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

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. 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)

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

5. 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. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0510152025303540 Echo Number A / A0 Data for CuSO 4 T 2 and D double fit Doran and Décorps, JMR A, 117(2), 311–316 (1995)

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 T 2 decay during the acquisition.

7. 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 T 2 “correction”. Standard multi-echo sequences measure an incorrect T 2 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 / % ADC mono / 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.

8. 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/S 0 = f exp(-b.ADC biexp ) + (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 T 2 measurements in our “correction scan” or motion artifacts?

10. 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.

11. 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* / 10  3 mm 2 s  1 Number of pixels 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*.

12. 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 / % ADC biexp / 10  3 mm 2 s  1 r = 0.03, p = 0.012 Tumour regression / % D* / 10  3 mm 2 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.

13. 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 ADC mono measured with a mono-exponential model decreases with increasing tumour regression. r = 0.61, p = 0.012 Tumour regression / % Diffusion fraction

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 T 2 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.