is widely used in most clinical and research studies in human subjects. The
large availability of sequences, no special hardware requirements and easy of
protocol setup make this method a preferred choice for many user groups that do
not have technical expertise for more sophisticated approaches. A range of more
advance methods are being actively developed to address the limitations of
volume selection methods such as reduced spatial coverage and resolution, but
currently these methods are not widely available and are still evolving. Hence,
understanding and optimizing the mechanisms of volume selection methods is
important to achieve the best performance, to eliminate possible sources of
artifacts and to obtain adequate spectral quality for reliable metabolite
quantification.Acknowledgements
NIH funding
K22
CA178269, 1R01CA211080-01. Collaboration with Drs Wolfgang Bogner,
Malgorzata Marjanska, Borjan Gagoski,
Andre van der Kouwe,
and Aaron Hess.
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