intravoxel incoherent motion imaging in locally advanced rectal tumours
DESCRIPTION
S. 1 Department of Physics, University of Surrey, Guildford. 2 Glaxo Smith Kline 3 Clinical MR Research Group Institute of Cancer Research. 1 C Domenig, 2 A Jurasz, 3 M Leach, 1 S Doran. Intravoxel Incoherent Motion Imaging in Locally Advanced Rectal Tumours. Dr S J Doran - PowerPoint PPT PresentationTRANSCRIPT
Intravoxel Incoherent Motion Imaging in Locally Advanced Rectal Tumours
Dr S J Doran
Department of PhysicsUniversity of Surrey
S 1Department of Physics,University of Surrey,Guildford
1C Domenig, 2A Jurasz, 3M Leach, 1S Doran
2Glaxo Smith Kline3Clinical MR Research GroupInstitute of Cancer Research
Structure of talk
• ADC as a measure of treatment response:a tantalising prospect
• Why Burst imaging for diffusion?Why not Burst imaging!
• Initial analysis of the data
• Further analysis of the data and future work
A tantalising prospect: Diffusion imaging in tumours
• 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
• Previous studies have evaluated ADC’s in extra-cranial organs using only a restricted range of b-values, sometimes as few as two.
• 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)
Results in liver
Why use Burst for extra-cranial diffusion imaging?
• Measurement of diffusion coefficients using Burst was first introduced in 1995.
• Burst allows us to obtain a very large number of points on the diffusion decay curve.
• 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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0 5 10 15 20 25 30 35 40
Echo Number
A /
A0
Data for CuSO4
T2 and D double fit
Doran and Décorps, JMR A, 117(2), 311–316 (1995)
Why not Burst imaging?
• 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.
• SNR was too poor to make a good quantitative analysis on single pixels.
Initial analysis of the data 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
ln (
S/S
0)
• However, the results for tumour ROI’s appeared very promising, leading to a good quality fit.
Tumour regression / %
AD
Cm
on
o /
cm
2 s
1
r = -0.83, p = 0.012• A “naïve” automated analysis,
based on a single exponential diffusion diffusion decay led to the results published in The Lancet.
Further analysis of the data (1)
• 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.
b-value / s mm-2
ln (
S/S
0)
S/S0 = f exp(-b.ADCbiexp) + (1-f) exp(-bD*)
Further analysis (2): Key questions
This observation poses a number of significant questions:
• What did we actually measure?
• How do we get a genuine ADC from these measurements?
• How much of what we see is due to the low SNR of Burst?
• Are the results caused by incorrect T2 measurements in our
“correction scan” or motion artifacts?
Further analysis (3): What did we measure?
b-value / s mm-2
ln (
S/S
0)
Effect of original analysis was to return an average between ADC and D*. Not so very different from doing a two-point diffusion measurement!
• However, results are severely biased by where the cutoff is chosen.
• 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.
b-value / s mm-2
ln (
S/S
0)
• Fitting to the last half of the plot gives us an estimate of ADC.
Further analysis (4): SNR issues
• 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?
Conclusion 1: The effects that we see are not artefacts of low Burst SNR
D* / 103 mm2 s1
Nu
mb
er
of
pix
els 128 128
64 64
32 32• We can increase SNR by rebinning the
data to lower resolution
• With SNR increased by factors of 2 and 4, we maintain the broad range of D*.
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.
Tumour regression / %
AD
Cb
iex
p /
10
3 m
m2 s
1
r = 0.03, p = 0.012
Tumour regression / %
D*
/ 1
03 m
m2 s
1
r = 0.14, p = 0.143
Conclusion 2: The (genuine) effect seen is not caused by D, as at first thought.
• We then fitted an IVIM diffusion model to data for the tumour ROI’s.
Further analysis of the data (5)
• 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
Tumour regression / %
Dif
fus
ion
fra
cti
on
Discussion
• 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
• 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.