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Intravoxel Incoherent Motion Imaging in Locally Advanced Rectal Tumours

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

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Intravoxel Incoherent Motion Imaging in Locally Advanced Rectal Tumours

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  1. S 1Department of Physics, University of Surrey, Guildford 2Glaxo Smith Kline 3Clinical MR Research Group Institute of Cancer Research 1C Domenig, 2A Jurasz, 3M Leach, 1S Doran Intravoxel Incoherent Motion Imaging in Locally Advanced Rectal Tumours Dr S J Doran Department of Physics University of Surrey

  2. 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

  3. Lancet 360, 307–308 (2002) 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.

  4. 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. Results in liver Yamada et al., Radiology, 210, 617–623 (1999)

  5. 1 0.9 Data for CuSO4 T2 and D double fit 0.8 0.7 0.6 A / A0 0.5 Doran and Décorps, JMR A, 117(2), 311–316 (1995) 0.4 0.3 0.2 0.1 0 0 5 10 15 20 25 30 35 40 Echo Number 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.

  6. 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.

  7. Anomalously high D for fat is due to T2 “correction”. Standard multi-echo sequences measure an incorrect T2 for fat. • 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/S0) ADCmono / cm2 s-1 b-value / s mm-2 Tumour regression / % Initial analysis of the data

  8. ln (S/S0) b-value / s mm-2 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. S/S0 = f exp(-b.ADCbiexp) + (1-f) exp(-bD*)

  9. 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?

  10. 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. • Fitting to the last half of the plot gives us an estimate of ADC. Effect of original analysis was to return an average between ADC and D*. Not so very different from doing a two-point diffusion measurement! ln (S/S0) ln (S/S0) b-value / s mm-2 b-value / s mm-2 Further analysis (3): What did we measure? • However, results are severely biased by where the cutoff is chosen.

  11. 128  128 Number of pixels D* / 10-3 mm2 s-1 64  64 • 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*. 32  32 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

  12. r = 0.03, p = 0.012 r = 0.14, p = 0.143 ADCbiexp / 10-3 mm2 s-1 D* / 10-3 mm2 s-1 Tumour regression / % Tumour regression / % Further analysis of the data (4) • We then fitted an IVIM diffusion model to data for the tumour ROI’s. • 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. Conclusion 2: The (genuine) effect seen is not caused by D, as at first thought.

  13. r = 0.61, p = 0.012 Diffusion fraction Tumour regression / % 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.

  14. 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.

  15. 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.

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