Magnetic Resonance in Medicine 58:1107–1116 (2007)
Accelerated Short-TE 3D Proton Echo-Planar
Spectroscopic Imaging Using 2D-SENSE with
a 32-Channel Array Coil
Ricardo Otazo,1* Shang-Yueh Tsai,2 Fa-Hsuan Lin,3–5 and Stefan Posse1,6,7
MR spectroscopic imaging (MRSI) with whole brain coverage in
clinically feasible acquisition times still remains a major challenge. A combination of MRSI with parallel imaging has shown
promise to reduce the long encoding times and 2D acceleration
with a large array coil is expected to provide high acceleration
capability. In this work a very high-speed method for 3D-MRSI
based on the combination of proton echo planar spectroscopic
imaging (PEPSI) with regularized 2D-SENSE reconstruction is
developed. Regularization was performed by constraining the
singular value decomposition of the encoding matrix to reduce
the effect of low-value and overlapped coil sensitivities. The
effects of spectral heterogeneity and discontinuities in coil sensitivity across the spectroscopic voxels were minimized by unaliasing the point spread function. As a result the contamination
from extracranial lipids was reduced 1.6-fold on average compared to standard SENSE. We show that the acquisition of
short-TE (15 ms) 3D-PEPSI at 3 T with a 32 ⴛ 32 ⴛ 8 spatial
matrix using a 32-channel array coil can be accelerated 8-fold
(R ⴝ 4 ⴛ 2) along y-z to achieve a minimum acquisition time of
1 min. Maps of the concentrations of N-acetyl-aspartate, creatine, choline, and glutamate were obtained with moderate reduction in spatial-spectral quality. The short acquisition time
makes the method suitable for volumetric metabolite mapping
in clinical studies. Magn Reson Med 58:1107–1116, 2007.
© 2007 Wiley-Liss, Inc.
Key words: echo-planar spectroscopic imaging; parallel imaging; SENSE; regularization; large array coil
MR spectroscopic imaging (MRSI) provides spatial distribution of chemical shifts (1,2). As traditionally imple-
1Electrical
and Computer Engineering Department, University of New Mexico,
Albuquerque, New Mexico.
2Department of Electrical Engineering, National Taiwan University, Taipei,
Taiwan.
3MGH-HMS-MIT Athinoula A. Martinos Center for Biomedical Imaging,
Charlestown, Massachusetts.
4Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
5Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
6Department of Psychiatry, University of New Mexico School of Medicine,
Albuquerque, New Mexico.
7Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico.
Grant sponsor: National Institutes of Health; Grant numbers: R01 HD040712,
R01 NS037462, R01 EB000790-04, P41 RR14075; Grant sponsor: Mental
Illness and Neuroscience Discovery Institute (MIND).
Presented in part at the 14th Annual Meeting of ISMRM, Seattle, WA, 2006,
and at the 15th Annual Meeting of ISMRM, Berlin, Germany, 2007.
*Correspondence to: Ricardo Otazo, Electrical and Computer Engineering
Department, University of New Mexico, MSC01 1100 ECE Bldg., Albuquerque, NM 87131. E-mail: otazo@ece.unm.edu
Received 16 May 2007; revised 29 August 2007; accepted 3 September 2007.
DOI 10.1002/mrm.21426
Published online 29 October 2007 in Wiley InterScience (www.interscience.
wiley.com).
© 2007 Wiley-Liss, Inc.
mented with phase-encoding (3), it is very time-consuming, requiring as many repetitions as there are voxels in the
image, e.g., the acquisition time for a 3D experiment is
given by TA ⫽ NxNyNzTR, where Nx, Ny, and Nz are the
dimensions of the spatial grid and TR is the repetition
time. As a consequence, MRSI is usually restricted to low
spatial resolution and single-slice acquisition in clinical
practice. The development of fast MRSI methods that enable whole brain coverage with high spatial resolution
remains a major challenge in MRSI research. Many methods have been developed to provide faster spatial-spectral
encoding (4), such as echo-planar techniques that allow for
simultaneous spatial-spectral encoding using time-varying
gradients (5). Proton echo planar spectroscopic imaging
(PEPSI) (6,7) is an implementation of this technique with a
trapezoidal readout gradient for simultaneous encoding of
one spatial dimension (x) and the spectral dimension (f)
providing a net acceleration of Nx over the conventional
phase-encoding method with comparable signal-to-noise
ratio (SNR) per unit time and unit volume (8). However,
3D-PEPSI is still very time-consuming due to phase-encoding along the third spatial dimension.
Accelerated spatial encoding can be accomplished using
parallel imaging techniques (9,10), where subsampled kspace data are acquired using multiple receive coils with
spatially varying reception profiles. The knowledge of the
spatially varying coil sensitivity profiles allows for reconstruction of subsampled data. Acceleration is obtained at
the expense of SNR reduction in the reconstructed image.
Sensitivity-encoding (SENSE) (11) parallel imaging
method has been applied to accelerate phase-encoded (12)
and turbo-spin-echo (TSE) MRSI (13). Even though SENSE
reconstruction is applied in the same way as in MRI for
each spectral point of the MRSI data, the low-resolution
characteristics of the MRSI acquisition can produce residual aliasing artifacts if the coil sensitivities vary within the
voxel. In order to reduce these artifacts Dydak et al. (12)
used extrapolation of the sensitivity maps to avoid discontinuities at the border; Zhao et al. (14) employed a two-step
SENSE reconstruction to optimize the sensitivity maps;
and Sanchez-Gonzalez et al. (15) proposed using coil sensitivities with higher spatial resolution to optimize the
point spread function (PSF) with respect to variation of the
coil sensitivities within a voxel. Using 2D SENSE with
TSE-MRSI, Dydak (16) has shown 3D-MRSI within 20 min
with a 2 ⫻ 2 acceleration along x and y using a 32 ⫻ 32 ⫻
8 spatial matrix. Further acceleration in parallel MRSI can
be obtained by combining fast gradient-encoding techniques such as echo-planar encoding and parallel imaging.
We recently introduced the combination of 1D-SENSE and
2D-PEPSI using an array coil with eight elements to
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achieve up to 3-fold acceleration along the y dimension
(17). We also presented preliminary data using the combination of 1D-GRAPPA (generalized autocalibrating partially parallel acquisition) and PEPSI in previous studies
obtaining a similar acceleration factor (18,19). Recently,
Zhu et al. (20) demonstrated an acceleration factor of 1.5
using 1D-GRAPPA and 3D-EPSI.
Acceleration in parallel imaging is limited by the available SNR and the spatially varying noise amplification
factor in the reconstruction (g-factor). Several methods
were proposed to reduce the loss in SNR in order to
achieve higher accelerations, such as acceleration along
more than one spatial dimension (e.g., 2D-SENSE (21)) and
the use of very high field scanners, which increases the
baseline SNR and also improves sensitivity encoding by
taking advantage of the stronger spatial modulation of the
coil profiles (22). Other works have described the adaptation of the array coil geometry to minimize g-factor (23),
regularization in the reconstruction to improve conditioning of the encoding matrix (24), and array coils with a large
number of small elements to increase sensitivity and disparate coil sensitivity encoding along all spatial dimensions (25,26).
The use of 2D acceleration combined with an array coil
with a large number of elements is expected to provide
high acceleration capability for 3D encoding. For example,
acceleration factors as high as 16 were demonstrated in in
vivo for imaging experiments using 32-element arrays designed for multidimensional sensitivity encoding (25,26).
Moreover, in contrast to single surface coils, large array
coils also provide an improved depth penetration for volumetric applications (27,28).
This work aims at developing a very high speed encoding method for volumetric spectroscopic imaging in human brain at short TE using a combination of 3D-PEPSI
and regularized 2D-SENSE reconstruction. High 2D-acceleration factors were feasible using a 32-element array coil.
To overcome the technical challenges of this implementation we use regularization of the inverse matrix problem to
reduce amplification of noise and coil sensitivity estimation errors in positions with low-value and overlapped
coil sensitivities, and optimization of the sensitivity maps
to avoid residual aliasing artifacts due to the low spatial
resolution nature of MRSI. This is particularly important
for minimizing contamination from peripheral lipids due
to increased residual aliasing of the PSF with high acceleration. Lipid contamination is particularly strong at short
TE, which is advantageous for maintaining the sensitivity
gain at high field, as metabolite T2-values have been shown
to decrease with field strength (29,30). We demonstrate the
feasibility of 3D-MRSI at 3 T in 1 min for a 32 ⫻ 32 ⫻ 8
spatial matrix and 0.7 cc nominal voxel size.
MATERIALS AND METHODS
Data Acquisition
3D-PEPSI measurements were performed on healthy volunteers using a 3T MR scanner (Tim Trio, Siemens Medical Solutions, Erlangen, Germany) equipped with Sonata
gradients (maximum amplitude: 40 mT/m, slew rate:
200 mT/m/ms). The scanner has a built-in 32-channel RF
Otazo et al.
FIG. 1. a: 3D-PEPSI pulse sequence with water-suppression (WS),
outer-volume-suppression (OVS), spin-echo RF excitation, phase
encodes on Gy and Gz for y and z encoding and trapezoidal Gx
gradient for simultaneous encoding of x and f. b: The resulting
k-space trajectory is composed by parallel planes of zig-zag kx-t
trajectories. ⌬ky and ⌬kz determine the uniform sampling grid for ky
and kz. The black lines represents the trajectory after downsampling
ky and kz by a factor Ry and Rz, respectively.
receiver capability. A noncommercially available 32-channel head array coil (31) was used for RF reception, while
RF transmission was performed with a quadrature body
coil. The receiver array coil was built with a close-fitting
helmet design with circular elements arranged in patterns
of hexagonal and pentagonal symmetry similar to a soccer
ball providing sensitivity encoding along all spatial directions and higher sensitivity at any depth when compared
to commercial 8-channel array coils (31).
The 3D-PEPSI sequence (6) consisted of water-suppression (WS), outer-volume-suppression (OVS), spin-echo RF
excitation, phase-encoding for y and z, and the echo-planar readout module (Fig. 1). WS was performed using a
3-pulse WET module (32). OVS was applied along the
perimeter of the brain using 14 slices: 8 slices were manually positioned in the axial plane and 6 slices were fixed
on the boundaries of the 3D slab. Spatial-spectral encoding
is performed in k-space, where three orthogonal gradients
and the evolution of time traverse a path in four dimensions (kx, ky, kz, t), the Fourier space corresponding to (x, y,
z, f). Here x and y are the transverse coordinates, z is the
slice selection direction, and f is the chemical shift. ky and
kz are sampled on a uniform spatial grid by phase encoding
prior to readout. kx and t are sampled simultaneously
during the readout interval on a zig-zag trajectory defined
by a periodic trapezoidal gradient (Fig. 1). Acceleration
was performed by subsampling uniformly the k-space data
along the ky and kz dimensions by factors Ry and Rz,
respectively. The acquisition time is then given by TA ⫽
Ny/Ry ⫻ Nz/Rz ⫻ TR. Three in vivo accelerations were
employed: R ⫽ 4 (Ry ⫽ 2, Rz ⫽ 2), R ⫽ 8 (Ry ⫽ 4, Rz ⫽ 2),
and R ⫽ 12 (Ry ⫽ 6, Rz ⫽ 2). For comparisons the fully
sampled data were also acquired. Data acquisition includes WS and non-WS (NWS) scans. Data were acquired
Accelerated 3D-PEPSI Using 2D-SENSE
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FIG. 2. SENSE-PEPSI reconstruction diagram. Multicoil accelerated positive Yp(kt) and negative Yn(kt) echoes are reconstructed separately
using 2D-SENSE reconstruction for each timepoint. Coil-by-coil PEPSI reconstruction is then performed where positive and negative
echoes are combined after spectral phase correction. The final spectroscopic image S(r,f) data is obtained by least-squares combination
of the coil-by-coil reconstruction.
in an axial orientation using a 32 ⫻ 32 ⫻ 8 spatial matrix
to reconstruct 8 axial slices (FOV: 240 ⫻ 240 ⫻ 100 mm,
nominal voxel size: 0.7 cc). The readout direction was
right-left (RL) and phase-encoding directions were anterior-posterior (AP) and foot-head (FH). Fully sampled data
were acquired in 8.5 min using TR ⫽ 2 sec, TE ⫽ 15 ms.
The readout gradient consisted of 512 periods (each period
has a positive and negative part). The spectral bandwidth
after positive and negative echo separation was 1087 Hz. A
second NWS scan with much shorter readout duration (16
periods) and TR ⫽ 500 msec was acquired to estimate coil
sensitivity profiles (2.1 min). Data were collected with
2-fold oversampling for each readout gradient separately
to improve regridding performance and using a ramp sampling delay of 8 s to limit chemical shift artifacts. Regridding was applied to correct for ramp sampling distortion of
the kx-t trajectory. After regridding, 2-fold oversampling
was removed. The datasets were filtered in k-space using a
regular Hamming window along the x and y dimensions,
which increased the effective voxel size to 1.8 cc.
unfolded NWS data, and these phase corrections were
applied to the corresponding unfolded WS data arrays.
Spectral frequency assignment in the unfolded WS array
was made using the unfolded NWS data and assuming that
the largest signal in the unfolded NWS data represents
water. Positive and negative echo data were then added.
Eddy-current correction (33) was applied to the reconstructed NWS and WS data using the phase of the reconstructed NWS data to remove residual line shape distortion and possible water sidebands. The resulting multicoil
spatial-spectral signal S(r,f) was then combined using a
least-squares combination, i.e., SENSE reconstruction for
the case of nonaccelerated data, to obtain the final spectroscopic image S(r,f):
Nc
冘
S共r,f兲 ⫽
cl *共r兲Sl 共r,f兲
l⫽1
,
n
冘
[1]
cl *共r兲cl 共r兲
k⫽1
SENSE-PEPSI Reconstruction
SENSE-PEPSI reconstruction was performed on the accelerated NWS and WS data by separate processing of echoes
acquired with positive and negative gradients (Fig. 2). The
NWS dataset is required as a reference for spectral phase
correction, frequency alignment, eddy current correction,
and absolute metabolite concentration estimation. Accelerated positive and negative echoes (Yp(kt) and Yn(kt),
respectively, where k ⫽ (kx,ky,kz) is the k-space vector)
were sorted into separate arrays after time reversal of the
data acquired with negative gradients. A spatial Fourier
transform was then applied to obtain the spatially aliased
signals yp(rt) and yn(rt), where r ⫽ (x,y,z) is the position
vector. 2D-SENSE reconstruction with regularization as
described below was applied to each timepoint of yp(rt)
and yn(rt) to remove aliasing along y and z. Coil-by-coil
SENSE reconstructions were computed by multiplying the
2D-SENSE reconstruction by each of the individual coil
profiles (cp(r) for positive echoes and cn(r) for negative
echoes) to obtain ŝp(r,t) and ŝn(r,t). PEPSI reconstruction
(7,8) was performed using the coil-by-coil SENSE reconstruction, where the unfolded WS data from each coil was
separately phase-corrected and frequency-aligned along
the spectral domain using the corresponding unfolded
NWS dataset for coherent combination. Zero-order phases
of the water signals were automatically determined in the
where Sl(r,f) is the reconstructed signal for the l-th coil,
cl(r) is the coil sensitivity, and * denotes complex conjugation.
2D-SENSE with SSVD Solution
2D-SENSE reconstruction was performed on the y-z space
for each point of the x-t space. The x and t-space are
considered to be orthogonal, since the chemical shift artifact of the PEPSI spatial-spectral encoding is small (8).
Correlation between coils was removed by prewhitening
the acquired data and the estimated coil sensitivity functions using a sample average estimate of the noise covariance matrix (24). The benefit of this operation is that the
noise covariance matrix becomes equal to identity and can
hence be omitted later in the reconstruction. From this
point onward we consider that the accelerated data and
the reference data to estimate the coil sensitivity functions
were prewhitened, thus creating a set of virtual channels
that are uncorrelated.
The signal acquired by each coil in k-space with uniform
subsampling (acceleration) of ky and kz by factors Ry and Rz
respectively can be represented as:
Y l共k x,k y,k z兲 ⫽
冘
s共x,y,z兲c l共x,y,z兲e j2共kxx⫹Rykyy⫹Rzkzz兲,
x,y,z
l ⫽ 1,2, . . . ,N c,
[2]
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Otazo et al.
where s(x,y,z) is the object function to be reconstructed,
cl(x,y,z) is the coil sensitivity function and Nc is the number of coils. Applying a spatial discrete Fourier transform
(DFT), we obtain the spatially aliased signals:
Ry⫺1 Rz⫺1
y l共x,y,z兲 ⫽
冘冘
s共x,y ⫹ m yŴ y,z ⫹ m zŴ z兲c l共x,y
my⫽0 mz⫽0
⫹ m yŴ y,z ⫹ m zŴ z兲,
[3]
where Ŵy ⫽ Wy /Ry and Ŵz ⫽ Wz /Rz are the reduced FOV
along y and z (Wy and Wz represent the full FOV). Concatenating the signals acquired by each coil in a column
vector y (NC ⫻ 1), the matrix formulation of the encoding
equation with 2D acceleration for each point in the aliased
images is given by:
y ⫽ Es,
[4]
where the entries of the encoding matrix E (Nc ⫻ RyRz) are
given by the coil sensitivity functions at the corresponding
positions indicated in Eq. [4] and the vector s (RyRz ⫻ 1)
are the set of voxels to be reconstructed. The standard
solution of the system is given by the Moore-Penrose
pseudoinverse: ŝ ⫽ E†y ⫽ 共EH E兲⫺1 EH y (11). We will refer
to this expression as the least-squares (LS) or the standard
SENSE solution. For high acceleration, E becomes ill-conditioned and the standard SENSE solution is very susceptible to noise amplification and residual aliasing errors due
to numerical instabilities.
In this work we propose an inverse regularization
method based on constraining the singular value decomposition (SVD) of the encoding matrix E to compute its
pseudoinverse. The SVD of E is given by UE E VHE , where
UE (Nc ⫻ Nc) and VE (R ⫻ R) are unitary matrices containing the singular vectors ui and vi in their columns. ⌺E is a
diagonal matrix (Nc ⫻ R) containing the singular values of
E (i). The SVD-based pseudoinverse of E is then given by
冘
E† ⫽ VE
冘 冘
E
⫺1
E
UEH and the SENSE-SVD solution is repre-
sented as:
R
ŝ SVD ⫽ VE ¥E⫺1 UHE y ⫽ ¥i⫽1
uiH y
v.
i i
[5]
The SVD and the standard SENSE solution are equivalent.
Note that small values of i represent potential numerical
instabilities in the reconstruction. Since small singular
values will be inverted to large values, either noise or
systematic errors in sensitivity estimation affecting the
singular vectors associated with these small singular values will be amplified in the reconstruction, resulting in a
decreased SNR and residual aliasing artifacts. This situation is particularly evident in regions with overlapped
and/or low-value coil sensitivities where the coils are not
able to provide distinct information and the reconstruction
fails to remove the aliasing. If the number of singular
values is high, the truncated SVD solution (34) could be
used to eliminate the components responsible for noise
and error propagation by setting a minimum singular value
threshold. However, for SENSE we have only R singular
values, commonly R ⬍ 10, therefore it is not possible to
separate those components. Instead of truncating the SVD,
the set of singular values can be shifted away from zero
using a shift value given by a small portion of the largest
singular value, thus the solution components for large
singular values will remain similar to the nonshifted SVD
while the components corresponding to small singular
values will be attenuated. The shifted-SVD (SSVD) approach shifts the set of singular values away from zero
using a minimum singular value shift based on an upper
bound on the condition number (CN) of E (c0). The shifted
singular values are given by: is ⫽ i ⫹ ⌬ ⫽ i
⫹ max/0 , where max is the largest eigenvalue. The condition number is the ratio of the maximum to the minimum singular value. In this way the set of singular values
will be shifted away from zero by adding a minimum
singular value to improve the conditioning of E. The SSVD
of E is then ES ⫽ UE ¥Es VEH where ¥sE is a diagonal matrix
with the shifted singular values. Note that ES is a shifted
version of E. Since we have a large condition number (CN)
for an ill-conditioned matrix, e.g., CN ⬎ 1000, and c0 is
chosen in the range of a well-conditioned matrix, e.g., 10 ⬍
c0 ⬍ 100, the shift is very small for the largest singular
values but it is significant for the small singular values that
are responsible for numerical instabilities. If the encoding
matrix is well-conditioned, e.g., CN ⫽ 5, the Es remains
very close to E. The SENSE-SSVD solution is given by:
ŝ SSVD ⫽ VE
冘
s ⫺1
E
R
H
E
U y⫽
冘
i⫽1
uiH y
v.
i ⫹ ⌬ i
[6]
Note that the method acts like a filter, attenuating more the
effect of small singular values. For example, assuming that
c0 ⫽ 50, then ⌬ ⫽ 0.02 ⫻ max. For components with
i ⬎ 0.2max, the difference will be less than 10%. Components with i ⬇ ⌬ will be attenuated by a factor of 2.
Since small singular values correspond to more oscillatory
singular vectors, the method is attenuating fine details in
the reconstructed image. Therefore, with SSVD regularization we are improving the SNR and reducing the aliasing
artifact at the expense of blurring in regions susceptible to
numerical instability. The difference between the SVD and
the SSVD solution is given by:
R
ŝ SVD ⫺ ŝSSVD ⫽
冘
i⫽1
⌬ uiH y
v,
i i ⫹ ⌬ i
[7]
Note that for components with large singular values and
therefore small ⌬/i , the difference is very small.
Array geometry related noise amplification in the reconstruction was computed using the g-factor (11):
g共r兲 ⫽ 冑共ES H ES 兲r⫺1 共ES H ES 兲r.
[8]
The regularization procedure will reduce g-factor since it
improves the conditioning of the matrix E and consequently of EHE at the expense that certain features will be
omitted in the reconstructed image, e.g., blurring in positions with strongly overlapped coil sensitivities. There-
Accelerated 3D-PEPSI Using 2D-SENSE
fore, it allows a tunable tradeoff between ideal accuracy
and practical image quality and SNR. To choose the
threshold on CN, the reconstruction of the first time-domain point of the accelerated NWS was employed for
different values of c0 between 10 and 100 with steps of 5.
The value of c0 that proportioned the smallest root mean
square error (RMSE) was chosen. The RMSE is defined
with respect to the nonaccelerated data. For optimal results, c0 needs to be reoptimized for each geometry/subject, which does not represent an increase in acquisition
time, only extra processing time. For the matrix size used
in this work it took less than 1 min.
Coil Sensitivity Estimation
Coil sensitivity functions were estimated using spectral
water images from an extra fully sampled NWS acquisition
with fewer timepoints (17). The reference signal is appropriate since it was acquired with the same readout as the
accelerated PEPSI data, which is advantageous to avoid
spatial registration errors. Following the inverse Fourier
transform law, the integral along the spectral domain is
contained in the first temporal point, which was used as
the spectral water image for each coil. The change in
contrast due to a shorter TR and anatomical features were
reduced by normalizing the reference signal of each coil by
the sum-of-squares (SoS) reconstruction of the multicoil
reference data. The raw sensitivity maps are still impaired
by noise and present discontinuities at the object border.
Refinement of the raw sensitivity maps was performed by
extracting the low-frequency components using polynomial fitting and spatial extrapolation beyond the borders of
the object (11). A third order polynomial fit was employed.
Low Spatial-Resolution Effects
Reconstruction from truncated k-space data can be represented as the convolution of the true object function and
the PSF of the reconstruction method. The PSF is a spatial
weighting function that describes the signal origin in that
voxel. This aspect is particularly important for MRSI,
where the intrinsically low SNR requires a minimal voxel
size, or equivalently a maximal sampled spatial frequency,
resulting in truncation artifacts and a poor PSF. For truncated k-space data sampled at the Nyquist rate (no acceleration), the PSF limits the spatial resolution of the reconstructed image to its effective width and produces Gibbs
ringing due to its oscillatory nature. In MRSI, ringing can
produce strong contamination from lipid components located at the periphery of the brain, which have a much
larger concentration than the metabolites of interest. For
parallel imaging, where truncated k-space data are sampled at a multiple of the Nyquist rate, the PSF will be also
aliased, which may cause residual aliasing artifacts in
regions where the coil sensitivities are not well defined
(35). For example, due to the low spatial resolution of
MRSI the coil sensitivities are poorly defined at the periphery of the brain, where the lipids are located, which
cause extra lipid contamination inside the brain due to
improper unaliasing of the PSF. Coil sensitivity extrapolation following the third order polynomial fit was performed to avoid discontinuities at the border of the object.
1111
Moreover, the regularization method described above will
reduce the effect of inconsistencies between the extrapolation model and the actual coil sensitivities, thus providing an improved PSF. Data truncation effects for SENSE
reconstruction were evaluated by computing the PSF. The
PSF was computed by reconstructing a simulated source
point at a specific spatial position using coil sensitivity
profiles estimated with a 128 ⫻ 128 ⫻ 32 spatial grid to
have 4 points along each dimension within each voxel. No
spatial filter was employed to reconstruct the PSF.
Spectral Fitting, Metabolite Images, and Error
Quantification
Spectra were quantified using LCModel fitting (36). Basis
sets included the following 18 metabolites: aspartate
(Asp), glutathione (GSH), inositol (Ins), scyllo-inositol
(sIns), glucose (Glc), choline (Cho), phosphocholine
(PCho), glycerophosphocholine (GPC), creatine (Cr), phosphocreatine (PCr), glutamine (Gln), glutamate (Glu),
gamma-aminobutyrate (GABA), N-acetyl-aspartate (NAA),
N-acetyl-aspartylglutamate (NAAG), lactate (Lac), phosphoethanolamine (PE), and taurine (Tau). The basis sets
for LCModel were generated by simulating the spectral
pattern of each metabolite using density matrix simulations based on chemical-shift and J-coupling values (37).
The PEPSI sequence was approximated as a simple spinecho sequence without slice-selective gradients and assuming infinitely short RF pulses.
Spectra were fitted in the spectral range between 1.0 and
4.0 ppm. Metabolic concentration values in the reconstructed WS data were computed in reference to the NWS
data using the water-scaling method with the following
scale factors: water concentration ⫽ 55 molar and attenuation correction for water and metabolites ⫽ 1.0. Combined absolute concentrations in millimolar (mM) units
are reported in this work: NAA ⫽ NAA⫹NAAG, Cr ⫽
Cr⫹PCr, and Glu ⫽ Glu⫹Gln. Cho was represented by GPC
only. Metabolite concentrations were corrected for relaxation effects as described (8).
Errors in metabolite quantification in LCmodel (%SD)
are expressed in Cramer-Rao lower bound (CRLB, the lowest bound of the standard deviation of the estimated metabolite concentration expressed as percentage of this
concentration), which when multiplied by 2.0 represent
95% confidence intervals of the estimated concentration
values (36).
Metabolite concentration images with the spatial matrix
of the acquisition (32 ⫻ 32 ⫻ 8) were created using the
following thresholds to accept voxels: 1) CRLB ⱕ 20% for
NAA and Cr, CRLB ⱕ 30% for Cho, and CRLB ⱕ 50% for
Glu, and 2) spectral linewidth (FWHM) ⱕ 0.2 ppm. Error
maps were computed between the accelerated data reconstruction and the fully sampled data reconstruction using
the root mean square (RMS) value of the difference. Finally, the metabolite concentration maps were interpolated to a 128 ⫻ 128 ⫻ 8 matrix using zero-filling to
improve visualization.
Lipid images were created by spectral integration between 0.5 and 1.6 ppm of the reconstructed absorption
mode spectra.
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Otazo et al.
therefore the SSVD solution did not affect the reconstruction. Even though SENSE-SSVD provided low and more
uniform g-factors at high accelerations, the SNR penalty
due to highly undersampled data (公R-factor) imposed the
limit for the maximum feasible acceleration. SENSE-SSVD
reconstruction of the first time-domain point of the accelerated NWS data presented good performance up to R ⫽
4 ⫻ 2 (Fig. 4b). For R ⫽ 6 ⫻ 2, the reconstruction was
deteriorated due to high acceleration factor along the y
dimension.
Point Spread Function
FIG. 3. Average g-factor for simulated 1D and 2D accelerations
using the estimated sensitivity maps. For SENSE-SSVD (shifted
singular value decomposition), a threshold c0 ⫽ 25 on the condition
number of the encoding matrix was employed.
RESULTS
SSVD Reconstruction and g-Factor
The g-factor obtained from 1D acceleration was reduced
considerably by 2D acceleration (Fig. 3). As a result, the
average SNR decrease given by g 公R where g is the average
g-factor for R ⫽ 8 ⫻ 1 is 12.6 but only 4.4 for R ⫽ 4 ⫻ 2.
Therefore, there is a 2.8-fold gain in average SNR performance when using 2D-SENSE for an 8-fold acceleration.
However, for high accelerations (R ⫽ 4 ⫻ 2 and R ⫽ 6 ⫻ 2),
g-factor still presented large values at central zones (Fig.
4a). SSVD reconstruction reduced g-factor for R ⫽ 4 ⫻ 2
and R ⫽ 6 ⫻ 2 specially in central zones where the coil
sensitivities have low value and overlap, thus producing
an ill-conditioned encoding matrix. The threshold of c0 ⫽
25 on the condition number of the encoding matrix represented a good tradeoff to achieve both reasonable numerical conditioning and good unaliasing performance. For
R ⫽ 2 ⫻ 2 the encoding matrix was well-conditioned and
2D-SENSE reconstruction of the accelerated data with coil
sensitivity extrapolation beyond the border of the brain
provided a properly unaliased PSF (Fig. 5). Without sensitivity extrapolation, SENSE reconstruction may lead to
residual aliasing artifacts due to discontinuities at the object border. The SSVD solution improved aliasing suppression for larger accelerations (R ⫽ 4 ⫻ 2 and R ⫽ 6 ⫻ 2). The
aliasing peaks along the y dimension were reduced by
35 dB approximately for Ry ⫽ 2 and Ry ⫽ 4. For Ry ⫽ 6, the
aliasing peak to the left of the signal peak was poorly
suppressed if the SSVD was not used. With SSVD the
aliasing peaks for Ry ⫽ 6 were at least reduced by 20 dB.
The aliasing peak along the z dimension was reduced by
20 dB approximately for Rz ⫽ 2. The PSF for SENSE
reconstruction is asymmetric which is due in part to asymmetries in the array coil configuration. Note that for R ⫽
1 ⫻ 1 (nonaccelerated data), the PSF for SENSE reconstruction presented lower side lobes than the one for SoS
reconstruction, which results in reduced contamination
from outside voxels. This is due to the better defined coil
sensitivity functions used in SENSE. For SoS reconstruction, coil sensitivities are assumed to be equal to the spatial SNR profiles of the data; for SENSE a polynomial fit
was employed.
Metabolite Maps and Spectra
Metabolite concentration mapping was feasible up to R ⫽
4 ⫻ 2 (1-min acquisition) with a moderate reduction in
FIG. 4. a: g-factor maps for slices
2–7 and b: reconstruction of the
first time-domain point of the
NWS data from slice 5 for different accelerations. The threshold
on CN was set using the reconstruction with R ⫽ 4 ⫻ 2 (c0 ⫽ 25).
RMSE is the average RMS error
with respect to the nonaccelerated data (R ⫽ 1 ⫻ 1). Note the
reduction of g-factor for the SSVD
reconstruction, which reduces
noise in the reconstructed spectra. R ⫽ 2 ⫻ 2 and R ⫽ 4 ⫻ 2
presented similar reconstruction
to the nonaccelerated data. For
R ⫽ 6 ⫻ 2, even though the gfactor was reduced considerably
using SSVD reconstruction, the
SNR reduction due to 公R-factor
deteriorated the reconstruction.
Accelerated 3D-PEPSI Using 2D-SENSE
1113
contamination that could have happened due to residual
aliasing was highly reduced by extrapolation of the sensitivity maps and the SSVS reconstruction. Figure 9a shows
lipid maps for slice 5 from the nonaccelerated data (R ⫽
1 ⫻ 1) and R ⫽ 4 ⫻ 2 using standard SENSE (LS) and SSVD
reconstruction. Accelerated data reconstruction showed
lipid contamination due to residual aliasing especially in
central zones where the encoding matrix is ill-conditioned
as shown in the g-factor maps in Fig. 4a. On average the
lipid contamination in standard SENSE reconstruction
was reduced by a factor of 1.6 when using SSVD reconstruction due to improved unaliasing of the PSF. Figure 9b
shows an example from a white matter region where the
lipid contamination was reduced by a factor of 1.8.
DISCUSSION
FIG. 5. Point spread function (PSF) along y (a) and z (b) for different
accelerations. The source point was located at the border of the
brain, where there is a discontinuity of the reference signal to
estimate the coil sensitivities. The top row shows the aliased PSF for
SoS reconstruction of the accelerated data and the bottom row the
unaliased PSF for SENSE reconstruction. Note that the reconstruction using SSVD presented better aliasing suppression.
In this work we demonstrated feasibility of a very highspeed method for volumetric spectroscopic imaging in
human brain using a combination of highly accelerated
PEPSI and 2D-SENSE with a 32-element array coil. We
show that the acquisition of short TE (15 ms) 3D-PEPSI
with a 32 ⫻ 32 ⫻ 8 spatial matrix can be accelerated up to
1 min to map the concentrations of NAA, Cr, Cho, and Glu
at the expense of a moderate reduction in spatial-spectral
quality. This short acquisition time constitutes a major
advance as compared to previous studies using parallel
MRSI, such as data presented in Ref. 16 that required
20 min of encoding time for the same spatial matrix. Large
accelerations in parallel imaging comes with a spatially
spatial-spectral quality when compared to the nonaccelerated acquisition: RMSEs were less than 0.3%; and the
increase in average CRLBs were 5.1, 6.8, 10.7, and 13.9 for
NAA, Cr, Cho, and Glu, respectively (Fig. 8a). R ⫽ 2 ⫻ 2
(2-min acquisition) presented similar results to the nonaccelerated acquisition: RMSEs were less than 0.5%; and the
increase in average CRLBs were 2.0, 2.5, 5.6, and 5.4 for
NAA, Cr, Cho, and Glu, respectively (Fig. 8b). Figure 6
shows the concentration maps for the three major single
resonances NAA, Cr, and Cho. Figure 7a shows the corresponding results for Glu (a multiplet resonance with comparatively low sensitivity). Table 1 shows average concentration values for each acceleration, which are within the
range of concentration values reported in previous studies
(38). The inferior slices (2 and 3) suffered from larger
errors since the coil sensitivities are lower in those brain
regions. The accuracy of spectral quantification, indicated
by the CRLB from LCModel fitting, decreased with acceleration due to reduced SNR (Fig. 8b). This situation is
particularly evident in the inferior slices for the reason
mentioned above. The Glu image shows similar intensity
in central and lateral gray matter (GM), and much lower
intensity in white matter (WM) (⬇50% less in the voxel
from Fig. 7a), consistent with previous studies (ratio
Glu(GM)/Glu(WM) ⫽ 2.4⫾0.5 (38)). Examples of spectra
show decreased SNR with larger acceleration, as expected,
and small distortions around the lipid region (1.3 ppm)
due to imperfections in the estimation of coil sensitivity
information at the periphery (Fig. 7b). However, extra lipid
FIG. 6. Metabolite concentration maps of (a) NAA, (b) Cr, and (c)
Cho for slices 2–7.
1114
Otazo et al.
FIG. 7. a: Glutamate concentration maps at different accelerations for slices 4 –7. b: Raw absorption mode spectrum (thin
black line) and corresponding LCModel fit (bold black line) for a
gray matter (GM) voxel and a
white matter (WM) voxel (voxel locations are indicated in part a).
The remaining baseline is given
by the smooth curve. The concentration of glutamate is given in
each case.
varying reduction in SNR, which is due to the acquisition
of fewer phase encoding steps (公R-factor) and noise amplification in the reconstruction due to ill-conditioning of
the encoding matrix (g-factor) (11). A high acceleration
factor (R ⫽ 8) was achieved by using 2D acceleration (Ry ⫽
4 and Rz ⫽ 2), a large array coil of 32 elements and
regularization in the SENSE reconstruction.
Acceleration applied simultaneously to the ky and kz
phase-encoding dimensions increased the acceleration capability by reducing the large SNR loss from high 1D
accelerations. The conditioning of the reconstruction improved considerably by exploiting sensitivity encoding
FIG. 8. a: RMS error of the concentrations of NAA, Cr, Cho, and
Glu from the accelerated data reconstruction with respect to the
acceleration factor. The RMSE is
computed with respect to the
concentration of the fully sampled
data reconstruction. b: Spectral
fitting error given by the average
CRLB (Cramer-Rao lower bound)
from LCModel fitting with respect
to the acceleration factor for NAA,
Cr, Cho, and Glu.
along two dimensions and therefore reduced geometry
related noise amplification as described in Ref. 21. The
difference between 1D-SENSE and 2D-SENSE for the same
net acceleration factor is given by the g-factor. 2D-SENSE
presents a lower and more spatially uniform g-factor than
1D-SENSE (see Figs. 3, 4). For example, the average SNR
decrease for R ⫽ 8 ⫻ 1 is 12.6 but only 4.4 for R ⫽ 4 ⫻ 2.
Therefore, there is a 2.8-fold gain in average SNR performance when using 2D-SENSE in this work for an 8-fold
acceleration.
Even though the combination of the 32-element array
and 2D acceleration improved the conditioning of the en-
Accelerated 3D-PEPSI Using 2D-SENSE
1115
Table 1
Average Absolute Concentration and Standard Deviation for
Different Accelerations
Absolute concentration [mM]
Metabolite
NAA
Cr
Cho
Glu
R⫽1⫻1
R⫽2⫻2
R⫽4⫻2
9.2 ⫾ 3.4
7.3 ⫾ 2.4
1.6 ⫾ 0.7
8.3 ⫾ 3.6
9.4 ⫾ 3.8
7.2 ⫾ 2.2
1.4 ⫾ 0.8
8.5 ⫾ 3.9
9.5 ⫾ 4.3
7.2 ⫾ 2.8
1.4 ⫾ 0.9
8.8 ⫾ 5.2
coding matrix, the g-factor was still high in positions with
low value and overlapped coil sensitivities. When the
sensitivities of the receiver coils severely overlap, different
rows of the encoding matrix become nearly identical. This
causes the encoding matrix to become nearly singular and
therefore highly susceptible to amplify noise and errors in
the coil sensitivities estimation process associated with
small singular values. Regularization of the encoding matrix inversion can be performed by constraining the SVD
solution. One possible strategy is to truncate the singular
values that are lower than a certain threshold. This solution, known as truncated SVD, can be applied when the
encoding matrix is sufficiently large so that the fraction of
eigenvalues retained still provides a reasonable approximation to the true solution, e.g., SPACE-RIP method (34).
For SENSE, a small encoding matrix is used for each
position in the aliased images, therefore the number of
singular values is very small and truncated SVD is not an
adequate approach. In this work we proposed the SSVD
solution, which consists of shifting the set of singular
values of the encoding matrix away from zero based on a
threshold on the condition number (SSVD solution). The
method proved to work adequately for SENSE to improve
the conditioning of the encoding matrix in positions with
low value and overlapped coil sensitivities at the expense
of spatial resolution in central regions of the reconstructed
image. Since MRSI is intrinsically a low spatial resolution
technique, this effect is small. The SSVD was tuned using
the first time-domain point of the NWS data (maximum
SNR), and the same procedure was applied to later timepoints which are noisy.
The analysis of the PSF revealed the parameters to optimize the sensitivity estimation process. The use of sensitivity extrapolation beyond the borders of the object
highly reduced the aliasing peak from positions close to
the border of the brain, consistent with previous findings
(12), and SSVD reconstruction provided better aliasing
suppression at large acceleration factors for positions with
overlapped coil sensitivities. These two factors were very
important to reduce extracranial lipid contamination due
to residual aliasing. Lipid signals are particularly strong at
short TE, which is advantageous to maintain the sensitivity advantage at high field and to improve the detection of
J-coupled metabolites. The sensitivity profiles were fitted
using a third-order polynomial function, which provided a
better representation of this array coil than the commonly
used second-order polynomial function. SSVD reconstruction reduced the lipid contamination inside the brain as
compared to standard SENSE reconstruction by a factor of
1.6 on average due to better aliasing suppression in regions
with overlapped coil sensitivities.
The maximum attainable acceleration was evaluated
quantitatively using the RMSE of the metabolite concentration with respect to the nonaccelerated acquisition and
the CRLB from LCModel spectral fitting. The CRLB represents the combined influence of SNR, spectral line width,
and spectral shape on the accuracy of the fit. We also
generated RMSE maps to evaluate qualitatively the spatial
homogeneity of the concentration maps. Based on these
parameters, R ⫽ 4 ⫻ 2 presented an acceptable reduction
in spatial-spectral quality to map the concentrations of
NAA, Cr, Cho, and Glu.
The short acquisition time of the method is advantageous for applications using hyperpolarized contrast
agents (39), allowing highly accelerated acquisition of
large data volumes during the short duration of enhanced
polarization. Also, the use of high magnetic field strength
has been shown to improve the performance of parallel
imaging by increasing the baseline SNR and providing
stronger sensitivity encoding (22). These two factors promise to shift the SNR balance for highly parallel MRI and
perhaps pay the price for even larger array coils. In future
work we are planning to implement the technique at 7 T
using a 32-element array. However, in order to take advantage of the larger acceleration capability and to achieve
adequate volume coverage for 3D acquisitions it is necessary to maximize the uniformity of the spectral quality
using improved volumetric shim algorithms and automatic placement of the OVS slices. We are in the process of
implementing automatic positioning of the OVS slices for
3D experiments using lipid masks from high-resolution
images (40).
In conclusion, this work extended our previously introduced PEPSI-SENSE method (17) for fast 3D-MRSI. A high
2D acceleration was achieved by using a large array coil
FIG. 9. a: Lipid image from slice 5 for R ⫽ 1 ⫻ 1 and R ⫽ 4 ⫻ 2 using
standard SENSE (LS) and SSVD reconstruction. b: Absorption
mode spectrum from the voxel indicated in part a. Note the reduction in lipid contamination due to residual aliasing in areas with high
g-factor seen in Fig. 4b when using SSVD reconstruction as compared to standard SENSE reconstruction.
1116
and SVD-based regularization in SENSE reconstruction. In
vivo results demonstrated that single-average 3D-MRSI
with a 32 ⫻ 32 ⫻ 8 spatial matrix and 1.8 cc effective voxel
size at 3 T can be accelerated with minimum acquisition
time of 1 min. SVD-based regularization in SENSE reconstruction leads to improved conditioning of the reconstruction in regions with low value and overlapped coil
sensitivities and thus reduction of lipid contamination
inside the brain from peripheral regions. The short acquisition time makes the method suitable for volumetric mapping of metabolites (including J-coupled) as an add-on in
clinical MR studies.
ACKNOWLEDGMENTS
We thank Graham G. Wiggins and Lawrence Wald (Massachusetts General Hospital) for making the array coil
available for this study. We also thank Pierre-Gilles Henry
and Malgorzata Marjanska (University of Minnesota) for
the simulation of basis sets, and Ramiro Jordan (University
of New Mexico) for supporting this project.
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