Magnetic Resonance Imaging of Fast Relaxing Spins:

Acquisition during Adiabatic Excitation 

November 14, 2005, CMRR : Djaudat Idiyatullin

Mike’s crazy idea is working 

Interleaved excitation and sampling during a frequency-swept pulse

1. Steady state 2. Sensitive to spins with a very

short T23. It is not FID but the signal

predictable by Bloch simulation.

d

p

. . .

BIR4

……

How to extract information from this weird sampling during swept excitation?

Least square method Monte Carlo simulation Wavelet transform

22 1aT

21/ 1a

How to extract information from this weird sampling during swept excitation?

Solution:

1. Away from adiabatic condition

22 1aT

21/ 1a

2 21 1a a

2. Linear system- Correlation method

0 0180 90

Linear system

Output r(t)

Input x(t)

System h(t)

A system is linear if:1. Linearity : Output = C * Input2. Shift invariant : delaying of Input → same delaying of Output

0 τ t

x(τ) → x(τ)h(t- τ)( ) ( ) ( )r t x h t d

( ) ( ) ( )R X H

Convolution

Fourier theorem:

Evolution of the isochromats during HS8 pulse (dw=10mks, (~ 30 degree), R2=500Hz)

30kHz

time

20kHz

10kHz

0kHz

-10kHz

-20kHz

-40-20

02040

30kHz

frequency

20kHz

10kHz

0kHz

-10kHz

-20kHz

sum of all

Evolution of the isochromats during HS8 pulse (dw=10mks, (~ 30 degree), R2=500Hz)

30kHz

time

20kHz

10kHz

0kHz

-10kHz

-20kHz

-40-20

02040

30kHz

frequency

20kHz

10kHz

0kHz

-10kHz

-20kHz

sum of all

Linear system

Correlation method for linear system

( ) ( ) ( )r t x h t d

Response r(t)

Excitation x(t)

Spin system h(t) FT

FT

( ) ( ) ( )R X H *

2

( ) ( ) ( )( )

( )( )

R X RH

XX

( )H

)(X

( )R

System spectrum

*

Simulated data HS4 pulse

100 isochromats from -12.5kHz step 250Hz

dw=10mks R1=500Hz

-40 -20 0 20 40

Hy()

f

/2 , kHz

eH

x()

dX

x()

cR

x()

bR

xy()

a

spin density

SWeep Imaging

with Fourier Transform (SWIFT)

(a)

Gz

Gy

Gx

acq

RF

1

RF

1

acq

(b)

. . .

Tp

Tr

HSn pulsesFlip angle < 90 degreeTr ~ TpBw=sw=2πN/TpBack-projection reconstruction

SWIFT, characteristics

Signal intensity depends only on T1 and spin density (M0) :

Maximum signal intensity Ernst angle:

Maximum T1 contrast:

Spin density contrast:

Sensitive to short T2 :

10

1

1 exp( / )sin( )

1 exp( / )cos( )tr

tr

T TS M

T T

1cos( ) exp( / )opt trT T

1.7 opt

opt

2 1/ ~ 10T s s

SWIFT, hardware problems

“Dead time” after pulse

4.7T , 7T : ~ 3μs : sw < 130kHz

4T : ~ 20μs : sw < 40kHz

FIFO underflow happens if:

Tr < 5ms for 128 samplingTr < 10ms for 256 samplingsw ~ 25-35 kHz

MIP of 3D image sw=32kHz

128x128 x 644T

Empty “16”-element TEM head coil

3D image of thermoplastic

T2~0.3ms sw=100kHz

128x128 x 1284.7T

Sensitivity to short T2

MIP of 3D image plastic toy in breast coil sw=39kHz

128x128 x 128D=25cm

4T

Sensitivity to short T2

Slices of 3D image of feet

sw=20kHz4T

First in vivo SWIFT 3D images

Slices of 3D image

raspberryin vivo

sw=100kHz128x128x128

D=3cm4.7T

Sensitivity to raspberry

Advantages Disadvantages

fast Too fast for VARIAN

FIFO underflow

Sensitive to short T2 Sensitive to coil material

Reduced motion artifacts(zero echo time, back projection reconstruction)

Problems with slice selection

Reduced signal dynamic range ?

quiet Too quiet

AnotherMike’s

crazy idea 

Breast MR scanner

Thanks to:Ivan Tkac Gregor Adriany Peter Andersen Tommy Vaughan Xiaoliang ZhangCarl SnyderBrian Hanna John StruppJanis Zeltins Patrick BolanLance DelaBarreUte Goerke

all CMMR

Fast & Quiet MRI by Sweeping Radiofrequency

Djaudat Idiyatullin, Curt Corum, Jang-Yeon Park, Michael Garwood

Macros, C programming

Hardware

Software

Yellow pages of CMRR Discussion