Burst: Applications in Ultra-Rapid Imaging and Quantitative Diffusion Measurement

Simon J Doran

Department of Physics, University of Surrey

S Dr. S. J. Doran Department of Physics,University of Surrey,Guildford, GU2 5XH, UK

Acknowledgements

• Marc Bourgeois (ICR)

• Claudia Domenig (UniS)

• Andy Dzik-Jurasz (ICR)

• Martin Leach (ICR)

• David Collins (ICR)

• Claudia Wheeler-Kingshott (IoN)

• Roger Ordidge (UCL)

Summary

• Introduction to Burst and background Basic concept

Historical survey

• Single-shot Burst imaging Burst variants, SNR comparison and choice made

Problems to overcome

Comparison of techniques in phantoms and in vivo

• Quantitative diffusion imaging extra-cranially Application of Burst for diffusion measurements

Early results and analysis

Comparison of Burst and other techniques at 1.5 T

Introduction: Basic concept

• Burst is a rapid imaging technique, first proposed by Hennig in 1988.

• A series of low angle pulses creates a train of echos, which can be used to form an image.

Burst pulse train(64 low-angle pulses)

180o Train of 64 echos

Gread

Gphase

Advantages of Burst

• Using a slice-selected Burst sequence, all the signals can come from pure spin-echoes.

Little geometric distortion or “signal drop-out” in regions of large susceptibility change

Better off-resonance properties than EPI — no need for fat-sat

• Less rapid gradient switching than EPI dB/dt issues not a problem from a safety point of view

Can be acoustically very quiet

• Lower RF power deposition than HASTE

• Extremely robust no shimming or set-up period required 20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10

Ambient Noise

EPI

Burst

dB

Disadvantages of Burst

• Low signal-to-noise

Intrinsically low SNR due to low flip angle pulses

(Relatively) high acquisition bandwidth (e.g., 10 s / point), but still much lower than EPI at 4.7 T

• Signal Decay

Diffusion and T2 during both excitation and read periods

This is both a disadvantage (for single-shot imaging) and a valuable feature (for diffusion imaging)

Literature survey: (1) Basic sequence

• This type of pulse sequence has been extensively studied in a non-imaging context.

Kaiser, Bartholdi and Ernst, J. Chem. Phys. 60, 2966 (1974)

Hennig et al. MRM, 3, 823 (1986)

• First images published in 1993 by Hennig and Hodapp (MAGMA, 1, 39-48) and Lowe and Wysong (JMR 101, 106)

• Burst is a variant on the DANTE sequence

spectroscopy: Morris and Freeman,JMR 29, 433 (1978)

cardiac tagging: Mosher and Smith,MRM 15, 334 (1990)

Data: McVeigh and Atalar

Literature survey: (2) Linear approximation

• Easiest theoretical treatment by assuming linear approximation, i,e., each pulse causes one echo. However, this works only for very low pulse angles.

• In practice, the Bloch equations are non-linear and higher order echoes occur.

• Interference between spin and stimulated echoes reduces the echo amplitudes.

Simulations from Zha and Lowe, MRM 33, 377 (1995)

Literature survey: (3) Phase modulation

• We can also look at the problem in the frequency domain. We get a small signal because only a small fraction of the sample is excited.

One pixel

• Zha and Lowe (MRM 33, 377 (1995)) showed that by suitable phase-modulation of the low-angle pulses, one can excite the sample almost completely and obtain the desired echo train.

One pixel

Literature survey: (4) Optimisation

• Several authors have considered optimisation of the Burst excitation pulse train.

Le Roux et al. Chirp pulses Proc. 10th SMRM , 238 (1991)

Zha and Lowe, OUFIS, MRM 33, 377 (1995)

van Gelderen et al. JMR B, 107, 78 (1995)

Heid, MRM 38, 585 (1997)

• The bottom line is that for N excitation pulses, i.e., N echoes, the pulse flip angle should be at most

2 N

as opposed to / 2N for the non-optimised pulse train.

• For 64 pulses, this equates to 11.25° still poor SNR

Literature survey: (5) Burst variants

• Burst can be seen as “simply” a means of generating multiple echoes.

• As such it can be incorporated into many standard sequences.

Radial imaging: Jakob et al., 36, 557 (1996)

SSFP: Heid, Proc. 8th ISMRM, 1499 (2000)

STEAM: Cremillieux et al. MRM 38, 645 (1997) (6 64 64 images in 210 ms)

Burst pulse train(16 low-angle pulses)

180o Train of 16 echos

Gread

Gphase

4

HASTE (BASE): van Gelderen et al. MRM 33, 439 (1995); Zha et al. Proc 5th ISMRM, 1820 (1997)

Burst pulse train(9 low-angle pulses) Multiple trains of 9 echos

Gread

RF

N / 18

EPI (URGE-EVI): Heid, Proc. 3rd ISMRM, 98 (1995)

Burst pulse train(9 low-angle pulses) Train of 9 echos

Gread

RF

N / 9

FLASH (URGE): Heid et al., MRM 33, 143 (1995)

Multi-refocusing Burst: (1) Excitation

• The original design of Burst sequence has two major problems with its excitation scheme:

The entire sample is excited by the train of hard pulses, so multi-slice acquisitions are not possible.

Image profile

Theoretical profile from -pulse frequ-ency response

Pixel NumberSig

na

l in

ten

sit

y /

arb

. u

nit

s

Although overall RF energy deposition is relatively small, the peak power required is excessive, because it needs to be applied as a short pulse.

Multi-refocusing Burst: (2) Selective excitation

• A solution to both problems is found by using selective excitation (van Gelderen et al. MRM 33, 439 (1995))

• However, this removes several of the key advantages of Burst. Now the sequence becomes noisy, is highly demanding on the gradients and we get some artifacts.

Gread

Gslice

N

RF

Gread

Gslice

N / 2

RF

Unipolar scheme Bipolar scheme

RF frequency offset inverted

Multi-refocusing Burst: (3) Excitation artifacts

• In the presence of B0-inhomogeneities, the bipolar scheme gives rise to slice-definition inconsistencies. Not noticeably a problem.

• However, we do see significant differences in echo phase.

Echo number

Ech

o ph

ase

/ rad

Echo number

Ech

o ph

ase

/ rad

Slice offset = 0

Slice offset = 60 mm

Raw Corrected

Multi-refocusing Burst: (4) Echo phase

• The most significant problem in developing the multi-refocusing Burst sequence at 1.5 T on the Siemens Vision is the unwanted variation in echo phase.

• A standard FT reconstruction algorithm assumes that, in the absence of the phase-encoding, all echoes have the same phase.

• In fact, we observe the phase to change in the following ways:

continuously during an echo train

discontinuously between echo trains

alternating when we use the bipolar slice selection

with an amplitude of variation that depends on the slice offset from isocentre

• The cause of these phase variations is still uncertain, but may be an eddy current effect.

Multi-refocusing Burst: (6) Echo phase examples

Continuous phase change during a single readout of 64 echoes

Alternating phase during a single readout of 64 echoes (bipolar slice gradient), small slice offset

Alternating phase during a single readout of 64 echoes (bipolar slice gradient), large slice offset

(NB Phase needs unwrapping!)

Discontinuous phase change during a multi-refocusing readout of 12 6 echoes

Multi-refocusing Burst: (6) Echo phase artifact

• Uncorrected, the echo phase problem gives rise to a serious artifact.

• With suitable correction, using a non-phase encoded echo train, the artifact can be mostly removed, but the remaining artifacts still degrade the performance of the sequence.

• The major unsolved problem is to achieve the correction in regions of the body that move between the non-phase encoded scan and the “image” scan.

Early results at 1.5 T

• Comparison of original OUFIS with “off-the-shelf” EPI on Siemens Vision.

• Slice deliberately chosen to highlight problems with EPI.

• Poor resolution and SNR, but excellent geometric fidelity, particularly around air spaces

• Note the difference in contrast.

64-pulse OUFIS

128 128 EPI

Very (!) early results at 4.7 T

• Results after approximately two days on 4.7 T system in factory environment

• Single-shot 642 image (partial Fourier, reconstructed to 64 112) acquired at 4.7 T

• Poor resolution and SNR, but excellent geometric fidelity, particularly around air spaces

• Note: no need to shimCompare the EPI acquired at the same time (shimmed to get best results on top slice).

Recent comparison at 1.5 T

• “Original” Burst (OUFIS)100 ms, 3.63.6 mm2, TEeff~10 ms, SNRN=2.3, SL=7mm

• Refocussed Burst238 ms, 1.8 1.8 mm2, TEeff~25 ms, SNRN=8.3, SL=7 mm

• EPI248 ms, 1.8 1.8 mm2, TEeff~90 ms, SNRN=27, SL=7 mm

• HASTE344 ms, 1.25 1.25 mm2, TEeff=?, SNRN=48, SL=7 mm

Refocused Burst“Original” Burst

EPI HASTE

SNRN = SNR / ( (acq. time)1/2. (pixel area) )

Extra-cranial imaging at 1.5 T: pelvis

HASTE

EPI + “fat sat”

“New” Burst

• Pelvic imaging is important for diagnosing rectal and prostate cancers.

• HASTE is currently the method of choice for single-shot imaging, but RF power deposition is a potential problem.

• EPI is not widely used because of the presence of fat.

• Burst works “quite well”.

SNR: Comparison with EPI

• Ignoring artifacts, the key relationship is SNR sin / BW 1/2.

• On the Siemens Vision at 1.5 T, we have shown that the SNR of EPI is approximately a factor of 3 higher than our best Burst.

• At higher field, a spin-echo based Burst sequence could be read out at the same BW, whereas the EPI sequence would be likely to require a much higher bandwidth.

Image contrast (1)• What sort of information can we get out of Burst images?

• Contrast properties of Burst images very little studied so far.

2/

0

TTEDb

jjj eeAA

• Sequence is inherently T2 and D weighted. For low flip angles

• By adjusting TE and the read gradient, we can emphasise either T2 or diffusion decay.

0

0.1

0.2

0.3

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0 5 10 15 20 25 30 35 40

Echo Number

A /

A0

Data for CuSO4

T2 and D double fit

Typical Spectroscopic Data Typical Image Data

Can we use the decay to get D and T2?

ADC and T2 Burst sequence

• A data array of n echoes is acquired for each PE step.

• Each echo, j, corresponds to the same k-space line of the same slice, but with a different ADC and T2 weighting.

• Corresponding echoes in successive arrays are used to reconstruct a given image.

In one scan we collect n images weighted by ADC and T2.

Ph

ase

enco

de

Readout

D, T 2

j = 0

j = n-1

Burst images SE images

ME images

First in vivo images (8 T)

Quality of double-exponential data fit

Echo number Echo number

Echo number Echo number

Aj / A

0A

j / A

0

Aj / A

0A

j / A

0

ROI1

ROI2

ROI3

Typical single pixel fit

First human study (1.5 T)

• Multi-functional rectal carcinoma study

• Images had very poor SNR, so analysis performed on ROIs in tumour.

• Remarkable correlation between tumour ADC and treatment success.

But are we really measuring diffusion?

• The data fit moderately well to a bi-exponential function.

• There are several possible explanations:

genuine IVIM perfusion effect

incorrect T2 correction

motion

many different ADC values in the ROI

Burst single-shot imaging: Conclusions

• Burst has been around for a “long” time (10 years), but has never really caught on.

• It has a number of attractive features, most notably that it can be made almost impervious to susceptibility, giving undistorted images.

• The SNR has been improved by a factor of approximately 30 since the original introduction of the sequence, but is still quite low.

• The contrast of the sequence needs investigating further.

• Our 3-year EPSRC project came to the conclusion that Burst is “almost competitive”, but not quite on the hardware we used.

• Application at higher fields remains an attractive possibility.

Measurement of diffusion with Burst: Conclusions

• Burst gives us a potentially exceedingly time-efficient way of obtaining many b-values in the same measurement.

• The SNR in the original measurements was low, but we have researched a number of ways of improving this.

• There are still a number of technical difficulties with the approach, the most serious of which is motion.

• This makes it as yet unclear whether the values we are getting from Burst are correct or not.

• We have performed extensive phantom and initial in vivo comparisons with three other diffusion imaging sequences: split-echo HASTE, PSIF and segmented EPI.

Choice of Burst sequence to develop

• Aim: medium resolution, single-shot, multi-slice dataset

• Choice made on basis of expected SNR.

Chosensequence

“Original” Burst

Multi-refocusing Burst: (4) Unwanted echoes

• The need for crusher gradients around the 180° pulse can be understood by the use of an extended phase graph.

180 180 180 180

...

• Everywhere that magnetisation crosses the central axis, an echo is formed. (Not all paths from the original - pulses are shown.)

• Higher order echoes are superimposed on the desired spin echoes.

Multi-refocusing Burst: (5) Unwanted echoes

• We can separate out the unwanted echoes by changing the gap between the last - pulse and the first 180°.

180 180 180 180

...

• The unwanted echoes are small, but can be significant.

• A very sensitive test of the efficiency of spoiling is to acquire a non-phase-encoded dataset and FT all the echoes.

• Depending on where the unwanted echoes occur, the effect on the image may be slight or extremely serious.

Multi-refocusing Burst: (7) Segment offset artifacts

• The current implementation of the sequence uses mosaic tiling of the k-space segments.

• Eddy currents and poor performance of the Vision gradient system lead to offsets in the phase-encoding blip gradient of small fractions of kphase.

• These again lead to complicated multiple ghosting artifacts in the phase-encoding direction. The process can be simulated and, in principle, corrected.

Segment 1

Segment 2

Segment 4

Segment 3

kx

ky

Segment 1

Segment 2

Segment 4

Segment 3

kx

ky

Phantom comparison at 1.5 T

SNR=19, SL=10mm SNR=72, SL=10mm SNR=75, SL=5mm

“Original” Burst “New” Burst “Best” EPI

Image contrast (2)

• Do we need T2 contrast to see the activations?

E.g., Hutchinson et al. JMRI 7(2), 361-364 (1997)

• Potential method for getting T2 contrast …

Burst pulse train(64 low-angle pulses)

180o Train of 64 echos

Gread

Gphase

T2 delay

• In practice, this works with the basic Burst sequence, but we have not had any success in achieving T2* weighting with multiply refocused Burst.