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

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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.