Transcript Document 7515798

S
1C
Domenig, 2A Jurasz,
3M Leach, 1S Doran
1Department
of Physics,
University of Surrey,
Guildford
2Glaxo
Smith Kline
MR Research Group
Institute of Cancer Research
3Clinical
Intravoxel Incoherent Motion Imaging in
Locally Advanced Rectal Tumours
Dr S J Doran
Department of Physics
University of Surrey
Structure of talk
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ADC as a measure of treatment response:
a tantalising prospect
Why Burst imaging for diffusion?
Why not Burst imaging!
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Initial analysis of the data
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Further analysis of the data and future work
A tantalising prospect: Diffusion imaging in tumours
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Intriguing measurements were made
using the novel Burst diffusion imaging
sequence.
These appeared to show that (in this
patient cohort) there is a very strong
link between treatment outcome and
ADC prior to treatment.
However, there were a number of issues
concerning the methodology that
required further investigation.
This talk is about what we found as we
delved deeper into the data.
Lancet 360, 307–308 (2002)
IVIM Measurements in tumours
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Previous studies have evaluated ADC’s
in extra-cranial organs using only a
restricted range of b-values, sometimes
as few as two.
Results in liver
The existence of a significant tissue
perfusion effect is intrinsically of
interest.
Moreover, if the existence of perfusion
is ignored, then incorrect values of the
ADC may be calculated.
Measurement with multiple b-values is
relatively time-consuming and few
studies characterise the low b-value
regime fully.
Yamada et al., Radiology, 210, 617–623 (1999)
Why use Burst for extra-cranial diffusion imaging?
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Measurement of diffusion
coefficients using Burst was first
introduced in 1995.
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Data for CuSO4
T2 and D double fit
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Burst allows us to obtain a very
large number of points on the
diffusion decay curve.
A / A0
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This gives the potential for
analysing multiple exponential
signal decay.
This form of Burst leads to images
without distortion: potentially much
more suitable for extra-cranial
imaging than EPI.
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40
Echo Number
Doran and Décorps, JMR A, 117(2), 311–316 (1995)
Why not Burst imaging?
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Burst uses low flip angle pulses,
so the SNR is very poor.
Although typically 9-25 b-values
are acquired in the same time as
a single PGSE b-value, this is
still a multi-shot technique.
This gives rise to motion
artifacts, as in PGSE, that may
compromise our data.
We need to compensate for T2
decay during the acquisition.
Initial analysis of the data
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SNR was too poor to make a
good quantitative analysis on
single pixels.
However, the results for tumour
ROI’s appeared very promising,
leading to a good quality fit.
A “naïve” automated analysis,
based on a single exponential
diffusion diffusion decay led to
the results published in The
Lancet.
r = -0.83, p = 0.012
ln (S/S
0)2 s-1
ADCmono
/ cm
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Anomalously high D for fat
is due to T2 “correction”.
Standard multi-echo
sequences measure an
incorrect T2 for fat.
b-value
/ s mm/-2%
Tumour
regression
Further analysis of the data (1)
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Closer examination showed that
not all tumours followed the
same pattern.
A single-exponential diffusion
decay model was clearly
inappropriate for most.
The data are fitted moderately
well by a bi-exponential model.
This suggested that IVIM effects
may be important.
ln (S/S0)
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S/S0 = f exp(-b.ADCbiexp) + (1-f) exp(-bD*)
b-value / s mm-2
Further analysis (2): Key questions
This observation poses a number of significant questions:
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What did we actually measure?
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How do we get a genuine ADC from these measurements?
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How much of what we see is due to the low SNR of Burst?
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Are the results caused by incorrect T2 measurements in our
“correction scan” or motion artifacts?
Further analysis (3): What did we measure?
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Fitting a single-exponential decay to only the first half of the semi-log
plot allows us to make a crude estimate of the pseudo-diffusion
coefficient for individual pixels.
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Fitting to the last half of the plot gives us an estimate of ADC.
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However, results are severely biased by where the cutoff is chosen.
ln (S/S0)
ln (S/S0)
Effect of original analysis
was to return an average
between ADC and D*. Not
so very different from
doing a two-point diffusion
measurement!
b-value / s mm-2
b-value / s mm-2
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Ideally, we would always perform a
double exponential fit.
SNR is too poor to do this on
individual pixels, but we can fit a
straight line to get D* for every pixel.
We have a wide spread of values, but
how much of this is genuine and how
much due to low SNR?
We can increase SNR by rebinning the
data to lower resolution
Number of pixels
Further analysis (4): SNR issues
128  128
D* / 10-3 mm2 s-1
64  64
32  32
With SNR increased by factors of 2 and
4, we maintain the broad range of D*.
Conclusion 1: The effects that we see are not artefacts of low Burst SNR
Further analysis of the data (4)
To our surprise, we found no correlation between D and D* as
obtained in this model with tumour regression.
One patient had an anomalously high value for D* and was tentatively
excluded from our subsequent analysis.
ADCbiexp / 10-3 mm2 s-1
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We then fitted an IVIM diffusion model to data for the tumour ROI’s.
r = 0.03, p = 0.012
Tumour regression / %
r = 0.14, p = 0.143
D* / 10-3 mm2 s-1
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Tumour regression / %
Conclusion 2: The (genuine) effect seen is not caused by D, as at first thought.
Further analysis of the data (5)
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We did find a correlation (albeit relatively weak) between
diffusion fraction f and tumour regression.
This correlation is consistent with the original observation
that ADCmono measured with a mono-exponential model
decreases with increasing tumour regression.
r = 0.61, p = 0.012
Diffusion fraction
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Tumour regression / %
Discussion
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We still do not understand fully the origin of the excellent
correlation in our original result.
The parameter originally measured is a combination of ADC
and perfusion.
The “diagnostic” parameter appears to be the diffusion
fraction, f, rather than ADC or D* per se.
Further volunteer studies have highlighted the large
sensitivity to motion of this un-navigated sequence.
There are some concerns that any mis-estimation of T2 in our
data correction could mimic a multi-exponential behaviour in
the data.
Conclusion 3: It is difficult to envisage how the possible systematic errors
above could have led to the correlation seen.
Conclusions
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We have measured a very interesting phenomenon, which
could have important implications for cancer therapy.
The conclusions in our original Lancet paper need to be
revised in the light of our further investigations.
The observations are unchanged, but the underlying cause
must be re-interpreted.
Further studies of tumours using low b-values to measure
perfusion are strongly recommended.