Research Article| Volume 105, 102507, January 2023

Investigation of a water equivalent depth method for dosimetric accuracy evaluation of synthetic CT

Published:December 17, 2022DOI:


      • Conventional image similarity metrics are insufficient to assess the quality of sCT.
      • Water equivalent depth (WED) calculation can evaluate dosimetric quality of sCT.
      • The mean WED difference ( Δ W E D ¯ ) between the sCT and reference CT can be calculated.
      • The Δ W E D ¯ can facilitate the development process of the new sCT generation method.



      To provide a metric that reflects the dosimetric utility of the synthetic CT (sCT) and can be rapidly determined.


      Retrospective CT and atlas-based sCT of 62 (53 IMRT and 9 VMAT) prostate cancer patients were used. For image similarity measurements, the sCT and reference CT (rCT) were aligned using clinical registration parameters. Conventional image similarity metrics including the mean absolute error (MAE) and mean error (ME) were calculated. The water equivalent depth (WED) was automatically determined for each patient on the rCT and sCT as the distance from the skin surface to the treatment plan isocentre at 36 equidistant gantry angles, and the mean WED difference ( Δ W E D ¯ ) between the two scans was calculated. Doses were calculated on each scan pair for the clinical plan in the treatment planning system. The image similarity measurements and Δ W E D ¯ were then compared to the isocentre dose difference ( Δ D iso ) between the two scans.


      While no particular relationship to dose was observed for the other image similarity metrics, the ME results showed a linear trend against Δ D iso with R2 = 0.6, and the 95 % prediction interval for Δ D iso between −1.2 and 1 %. The Δ W E D ¯ results showed an improved linear trend (R2 = 0.8) with a narrower 95 % prediction interval from −0.8 % to 0.8 %.


      Δ W E D ¯ highly correlates with Δ D iso for the reference and synthetic CT scans. This is easy to calculate automatically and does not require time-consuming dose calculations. Therefore, it can facilitate the process of developing and evaluating new sCT generation algorithms.


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

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      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 Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Owrangi AM
        • Greer PB
        Glide-Hurst CK. MRI-only treatment planning: benefits and challenges.
        Phys Med Biol. 2018; 63: 05TR1
        • Arabi H.
        • Dowling J.A.
        • Burgos N.
        • Han X.
        • Greer P.B.
        • Koutsouvelis N.
        • et al.
        Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region.
        Med Phys. 2018; 45: 5218-5233
        • Dowling J.A.
        • Lambert J.
        • Parker J.
        • Salvado O.
        • Fripp J.
        • Capp A.
        • et al.
        An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy.
        Int J Radiat Oncol Biol Phys. 2012; 83: e5-e11
        • Dowling J.A.
        • Sun J.
        • Pichler P.
        • Rivest-Hénault D.
        • Ghose S.
        • Richardson H.
        • et al.
        Automatic substitute computed tomography generation and contouring for magnetic resonance imaging (MRI)-alone external beam radiation therapy from standard MRI sequences.
        Int J Radiat Oncol Biol Phys. 2015; 93: 1144-1153
        • Edmund J.M.
        • Nyholm T.
        A review of substitute CT generation for MRI-only radiation therapy.
        Radiat Oncol. 2017; 12: 1-15
        • Dinkla A.M.
        • Florkow M.C.
        • Maspero M.
        • Savenije M.H.F.
        • Zijlstra F.
        • Doornaert P.A.H.
        • et al.
        Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network.
        Med Phys. 2019; 46: 4095-4104
        • Kazemifar S.
        • McGuire S.
        • Timmerman R.
        • Wardak Z.
        • Nguyen D.
        • Park Y.
        • et al.
        MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach.
        Radiat Oncol J. 2019; 136: 56-63
        • Liu F.
        • Yadav P.
        • Baschnagel A.M.
        • McMillan A.B.
        MR-based treatment planning in radiation therapy using a deep learning approach.
        J Appl Clin Med Phys. 2019; 20: 105-114
        • Liu Y.
        • Lei Y.
        • Wang T.
        • Kayode O.
        • Tian S.
        • Liu T.
        • et al.
        MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method.
        Br J Radiol. 2019; 92: 20190067
        • Olberg S.
        • Zhang H.
        • Kennedy W.R.
        • Chun J.
        • Rodriguez V.
        • Zoberi I.
        • et al.
        Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.
        Med Phys. 2019; 46: 4135-4147
        • Florkow M.C.
        • Guerreiro F.
        • Zijlstra F.
        • Seravalli E.
        • Janssens G.O.
        • Maduro J.H.
        • et al.
        Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours.
        Radiat Oncol J. 2020; 153: 220-227
      1. Fu J, Singhrao K, Cao M, Yu V, Santhanam AP, Yang Y, et al. Generation of abdominal synthetic CTs from 0.35 T MR images using generative adversarial networks for MR-only liver radiotherapy. Biomed Phys Eng Express. 2020;6:015033.

        • Maspero M.
        • Bentvelzen L.G.
        • Savenije M.H.F.
        • Guerreiro F.
        • Seravalli E.
        • Janssens G.O.
        • et al.
        Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.
        Radiat Oncol J. 2020; 153: 197-204
        • Qi M.
        • Li Y.
        • Wu A.
        • Jia Q.
        • Li B.
        • Sun W.
        • et al.
        Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.
        Med Phys. 2020; 47: 1880-1894
        • Tang B.
        • Wu F.
        • Fu Y.
        • Wang X.
        • Wang P.
        • Orlandini L.C.
        • et al.
        Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy.
        J Appl Clin Med Phys. 2021; 22: 55-62
        • Bahrami A.
        • Karimian A.
        • Arabi H.
        Comparison of different deep learning architectures for synthetic CT generation from MR images.
        Phys Med. 2021; 90: 99-107
        • Boulanger M.
        • Nunes J.-C.
        • Chourak H.
        • Largent A.
        • Tahri S.
        • Acosta O.
        • et al.
        Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review.
        Phys Med. 2021; 89: 265-281
        • Greer P.
        • Martin J.
        • Sidhom M.
        • Hunter P.
        • Pichler P.
        • Choi J.H.
        • et al.
        A Multi-center Prospective Study for Implementation of an MRI-Only Prostate Treatment Planning Workflow.
        Front. Oncol. 2019; 9
        • Greer P.
        • Skehan K.
        • Goodwin J.
        • Dowling J.
        • Choi J.H.
        • Sidhom M.
        • et al.
        A Multi-Centre Study of MRI-Only Prostate Radiation Therapy Planning: A NINJA Trial Sub-Study. Asia Pac.
        J Clin Oncol. 2019;
        • Wang Z.
        • Bovik A.C.
        • Sheikh H.R.
        • Simoncelli E.P.
        Image quality assessment: from error visibility to structural similarity.
        IEEE Trans Image Process. 2004; 13: 600-612
        • Siebers J.V.
        • Keall P.J.
        • Nahum A.E.
        • Mohan R.
        Converting absorbed dose to medium to absorbed dose to water for Monte Carlo based photon beam dose calculations.
        Phys Med Biol. 2000; 45: 983-995
        • Chetty I.J.
        • Curran B.
        • Cygler J.E.
        • DeMarco J.J.
        • Ezzell G.
        • Faddegon B.A.
        • et al.
        Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning.
        Med Phys. 2007; 34: 4818-4853
        • Ma C.-M.
        • Li J.
        Dose specification for radiation therapy: dose to water or dose to medium?.
        Phys Med Biol. 2011; 56: 3073-3089
        • Khan F.M.
        The Physics of Radiation Therapy.
        Williams & Wilkins. 1994;
        • Metcalfe P.
        • Kron T.
        • Hoban P.
        The Physics of Radiotherapy X-rays and Electrons.
        Med Phys Pub. 2007;
        • Palmér E.
        • Persson E.
        • Ambolt P.
        • Gustafsson C.
        • Gunnlaugsson A.
        • Olsson L.E.
        Cone beam CT for QA of synthetic CT in MRI only for prostate patients.
        J Appl Clin Med Phys. 2018; 19: 44-52
        • Choi J.H.
        • Lee D.
        • O’Connor L.
        • Chalup S.
        • Welsh J.S.
        • Dowling J.
        • et al.
        Bulk Anatomical Density Based Dose Calculation for Patient-Specific Quality Assurance of MRI-Only Prostate Radiotherapy.
        Front. Oncol. 2019; 9
        • Eckl M.
        • Hoppen L.
        • Sarria G.R.
        • Boda-Heggemann J.
        • Simeonova-Chergou A.
        • Steil V.
        • et al.
        Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy.
        Phys Med. 2020; 80: 308-316