Diffusion-Weighted (DW) MRI can be used as a quantitative assessment in oncological imaging alongside conventional MRI¹, with the potential to be used as a Quantitative Imaging Biomarker (QIB) where low ADC may correlate with high cellularity associated with tumours. However, there is a lack of data evidencing its accuracy and reproducibility in a wide range of applications, particularly in whole body imaging (WBI). As QIB, it should demonstrate accuracy and reproducibility in lesion detection, utility in critical decision-making and treatment/therapy response in clinical drug trials^2–5. Previous studies have shown that ADC reproducibility is known to be affected by scanner type, acquisition parameters^6,7 and fat suppression (FS) techniques⁸. It is important to distinguish fluctuations in ADC from the biologically relevant factors such as the microenvironment variations at cellular level². Reproducibility can therefore be assisted by ensuring consistency in acquisition methods^6,7. However, it may also be possible to further reduce variability by using a reference tissue region to normalise tumour ADC values^9,10 as, previously reported.

1) To investigate the effect of a range of acquisition protocols including imaging parameters, FS techniques and MRI vendor on ADC variability in a pragmatic multi-centre whole body MRI trial. 2) To examine the effect of normalisation methods to minimise ADC variability in a range of tissue types.

Retrospective ROI analysis was carried out on the WB-ADC maps from the STREAMLINE study database^13,14 (on-going multicentre studies of WB-MRI staging in lung and colon cancer patients). 77 datasets (55% male) were selected, which was acquired on four different scanners (three vendors) at two field strengths (scanning parameters table1). A research fellow placed spherical ROIs in brain (both sides), bone, muscle, liver and spleen to extract mean ADC values in specific tissue types. The group (by scanner) mean values for each ROI were used to calculate the Coefficient of Variance (CoV) as a measure of reproducibility across scanners and subjects. To investigate the usefulness of normalisation an additional ROI was drawn for each subject and the mean ADC value was extracted and used as reference. Specifically tissues used for normalisation were spleen for the liver, and psoas muscle for bone as previously described^9,10. In addition the psoas muscle was also used for liver, and the spleen for bone to investigate the effectiveness of the reference tissue outside the acquisition FOV.The CoV across subjects for each tissue type in each scanner with and without regional tissue normalisation is shown in table 2 and the mean and SD of ADC is plotted in figure1. Mean ADC varied according to tissue type across the 4 scanners (Figure1). Coefficient of Variance was lowest with brain and greatest in bone (table2). In particular, mean bone ADC was highest in scanners using STIR FS compared to those using SPIR and SPAIR FS. There was no consistent effect of tesla field strength on tissue ADC. Variation in mean tissue ADC was least when scanner C and D (same vendor, 1.5T and STIR FS) were compared. For 3T platforms (difference in vendor), the range of ADC measurement for each normal tissue was greatest for scanner A, which used SPIR FS compared to scanner B which used SPAIR FS. The CoV was greatest in bone for all 4 scanners. The within FOV normalisation method has no effect in reducing the CoV for 3 of the 4 scanners, but there was some benefit for scanner B. Using a tissue of interest outside the FOV added even more variability.We have demonstrated significant variability in mean ADC of normal tissues (particularly bone) when measured on different MRI scanners. Furthermore, patient variability differed according to the individual scanner. Reference tissue normalisation (even using reference tissue within FOV) had no benefit in 3 of 4 scanners. Variability can be explained by differences in scanning parameters, vendors and in particular we found some evidence that FS technique influenced variability, particularly for bone. We speculate this may be due to different FS methods (unsuppressed olefinic fat using spectral methods¹¹). SPIR FS (scanner A) seemed to induce greater variation in ADC values¹². Additional factors such as differences in vendor coil technology may also have an effect. In a non-optimized/standardized multi-centre clinical trial there is substantial variation in ADC values across different individuals within and across scanners as measured by CoV. Varying fat suppression techniques are a likely component of the differences seen between vendors. Normalisation to a reference tissue in post-processing showed limited benefit on normal tissue. In quantitative ADC imaging harmonization of acquisition parameters may be the most effective method for minimising variance.