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: External magnetic field: Energy levels are split (Zeeman effect): B0B0 E , anti-parallel spin , parallel spin EE 0

Physical principles of NMR (very briefly) Motion of spins in an external magnetic field: NMR experiment: static field and radiofrequency (RF) field): In a rotating frame of reference with the angular frequency B0B0 B1B1 M M0M0 x y

Spatial localization in MRI Let the magnetic field vary in x, y and z-space. x Gx

MR IMAGING IN 1973 P.C. Lauterbur, Nature, 242: , 1973

2D - FFT ”k-space””reconstructed image” CONVERTING FREQUENCIES INTO SPATIAL LOCATIONS

TRAVEL IN ”K-SPACE” WITH THE SPIN ECHO SEQUENCE 180  ECHO The gradients permits sampling of points in k-space Each echo gives us one line in k-space. Scan time: TR x N_phase

RF SIGNAL Conventional gradient echo image acquisition slices / second N times N excitations / image

TE eff Echo Planar Imaging sequence RF SIGNAL 90 o

EPI image acquisition SIGNAL

EPI and T2*-sensitivity 64 echoes or more are acquired per image EPI is strongly sensitive to variabilities in the magnetic field (T2*)

(Gradient) Echo planar imaging TR/TE/flip = 3000ms/40ms/90deg 3.4 x 3.4 x 4 mm 3, 30 slices T2*-weighted image contrast

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 (Fe 2+ ) 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

r a Outside ”vessel”: Inside ”vessel”: 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 rCMRO 2 mismatch Neuronal activity CBF CMRO 2 BOLD effect

t 0s30s60s90s120s150s180s210s240s270s ON OFF Continuous EPI image acquisition fMRI - Blocked design OFF:ON:

fMRI – Blocked design T2*- weighted imageActivation map, p< T, 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 > ω 0 ω < ω 0 180

A moving spin in the presence of diffusion gradients  180

MR signal intensity in spin echo sequences decreases exponentially: D = diffusion coefficient The diffusion coefficient can be determined by measuring the spin-echo amplitude as a function of gradient strength

 ° Skiv- sel. Freq. Enc. Phase Enc. RF G  Introduce diffusion gradients in the imaging sequence

b vs. signal intensity b log (signal) T 2 -weighting DWI = diffusion weighted image b1b1 b0b0 S1S1 S0S0

The ADC image ADC = Apparent Diffusion Coefficient ADC = the slope –CSF  2000  m 2 /s –Brain  700  m 2 /s

A clinical example of diffusion-weighted MRI: acute stroke Var är infarkten? T2DWI ADC

Measurement of the Diffusion Tensor DT-MRI Gray matterCSFWhite 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 imageSampling 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 (k y range reduced) Fractional echo sampling of k-space (k x range reduced) But speed in k-space is crucially determined by gradient strength: 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. (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, N phase ( Scan time = TR * NEX * N phase ) Spatial resolution is retained if we keep the maximum spatial frequencies (k x,max and k y,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.

N phase 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 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) = number of pixels superimposed = number of coils used Generate Sensitivity matrix S (c rows, p columns, c x p): For each pixel in the reduced FOV images: We also need to describe how the noise is correlated between the coils: - reciever noise matrix

SENSE image reconstruction (II) Next, store the corresponding pixel signal intensity values from the reduced FOV images in a vector ”a”: Signal separation (in vector v) is then achieved by solving: 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.