Recent advances in Magnetic Resonance Imaging Peter Fransson MR Research Center, Cognitive...
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Transcript of Recent advances in Magnetic Resonance Imaging Peter Fransson MR Research Center, Cognitive...
Recent advances in Magnetic Resonance Imaging
Peter Fransson
MR Research Center, Cognitive Neurophysiology
Dept. of Clinical Neuroscience, Karolinska Institute
Overview
• Brief recap: MRI Physics
• Image acquisition speed is of essence…
• Functional Magnetic Resonance Imaging
• Diffusion tensor MRI, MR tractography
• Parallel Magnetic Resonance Imaging
• Outlook
Physical principles of NMR (very briefly)
Proton spin angular momentum:
Magnetic dipole moment:
I
I 1/Hz T
External magnetic field: 0 0B zB e
Energy levels are split (Zeeman effect):0hB
B0
E , anti-parallel spin
, parallel spin
E02
0
( 1)
3 z
h j jM B e
kT
Physical principles of NMR (very briefly)
Motion of spins in an external magnetic field:d
Mdt
0 1B B B
NMR experiment: static field and radiofrequency (RF) field):
In a rotating frame of reference with the angular frequency0
0
1
0
0
1/
dM RM M
dtT
2
2
1
1/ 0 0
0 1/ 0
0 0 1/
T
R T
T
B0
B1
M
M0
xy
Spatial localization in MRI
• Let the magnetic field vary in x, y and z-space.
x
Gx0 ( ))xG x
0
MR IMAGING IN 1973
P.C. Lauterbur, Nature, 242:190-191, 1973
xk
yk
2D -FFT
”k-space” ”reconstructed image”
CONVERTING FREQUENCIES INTO SPATIAL LOCATIONS
1
2
1 2
xG
0
0
TRAVEL IN ”K-SPACE” WITH THE SPIN ECHO SEQUENCE
180
RF
zG
xG
yG
ECHO
xk
yk
1
1
2
2 3
3
4
4
• The gradients permits sampling of points in k-space.
900
• Each echo gives us one line in k-space.
• Scan time: TR x N_phase
RF
SIGNAL
zG
xG
yG
Conventional gradient echo image acquisition
0.1-0.2 slices / secondN times
N excitations / image
yk
xk
TEeff
Echo Planar Imaging sequence
RF
SIGNAL
90o
xG
yG
zG
EPI image acquisition
xG
xk
yk
SIGNAL
yG
*2T
t
e
EPI and T2*-sensitivity
64 echoes or more are acquired per image
EPI is strongly sensitive to variabilities in the magnetic field (T2*)
xG
yG
(Gradient) Echo planar imaging
TR/TE/flip = 3000ms/40ms/90deg
3.4 x 3.4 x 4 mm3, 30 slices
T2*-weighted image contrast
1.5T GE Twinspeed Excite MR scanner
1.5T GE Twinspeed Excite MR scanner
1.5 Tesla Excite Twinspeed GE MR scanner - console
Functional Magnetic Resonance Imaging
Hypothesis on brain function
Paradigm design
Physiological and metabolic responses
Signal changes in the MR image
Post processing / statistical analysis
Visualisation / Activation maps
HEMOGLOBIN
• 4 subunits, each carrying a heme (red)
• one iron atom (Fe2+ ) is carried by each heme
• to each heme an oxygen molecule can be attached
• with oxygen : oxy-hemoglobin
• without oxygen : deoxy-hemoglobin
Oxy-hemoglobin
Slightly diamagnetic, same as the surrounding tissue
Deoxy-hemoglobin
Paramagnetic, susceptibility difference:
ppm
0B
0B
0B
r
a
)2cos()(sin' 22 r
ar
cos('
Outside ”vessel”:
Inside ”vessel”:
')' Y
The BOLD effect - theoretically
Magnetic field distortions:
Bandettini & Wong, Intern. J. of Imag. Syst. And Techn. 6:133, (1995)
Oxygen saturation and magnetic susceptibility
Historical background (II): Initial observations
• Ogawa (1990):
• Gradient echo imaging (T2*-sensitivity) of mouse brain at 7T
• Changed inhalation gas from 100% to 20% oxygen (room air)
• Observed a signal decrease in the vicinity of vessels (reversable)
• No signal change in corresponding spin echo images (T2-sensitivity)
Conclusion: Signal decrease is due to increased magnetic field inhomogeneities caused by an increase in the concentration of paramagnetic deoxy-Hb.
Cerebral blood oxygenation (CBO)
Signal change in T2*-sensitized MR images
BOLD - Blood Oxygenation Level Dependent
Hemodynamic response function (hrf)
rCBF and rCMRO2 mismatch
Neuronal activity
CBF
CMRO2
BOLD effect
t0s 30s 60s 90s 120s 150s 180s 210s 240s 270s
ON ON ON ONOFF OFF OFF OFF OFF
Continuous EPI image acquisition
fMRI - Blocked design
OFF: ON:
fMRI – Blocked design
T2*-weighted image Activation map, p<0.001
• 2T, blipped EPI: TR/TE/flip = 400ms / 54ms / 30 degrees
• 10s reversing checkerboard / 20s fixation cross, 6 repetitions
• Anatomy: RF-spoiled gradient eko (FLASH) ,TR/TE/flip = 70ms / 6ms / 60 deg.
Blocked fMRI signal intensity time course
Cavernoma
Self-paced fingertapping with left hand
1.5T GE Twinspeed Excite MR scanner – fMRI set up
MR compatible user feed-back ”glove”
1.5T GE Twinspeed Excite MR scanner – fmri running
fMRI Summary
• fMRI does not directly measure neuronal activity - it relies on vascular and metabolic correlates of changes in the neuronal work load.
• Results are dependent on the design of the experiment and the MR parameter settings.
• Large intersubject variability in the resulting activation maps
• Only relative changes in brain activity can be measured with BOLD fMRI.
Diffusion Tensor Magnetic Resonance Imaging
A stationary molecule in the presence of diffusion gradients
ω > ω0ω < ω0
2
1
( ) t
t
t dt
180
A moving spin in the presence of diffusion gradients
180
MR signal intensity in spin echo sequences decreases exponentially:
0
( ) bDM te
M
0 (b G
D = diffusion coefficient
The diffusion coefficient can be determined by measuring the spin-echo amplitude as a function of gradient strength
90 180°
Skiv-sel.
Freq. Enc.
Phase Enc.
RF
G
2 2 2 / 3b G
Introduce diffusion gradients in the imaging sequence
b vs. signal intensity
b
log (signal)
T2-weighting
DWI = diffusion weighted image
b1b0
S1
S0
The ADC image
• ADC = Apparent Diffusion Coefficient
• ADC = the slope– CSF 2000 m2/s– Brain 700 m2/s
1 0
0 1
ln /S SD
b b
1 0
0 1
ln /S SD
b b
1 0( )
1 0eb b DS S
A clinical example of diffusion-weighted MRI: acute stroke
Var är infarkten?
T2 DWI ADC
Measurement of the Diffusion Tensor DT-MRI
xx xy xz
yx yy yz
zx zy zz
D D DD D DD D D
D
Gray matter CSF White matter
FA-map (Fractional Anisotrophy) Spatial orientation of the diffusion tensor, red=L-R, green=S-I, blue=A-P
• Following the direction of the eigenvector corresponding to the largest eigenvalue through the imaged brain volume
• e.g. to see if/which two brain regions are connected
• several fibres in e.g. the brain stem can be identified
• Requires high-resolution & high SNR– Scan times minimum ~20 minutes
with SS-EPI
• Several methods for improve the results based on the still too noisy data– FACT, Spaghetti model,
Continuous tensor field
Courtesy of Susumu Mori, Johns Hopkins, Baltimore
MR Tractography – Fiber tracking
Parallelimaging in MRI
Acquisition of MR image Sampling of k-space
Imaging scan time is determined by the time it takes to sample k-space.
Scan time can be reduced by doing tricks in k-space such as
• Fractional NEX sampling of k-space (ky range reduced)
• Fractional echo sampling of k-space (kx range reduced)
But speed in k-space is crucially determined by gradient strength:
dttGk xx )(
dttGk yy )(
Only one point in k-space can be sampled at a time!
Receiver coil
Object
d
The measured signal will depend on the distance to the object being imaged.
3
1
dS (Biot-Savarts
law)
We can receive MR signals from several coils in parallel...
An image from each coil can be generated. The signal intensity in each voxel will depend on the spatial distance of that voxel and the coil.
De Zwart, et al. MRM, 48, 2002
• Can we use the multiple channel data to reduce scan time?
• Scan time can be reduced by decreasing the number of phase-encoding steps, Nphase ( Scan time = TR * NEX * Nphase)
• Spatial resolution is retained if we keep the maximum spatial frequencies (kx,max and ky,max) the distance between the sampling points in k-space is increased.
• The price we pay for a reduced scan time is a reduced FOV – aliasing (folding of the image object) will occure.
phasefreq
phasey TG
NFOV
• Nphase reduced by a factor of 2.
• Scan time reduced by a factor 2.
• FOV reduced by a factor of 2.
• Aliasing present in the images.
• Using conventional 2D Fourier imaging, it is impossible to recover the unfolded, full FOV image from the distorted reduced FOV images.
Pruessmann, et al. MRM, 42, 1999
Pha
se e
ncod
ing
dire
ctio
n
Phase encoding direction
SENSE (SENSitivity Encoding) MRI
Aliased image, coil 1
Aliased image, coil 2
Aliased image, coil 3
Aliased image, coil 4
Full FOV image
SENSE image reconstruction
Intermediate images
Final reconstructed image
To go from aliased images to a full FOV image we need to:
• Undo the signal superposition underlying the fold-over effect.
• This is feasible since THE SIGNAL CONTRIBUTION IS WEIGHTED WITH COIL SENSITIVITY MAP for each reduced FOV image (spatial sensitivity coding).
Preussmann, et al. MRM, 42, 1999
R = reduction factor
R=1.0
R=2.0
R=2.4
R=3.0
R=4.0
• Determine the sensitivity map for each coil:
Removal of noise and smoothing
”Raw” data from one coil.
Sensitivity map for one coil
Pruessmann et al., MRM, 42, 1999
• Sensitivity maps must be calculated for the ”full-FOV” images prior to SENSE imaging.
SENSE image reconstruction (I)
c= number of pixels superimposedp
= number of coils used
Generate Sensitivity matrix S (c rows, p columns, c x p):
cpcc
p
p
sss
sss
sss
S
21
22221
11211
For each pixel in the reduced FOV images:
We also need to describe how the noise is correlated between the coils:
)( cc - reciever noise matrix
SENSE image reconstruction (II)
Next, store the corresponding pixel signal intensity values from the reduced FOV images in a vector ”a”:
ci
ii
a
2
1
Signal separation (in vector v) is then achieved by solving:
aSSvS HH 1 aSSSvmatrixunfolding
HH
11)(
Vector v contains the separated pixels values for the originally superimposed pixel positions (length of v : p)
Repeat procedure for all pixels in the reduced FOV images. The result is a single full FOV image.
SENSE MRI – Example:
Coil configuration:
R=1.0
R=2.0
R=2.4
R=3.0
R=4.0
Pruessmann, et al. MRM 42, 1999
Reduction of FOV in the horizontal direction
SENSE MRI
Pruessmann, et al. MRM, 42, 1999
GRE, R=2.0, 2 coils, single coil image.
SENSE reconstruction, scanning time= 85
seconds.
SENSE reconstruction from fully Fourier
encoded data, scanning time = 170 seconds
Single channel headcoil
8 channel headcoil
Outlook
• Even higher spatial and temporal resolution, reduced sensitivity to image artifacts with modified image acquisition sequences.
• A further increase in image acquisition speed using parallel imaging techniques.
• Use of ”intelligent” contrast agents based on magnetic nanoparticles which are bound to receptors or antibodies.