ADC measurements on the Unity MR-linac – A recommendation on behalf of the Elekta Unity MR-linac consortium

Published:October 02, 2020DOI:


      • Machine characteristics must be considered when selecting b-values for measuring the ADC.
      • Spatial variation of the ADC requires positioning of a ROI near the iso-center.
      • The ADC can be measured on the Unity system during gantry rotation and irradiation.
      • Includes example acquisition parameters to facilitate multi-center research.


      Background and purpose

      Diffusion-weighted imaging (DWI) for treatment response monitoring is feasible on hybrid magnetic resonance linear accelerator (MR-linac) systems. The MRI scanner of the Elekta Unity system has an adjusted design compared to diagnostic scanners. We investigated its impact on measuring the DWI-derived apparent diffusion coefficient (ADC) regarding three aspects: the choice of b-values, the spatial variation of the ADC, and scanning during radiation treatment. The aim of this study is to give recommendations for accurate ADC measurements on Unity systems.

      Materials and methods

      Signal-to-noise ratio (SNR) measurements with increasing b-values were done to determine the highest bvalue that can be measured reliably. The spatial variation of the ADC was assessed on six Unity systems with a cylindrical phantom of 40 cm diameter. The influence of gantry rotation and irradiation was investigated by acquiring DWI images before and during treatment of 11 prostate cancer patients.


      On the Unity system, a maximum b-value of 500 s/mm2 should be used for ADC quantification, as a trade-off between SNR and diffusion weighting. Accurate ADC values were obtained within 7 cm from the iso-center, while outside this region ADC values deviated more than 5%. The ADC was not influenced by the rotating linac or irradiation during treatment.


      We provide Unity system specific recommendations for measuring the ADC. This will increase the consistency of ADC values acquired in different centers on the Unity system, enabling large cohort studies for biomarker discovery and treatment response monitoring.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic and Personal
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Radiotherapy and Oncology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Gurney-Champion O.J.
        • Mahmood F.
        • van Schie M.
        • Julian R.
        • George B.
        • Philippens M.E.P.
        • et al.
        Quantitative imaging for radiotherapy purposes.
        Radiother Oncol. 2020; 146: 66-75
        • Keenan K.E.
        • Biller J.R.
        • Delfino J.G.
        • Boss M.A.
        • Does M.D.
        • Evelhoch J.L.
        • et al.
        Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.
        J Magn Reson Imaging. 2019; 49: e26-e39
        • Keenan K.E.
        • Ainslie M.
        • Barker A.J.
        • Boss M.A.
        • Cecil K.M.
        • Charles C.
        • et al.
        Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom.
        Magn Reson Med. 2017;
        • O’Connor J.P.B.
        • Aboagye E.O.
        • Adams J.E.
        • Aerts H.J.W.L.
        • Barrington S.F.
        • Beer A.J.
        • et al.
        Imaging biomarker roadmap for cancer studies.
        Nat Rev Clin Oncol. 2017; 14: 169-186
        • Obuchowski N.A.
        • Buckler A.
        • Kinahan P.
        • Chen-Mayer H.
        • Petrick N.
        • Barboriak D.P.
        • et al.
        Statistical issues in testing conformance with the quantitative imaging biomarker alliance (QIBA) profile claims.
        Acad Radiol. 2016; 23: 496-506
        • Mahmood F.
        • Johannesen H.H.
        • Geertsen P.
        • Hansen R.H.
        Repeated diffusion MRI reveals earliest time point for stratification of radiotherapy response in brain metastases.
        Phys Med Biol. 2017; 62: 2990-3002
        • Yang Y.
        • Cao M.
        • Sheng K.
        • Gao Y.
        • Chen A.
        • Kamrava M.
        • et al.
        Longitudinal diffusion MRI for treatment response assessment: Preliminary experience using an MRI-guided tri-cobalt 60 radiotherapy system.
        Med Phys. 2016; 43: 1369-1373
        • Lambrecht M.
        • Vandecaveye V.
        • De Keyzer F.
        • Roels S.
        • Penninckx F.
        • Van Cutsem E.
        • et al.
        Value of diffusion-weighted magnetic resonance imaging for prediction and early assessment of response to neoadjuvant radiochemotherapy in rectal cancer: Preliminary results.
        Int J Radiat Oncol Biol Phys. 2012; 82: 863-870
        • Lambrecht M.
        • Van Herck H.
        • De Keyzer F.
        • Vandecaveye V.
        • Slagmolen P.
        • Suetens P.
        • et al.
        Redefining the target early during treatment. Can we visualize regional differences within the target volume using sequential diffusion weighted MRI?.
        Radiother Oncol. 2014; 110: 329-334
        • Park S.Y.
        • Kim C.K.
        • Park B.K.
        • Park W.
        • Park H.C.
        • Han D.H.
        • et al.
        Early changes in apparent diffusion coefficient from diffusion-weighted MR imaging during radiotherapy for prostate cancer.
        Int J Radiat Oncol Biol Phys. 2012; 83: 749-755
        • Liu Y.
        • Bai R.
        • Sun H.
        • Liu H.
        • Zhao X.
        • Li Y.
        Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation.
        Clin Radiol. 2009; 64: 1067-1074
        • Padhani A.R.
        • Liu G.
        • Mu-Koh D.
        • Chenevert T.L.
        • Thoeny H.C.
        • Takahara T.
        • et al.
        Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations.
        Neoplasia. 2009; 11: 102-125
        • Perfusion, Diffusion and Flow-MRI Biomarker Committee
        QIBA Profile: diffusion-weighted magnetic resonance imaging (DWI). Public comment draft.
        QIBA, 2019
      1. Baltzer AP, Mann RM, Iima M, Sigmund EE, Clauser P, Gilbert F. Diffusion-Weighted Imaging of the breast – A consensus and mission statement from the EUSOBI International Breast Diffusion- Weighted Imaging working group 2019:1–25.

        • Raaymakers B.W.
        • Lagendijk J.J.W.
        • Overweg J.
        • Kok J.G.M.
        • Raaijmakers A.J.E.
        • Kerkhof E.M.
        • et al.
        Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept.
        Phys Med Biol. 2009; 54: N229-N237
        • Mutic S.
        • Dempsey J.F.
        The ViewRay system: magnetic resonance-guided and controlled radiotherapy.
        Semin Radiat Oncol. 2014; 24: 196-199
        • Fallone B.G.
        The rotating biplanar linac-magnetic resonance imaging system.
        Semin Radiat Oncol. 2014; 24: 200-202
        • Keall P.J.
        • Barton M.
        • Crozier S.
        The Australian magnetic resonance imaging–linac program.
        Semin Radiat Oncol. 2014; 24: 203-206
        • Kooreman E.S.
        • van Houdt P.J.
        • Nowee M.E.
        • van Pelt V.W.J.
        • Tijssen R.H.N.
        • Paulson E.S.
        • et al.
        Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator.
        Radiother Oncol. 2019; 133: 156-162
        • Stejskal E.O.
        • Tanner J.E.
        Spin diffusion measurements: spin echoes in the presence of a time‐dependent field gradient.
        J Chem Phys. 1965; 42: 288-292
        • Hoogcarspel S.J.
        • Zijlema S.E.
        • Tijssen R.H.N.
        • Kerkmeijer L.G.W.
        • Jürgenliemk-Schulz I.M.
        • Lagendijk J.J.W.
        • et al.
        Characterization of the first RF coil dedicated to 1.5 T MR guided radiotherapy.
        Phys Med Biol. 2018; : 63
        • Gudbjartsson H.
        • Patz S.
        The Rician distribution of noisy MRI data (vol 34, pg 910, 1995).
        Magn Reson Med. 1996; 36: 332
        • Tijssen R.H.N.
        • Philippens M.E.P.
        • Paulson E.S.
        • Glitzner M.
        • Chugh B.
        • Wetscherek A.
        • et al.
        MRI commissioning of 1.5T MR-linac systems – a multi-institutional study.
        Radiother Oncol. 2019; 132: 114-120
        • Baig T.N.
        • Eagan T.P.
        • Petropoulos L.S.
        • Kidane T.K.
        • Edelstein W.A.
        • Brown R.W.
        Gradient coil with active endcap shielding.
        Concepts Magn Reson. 2007; 31B: 12-23
        • Chan R.W.
        • von Deuster C.
        • Giese D.
        • Stoeck C.T.
        • Harmer J.
        • Aitken A.P.
        • et al.
        Characterization and correction of eddy-current artifacts in unipolar and bipolar diffusion sequences using magnetic field monitoring.
        J Magn Reson. 2014; 244: 74-84
        • Meier C.
        • Zwanger M.
        • Feiweier T.
        • Porter D.
        Concomitant field terms for asymmetric gradient coils: consequences for diffusion, flow, and echo-planar imaging.
        Magn Reson Med. 2008; 60: 128-134
        • Bammer R.
        • Markl M.
        • Barnett A.
        • Acar B.
        • Alley M.T.
        • Pelc N.J.
        • et al.
        Analysis and generalized correction of the effect of spatial gradient field distortions in diffusion-weighted imaging.
        Magn Reson Med. 2003; 50: 560-569
        • Malyarenko D.
        • Galbán C.J.
        • Londy F.J.
        • Meyer C.R.
        • Johnson T.D.
        • Rehemtulla A.
        • et al.
        Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom.
        J. Magn. Reson. Imaging. 2013; 37: 1238-1246
        • Jackson S.J.
        • Glitzner M.
        • Tijssen R.H.N.
        • Raaymakers B.W.
        MRI B0 homogeneity and geometric distortion with continuous linac gantry rotation on an Elekta Unity MR-linac.
        Phys Med Biol. 2019;
        • Le Bihan D.
        • Breton E.
        • Lallemand D.
        • Grenier P.
        • Cabanis E.
        • Laval-Jeantet M.
        MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders..
        Radiology. 1986; 161: 401-407
        • Le Bihan D.
        What can we see with IVIM MRI?.
        NeuroImage. 2019; 187: 56-67
        • Lemke A.
        • Laun F.B.
        • Simon D.
        • Stieltjes B.
        • Schad L.R.
        An in vivo verification of the intravoxel incoherent motion effect in diffusion-weighted imaging of the abdomen: Verification of the IVIM Theory.
        Magn Reson Med. 2010; 64: 1580-1585
        • de Bazelaire C.M.J.
        • Duhamel G.D.
        • Rofsky N.M.
        • Alsop D.C.
        MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results.
        Radiology. 2004; 230: 652-659
        • Riches S.F.
        • Hawtin K.
        • Charles-Edwards E.M.
        • de Souza N.M.
        Diffusion-weighted imaging of the prostate and rectal wall: Comparison of biexponential and monoexponential modelled diffusion and associated perfusion coefficients.
        NMR Biomed. 2009; 22: 318-325
        • Shukla‐Dave A.
        • Obuchowski N.A.
        • Chenevert T.L.
        • Jambawalikar S.
        • Schwartz L.H.
        • Malyarenko D.
        • et al.
        Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials.
        J Magn Reson Imaging. 2019; 49: e101-e121
        • Barnhart H.X.
        • Barboriak D.P.
        Applications of the repeatability of quantitative imaging biomarkers: a review of statistical analysis of repeat data sets.
        Transl Oncol. 2009; 2: 231-235
        • Dietrich O.
        • Heiland S.
        • Sartor K.
        Noise correction for the exact determination of apparent diffusion coefficients at low SNR.
        Magn Reson Med. 2001; 45: 448-453<448::AID-MRM1059>3.0.CO;2-W
      2. Bito Y, Hirata S, Yamamoto E. Optimal gradient factors for ADC measurements. B. Abstr Third Annu Meet Int Soc Magn Resnance Med. ISMRM, Berkeley, CA: ISMRM; 1995, p. 913.

        • Saritas E.U.
        • Lee J.H.
        • Nishimura D.G.
        SNR dependence of optimal parameters for apparent diffusion coefficient measurements.
        IEEE Trans Med Imaging. 2011; 30: 424-437