M R I Physics Course Multichannel Technology

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M R I Physics CourseMultichannel Technology
Parallel Imaging

• Nathan Yanasak, Ph.D.
• Jerry Allison Ph.D.
• Tom Lavin, B.S.
• Department of Radiology
• Medical College of Georgia

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1) The Physics of Clinical MR, for
Neuroradiology, Taught Through Images
References

• AUTHORS VAL M. RUNGE1 MD, WOLFGANG R. NITZ2
  PHD, STUART H. SCHMEETS2 BS, RT, WILLIAM H.
  FAULKNER, JR.3 BS, RT, NILESH K. DESAI1 MD

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Multichannel Coil Technology (basics)

• Radiological Wish List for MR (and perhaps,
  other modalities as well)
• Higher spatial resolution
• Decreased acquisition time
• Higher signal to noise ratio (SNR)
• More images per patient for more diagnostic
  information
• Minimize SAR (problem with high-field MR)

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Multichannel Coil (cont.)

• Some factors to help meet these needs
• 1) Protocol/Pulse-sequence optimization
• 2) Faster image reconstruction hardware
• but also
• 3) Single-element coil increasing SNR requires
  increased acquisition time
• 4) Receiving coil element size decrease ?
  increased SNR per volume, but smaller tissue
  volume
• 5) Tissue proximity decrease ? increased SNR
• But Single-element coil one receive channel
  slow dataflow.
• Solution
• Number of RF receive channels increase ?
  decreased acquisition time

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Multichannel Coil (cont.)

• Circularly-polarized (CP) coil led to a 40
  increase in SNR (two-element coil).
• Also called a quadrature coil.
• Recent development Multichannel technology
• Coil uses multiple elements (loops) in phased
  array with overlapping anatomical coverage
• Each element acquires MR signals
  from the entire region.
• Highest signal in closest
  proximity to the element.
• Small element size ? higher
  received signal / higher
• overall signal
• RF hardware uses multiple channels for receiving
  signal from multiple elements.

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Multichannel Coil (cont.)

• Putting the coil and RF system together
• Signal from each element is (ideally) transferred
  through its own high-bandwidth RF channel.
• Reconstruction corrects element-to-element signal
  variations before forming the final image.
• Advanced reconstruction and storage hardware
  necessary to process the rapid inflow of
  information.

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Multichannel Coil (cont.)

• Figure 1 shows element arrangement for an
  8-element head coil, and images acquired from
  each element, together with the final single
  combined image.
• Note that images from each surface element show
  greater sensitivity near the element.

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The scan illustrated is a fat suppressed FLAIR
from a patient with brain metastases, with the
edema from a metastasis just superior and
posterior to the lateral ventricles visualized.
Figure 1 (ref 1)
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Multichannel Coil (cont.)

• Commercially available, eight-channel coil
  for brain imaging. (MRI Devices Corporation,
  Waukesha, WI).
• We use this coil with our GE 3T.
• Ref. 1

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Multichannel Coil (cont.)

• Figure 2 shows element arrangement for a
  12-element body coil, with six of the elements
  activated, and images acquired from each element
  together with the final single combined image.
• Typically, the scanner software routinely
  reconstructs the signal from all elements and
  provides only the combined image. Note that
  images from each surface element show greater
  sensitivity near the element.

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The scan sequence employed was trueFISP with
spectral fat saturation. This image depicts two
large liver hemangiomas.
Figure 2 (Ref 2)
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Multichannel Coil Example 1

• Case study Axial T1- and T2-weighted images of
  the brain
• Figures 3A,C (on left) use a standard CP coil
• Figures 3B,D (on right) use an eight element,
  phased array coil
• MR system uses eight, high-bandwidth, RF receive
  channels
• All pulse-sequence parameters same between left
  and right figures.
• T1 comparison shows increase in SNR, improved
  gray versus white matter distinction, and
  improvement in anatomic definition (e.g.,
  cortical gyri).
• T2 comparison shows increase in SNR, improved
  definition of gray matter, and improved
  visualization of the gray matter nuclei.

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Figures 3A,B,C,D (Ref 1)
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Multichannel Coil (cont.)

• Clinical benefit of multichannel technology
• Higher SNR achieved with multichannel
  technology allows greater flexibility in sequence
  parameter selection.
• If SNR is higher than needed, we can afford to
  lose a little SNR to gain
• An increase in spatial resolution
• A reduction in acquisition time (e.g., minimize
  motion-induced artifacts, increase number of
  images per exam).

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Multichannel Coil (cont.)

• Advancements in multi-element/multichannel
  technology (to 32 elements and beyond) will
  continue to play a role in the development of
  imaging techniques with higher spatial
  resolution, faster scan times, and increased
  diagnostic quality.
• MCG has an 8-element GE 3T scanner (Sept. 2005)
• 2) UGA has GE 3T research scanner (8-16 channels
  summer 2006).

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Multichannel Coil (cont.)
Advancements in multi-element/multichannel
coils New 96-channel head coil (Wald,
MGH) High-field imaging with 8-channel coil

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Parallel Imaging
As shown previously, no image from a single
surface coil element is optimally sensitive over
the whole area. However, an image reconstructed
from all coil elements leads to an increased SNR
over a standard acquisition, because each region
of the image is reasonably sampled by more than
one element. If SNR is higher than needed, one
can use the technique of parallel imaging to
increase acquisition speed. How? We can decrease
sampling of data by each element receiver. Also,
reduced sampling ? less RF excitations per unit
time ? lower SAR. Decrease sampling of data
decreased k-space sampling

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Parallel Imaging
Rather than fill all of k-space, parallel imaging
acquires a fraction of k-space to save time.
Because the anatomy is sampled by multiple coil
elements, we can reconstruct the missing
information (more or less). Less samples, of
course, leads to decreased SNR. But, if our
multi-channel SNR is better than we
diagnostically require, so what?
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Parallel Imaging
How fast can we go? If we have M coil elements
covering the FOV, we can skip up to M-1 lines for
each line in k-space we sample. The number of
lines skipped acceleration factor (R). This
can be fractional as well
of phase-encodes to cover k-space R
of
phase-encodes used in acquisition Names for
acceleration factors iPAT factor (Siemens)
SENSE factor (Philips) ASSET
factor (GE) Parallel systems iPAT (Siemens)
SENSE (Philips) ASSET (GE)
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Parallel Imaging
Example 1 We have an 8-element phased-array
head coil. We want an acquisition matrix of 256
x 256. What is the maximum acceleration factor
we can achieve? Answer If we have M elements,
we can skip up to M-1 lines in k-space. So, M8,
and M-17. In the case of this acceleration, for
each 8 lines within k-space, we are acquiring
only 1 of these line. of
phase-encodes to cover k-space R
of
phase-encodes used in acquisition
256 phase encodes / (1 acquired line/8
lines) 256 8
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Parallel Imaging
Increasing acceleration leads to decreasing SNR.
However, the benefits may be greater than saving
time as well. For EPI images, which are
greatly affected by susceptibility differences,
parallel imaging can improve geometric distortion
and/or image voids. Because the gradients are
switching so quickly for an EPI image, one can
accrue errors that lead to distortion. These are
alleviated using parallel imaging, where the
sequence requires less lines in k-space to be
read out.
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Parallel Imaging
Example of Parallel Acceleration on the GE 3T
R1 R2.0 R2.8 R3.2
R4.0
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SNR vs. Acceleration
Short-axis cardiac images 32-channel coil 1.5
T magnet
Reeder SB et al. MRM 54748, 2005
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Parallel Imaging (cont.)
Spatial coil sensitivity function describing
the sensitivity of the coil element at any
particular position in the FOV. (Ref. 2)
Total
C1
C2
R
L

Both types of parallel imaging techniques rely on
this function.
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Parallel Imaging (cont.)
How is the spatial sensitivity measured?
a
b
c
d
Method 1 Acquire quick images from each element
(a) and reconstruct the full image using all
elements (b). Image (a) divided by (b) gives a
noisy sensitivity map (c). Filtering smoothes
out the noise, yielding our sensitivity map
(d). In short, with this method, one must
acquire a map before running a parallel imaging
sequence. Takes a minute or so. If one uses the
summed image from all elements as a reference,
this technique is called Auto-SENSE.

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Parallel Imaging (cont.)
Method 2 During the parallel scan, we can
acquire extra data in the very center of k-space,
using the number of phase encodes in this region
that we would have used without parallel imaging.
Because the center of k-space is responsible
for low spatial resolution, this will also give
you spatial sensitivity maps for each coil
element. (This is the basis of AUTO-SMASH,
VD-AUTO-SMASH, and GRAPPA).
Key Whitefilled part of
k-space Blackunfilled k-space

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Parallel Imaging
Two main types of parallel imaging image based
reconstructionSENSE, mSENSE, ASSET
k-space based reconstructionSMASH, GRAPPA
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Parallel Imaging (Image Based Reconstruction)
of encodes

• Image-based reconstruction is, in principle,
  easier to understand than k-space-based
  reconstruction.
• If data is acquired with less phase encodes than
  will fill k-space, the reconstructed image will
  show aliasing.
• Weve seen this before less phase encodes in the
  same region of k-space? smaller FOV. If FOV is
  smaller than the object, we get aliasing.
• (from Boesiger Pruessmann, http//www.mr.ethz.c
  h/sense/sense_method.html)

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128
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Parallel Imaging (Image Based Reconstruction)

• Each pixel in the aliased image (Ialias) is
  comprised of overlapping (or summed) data from
  two or more pixels in the unaliased image (I1,
  I2, ).
• Use the spatial sensitivity function for each
  coil element to reconstruct the image intensity
  uniquely at each position.

(from Boesiger Pruessmann, http//www.mr.ethz.c
h/sense/sense_method.html)
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A Simplistic SENSE Example
Ialias,1s1,AIA s1,BIB
A
s1
IA
Ialias,1
B
IB
A
s2
Ialias,2
B
Ialias,2s2,AIA s2,BIB

• We know s1, s2 (sensitivity maps) we measure
  Ialias,1, Ialias,2 so we can calculate IA, IB.

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Parallel Imaging (k-space Reconstruction)

• Lets review some topics quickly again
• What does k-space really represent (we know that
  MRI collects data in k-space before
  reconstructing an image) ?
• 2) What is the relationship between spatial
  structure in an image and waves ?

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Parallel Imaging (k-space basics)
Remember we can decompose a complicated 1-D wave
into a combination of simple waves of a given
frequency.
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Parallel Imaging (k-space basics)
For each simple component, if we know the
amplitude
and the phase, we can construct a unique wave.
?
?
?
Amplitude change
Phase change
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Heres the representation of the waveform as a
plot of amplitudes and phases
Complicated Wave representation
f
amplitude/phase representation
x
phase
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Can transform back and forth. Now, lets look at
2D waves
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Fourier Transform Basics
Complicated Wave representation
K-space representation
y
x
Can transform back and forth. K-space is just a
2D (or 3D) version of the amplitude/phase
representation.
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Parallel Imaging (k-space method)
A
P
Back to 1-D again, for simplicity (i.e., the A-P
axis) Spatial sensitivities for each
element of a multi-element coil are periodically
distributed across the FOV. This picture shows
three spatial sensitivity functions spanning our
imaging FOV. So, each element is sensitive to
signal in a particular location.
Coil 2
Coil 3
Coil 1
C1
C3
C2
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Parallel Imaging (k-space method)
We can use each individual coil
sensitivity to our advantage. We can examine the
signals across a full field of view by combining
the signals in some proportion from each
coil. Depending on how we combine the signals
(add or subtract) from each coil into the total,
we can enhance or suppress our sensitivity to
signals of different spatial variation. Each
combination of coil signals would result in an
effective sensitivity across the full FOV.
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Parallel Imaging (k-space method)
The black curves represent two of the effective
sensitivities using elements in this example.
The upper combination is sensitive to these
spatial variations
Fundamental CA (add signal for all three)
First Harmonic CB (subtract middle signal)
while the lower combination is sensitive to
these spatial variations
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Parallel Imaging (k-space method)
So, each combination is sensitive to spatial
variations of different wavelength, or spatial
harmonics.
This modulation of sensitivity across the FOV
mimics modulation of spatial sensitivity by
phase-encode gradients. If our acceleration
factor is R, we have to reconstruct R of these
harmonics, by using R combinations of coil
signals.
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Parallel Imaging (k-space method)
(from Sodickson, et al., MRM 41 1009, 1999)

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Parallel Imaging (k-space method)
How are the missing k-space lines filled in? 1)
Spatial sensitivities of each coil are
determined. 2) Given an effective
sensitivity that we wish to calculate (i.e.,
sensitivity across the FOV to a sinusoid of a
particular periodicity), the actual sensitivities
require summation in particular proportions.
So, we use the desired effective sensitivity
and the measured sensitivities to determine the
necessary proportions. 3) The missing k-space
lines are calculated by summing k-space data
from each coil element, using the proportions
determined in the first step.

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Parallel Imaging (k-space method)
Example The fundamental effective sensitivity
covers the original lines in k-space Data for
the first harmonic is shifted up a line in
k-space.

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Parallel Imaging and Noise

• Noise in parallel images is 1) increased, and 2)
  non-uniform.
• As shown in this SENSE example, unfolding the
  alias multiplies the noise within particular
  regions (non-uniform).
• A similar effect appears in k-space based methods.

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Larkman DJ et al. Magn Reson Med 2006 55153-160
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Parallel Imaging (k-space Example)

• K-space oriented parallel acquisition
  techniques
• SMASH (Simultaneous Acquisition of Spatial
  Harmonics), AUTO-SMASH
• PILS (Parallel Imaging with Localized
  Sensitivities)
• GRAPPA (Generalized Autocalibrating
  Partially Parallel Acquisition).
• Figure 5 shows fast spin echo T2 weighted
  sagittal scans of the lumbar spine, without (A)
  and with (B) parallel imaging (the latter using
  GRAPPA).
• In (B), every second Fourier line has been
  skipped (an iPAT factor of 2, or acceleration
  factor). Scan time is thus reduced by a factor of
  two (comparing B to A).

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Figures 5A,B (Ref 2)
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Parallel Imaging (Image Based Reconstruction)

• Image-based reconstruction parallel acquisition
  techniques
• SENSE (SENSitivity Encoding).
• Figure 6 shows fast spin echo T2-weighted
  sagittal scan of the lumbar spine , without (A)
  and with (B) parallel imaging (using SENSE)
• In (B) every second Fourier line (parallel
  imaging with an IPAT factor of 2). Thus the scan
  time for (B) is half that of (A). Note that
  there are residual wrap around artifacts (arrow,
  B), a major drawback to the use of image-based
  reconstruction technique when anatomy is larger
  than FOV.

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Figures 6A,B (Ref. 2)
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Parallel Imaging (Drawbacks)

• Image-based reconstruction If an aliasing
  artifact would be present in the chosen FOV for a
  non-parallel image sequence, then this aliasing
  will cause reconstruction problems if parallel
  imaging is attempted.
• K-space based reconstruction The ability to
  construct effective sensitivities from the
  spatial sensitivities for each coil element
  depends on the sensitivity profile. This, in
  turn, depends on the coil element design
  therefore, coil design is more critical with this
  technique.

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GRAPPA (k-space)
SENSE (image)
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When Should You Use Parallel MR Imaging?

• To reduce total scan time
• To speed up single-shot MRI methods
• To reduce TE on long echo-train methods
• To mitigate susceptibility, chemical shift and
  other artifacts (may cause others)
• To decrease RF heating (SAR) by minimizing number
  of RF pulses

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USE 1 Reduction of SAR in body imaging

• Case study Breath-hold T2-weighted abdominal
  scans.
• Figure 4A 17 second T2-weighted breath-hold
  acquisition, using 29 echoes
• Figure 4B 17 second T2-weighted breath-hold
  acquisition, using 19 echoes.
• The missing Fourier lines for B were
  reconstructed using parallel imaging.
• Use of parallel imaging can be used to reduce
  the echo train length while keeping scan time the
  same (SAR reduction).
• Use of a shorter echo train ? more slices can
  be acquired within the same scan time, or overall
  time can be reduced.
• Also, less T2-blurring and motion artifact.

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Figures 4A,B (Ref 2)
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Use 2 Reduction of T2-blurring

• Parallel imaging reduces T2-blurring because
  the readout time is shorter? resolution is better.

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Augustin Me et al. Top Magn Reson Imag 2004
15207
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Use 3 Reduction of Susceptibility Artifact
(EPI)

• Parallel imaging reduces number of phase-encoding
  steps required per imaging time
• Top normal acquisition,
• Bottom R2 acceleration

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Other Current Uses

• Contrast enhanced MR (e.g., MRA)
• Improved spatial resolution for a given scan
  time.
• Cardiac MRI

R 2 6 heartbeats
R 3 4 heartbeats
R 4 3 heartbeats
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(Not-so-distant) Future uses of parallel imaging
2D acceleration
2D SENSE reconstruction (2X in L-R and 2X in A-P)
from an 8-channel head array coil conjugated
gradient iterative solver after 10 iterations .
http//www.nmr.mgh.harvard.edu/fhlin/tool_sense.h
tm