Towards a generalised development of synthetic CT images and assessment of their dosimetric accuracy

Abstract Background: The interest in generating “synthetic computed tomography (CT) images” from magnetic resonance (MR) images has been increasing over the past years due to advances in MR guidance for radiotherapy. A variety of methods for synthetic CT creation have been developed, from simple bulk density assignment to complex machine learning algorithms. Material and methods: In this study, we present a general method to determine simplistic synthetic CTs and evaluate them according to their dosimetric accuracy. It separates the requirements on the MR image and the associated calculation effort to generate a synthetic CT. To evaluate the significance of the dosimetric accuracy under realistic conditions, clinically common uncertainties including position shifts and Hounsfield lookup table (HLUT) errors were simulated. To illustrate our approach, we first translated CT images from a test set of six pelvic cancer patients to relative electron density (ED) via a clinical HLUT. For each patient, seven simplified ED images (simED) were generated at different levels of complexity, ranging from one to four tissue classes. Then, dose distributions optimised on the reference ED image and the simEDs were compared to each other in terms of gamma pass rates (2 mm/2% criteria) and dose volume metrics. Results: For our test set, best results were obtained for simEDs with four tissue classes representing fat, soft tissue, air, and bone. For this simED, gamma pass rates of 99.95% (range: 99.72–100%) were achieved. The decrease in accuracy from ED simplification was smaller in this case than the influence of the uncertainty scenarios on the reference image, both for gamma pass rates and dose volume metrics. Conclusions: The presented workflow helps to determine the required complexity of synthetic CTs with respect to their dosimetric accuracy. The investigated cases showed potential simplifications, based on which the synthetic CT generation could be faster and more reproducible.

[1]  Margie Hunt,et al.  Dosimetric and workflow evaluation of first commercial synthetic CT software for clinical use in pelvis , 2017, Physics in medicine and biology.

[2]  Alan Pollack,et al.  MRI-based treatment planning for radiotherapy: dosimetric verification for prostate IMRT. , 2004, International journal of radiation oncology, biology, physics.

[3]  Olivier Salvado,et al.  MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[4]  Ming Dong,et al.  Generating synthetic CTs from magnetic resonance images using generative adversarial networks , 2018, Medical physics.

[5]  D. Low,et al.  A technique for the quantitative evaluation of dose distributions. , 1998, Medical physics.

[6]  Carri Glide-Hurst,et al.  Implementation of a novel algorithm for generating synthetic CT images from magnetic resonance imaging data sets for prostate cancer radiation therapy. , 2015, International journal of radiation oncology, biology, physics.

[7]  Oliver Jäkel,et al.  Prospective feasibility analysis of a novel off-line approach for MR-guided radiotherapy , 2018, Strahlentherapie und Onkologie.

[8]  R. Hoogeveen,et al.  MR-only simulation for radiotherapy planning , 2015 .

[9]  Oliver Jäkel,et al.  Generation of synthetic CT data using patient specific daily MR image data and image registration , 2017, Physics in medicine and biology.

[10]  H. Kjer,et al.  A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times , 2014, Physics in medicine and biology.

[11]  Fredrik Nordström,et al.  Technical Note: MRI only prostate radiotherapy planning using the statistical decomposition algorithm. , 2015, Medical physics.

[12]  Xiao Han,et al.  MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.

[13]  Adam Johansson,et al.  Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information – potential application for MR-only radiotherapy and attenuation correction in positron emission tomography , 2013, Acta oncologica.

[14]  Tiina Seppälä,et al.  MRI-only based radiation therapy of prostate cancer: workflow and early clinical experience , 2018, Acta oncologica.

[15]  Richard Pötter,et al.  Aspects of MR Image Distortions in Radiotherapy Treatment Planning , 2001, Strahlentherapie und Onkologie.

[16]  Fredrik Nordström,et al.  MR-OPERA: A Multicenter/Multivendor Validation of Magnetic Resonance Imaging-Only Prostate Treatment Planning Using Synthetic Computed Tomography Images. , 2017, International journal of radiation oncology, biology, physics.

[17]  Katia Parodi,et al.  Development of the open‐source dose calculation and optimization toolkit matRad , 2017, Medical physics.

[18]  Melanie Traughber,et al.  Evaluating organ delineation, dose calculation and daily localization in an open-MRI simulation workflow for prostate cancer patients , 2015, Radiation Oncology.

[19]  Tufve Nyholm,et al.  Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions , 2010, Radiation oncology.

[20]  Jeffrey A Fessler,et al.  Female pelvic synthetic CT generation based on joint intensity and shape analysis. , 2017, Physics in medicine and biology.

[21]  Hugues Benoit-Cattin,et al.  Intensity non-uniformity correction in MRI: Existing methods and their validation , 2006, Medical Image Anal..

[22]  Olivier Salvado,et al.  An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. , 2012, International journal of radiation oncology, biology, physics.

[23]  E. Yorke,et al.  Use of normal tissue complication probability models in the clinic. , 2010, International journal of radiation oncology, biology, physics.

[24]  Josef A. Lundman,et al.  Zero TE‐based pseudo‐CT image conversion in the head and its application in PET/MR attenuation correction and MR‐guided radiation therapy planning , 2018, Magnetic resonance in medicine.

[25]  Lei Xing,et al.  Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. , 2017, International journal of radiation oncology, biology, physics.

[26]  Tiina Seppälä,et al.  A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer. , 2013, Medical physics.

[27]  Jonathan J Wyatt,et al.  Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy. , 2018, International journal of radiation oncology, biology, physics.

[28]  S. Delorme,et al.  MR-guidance – a clinical study to evaluate a shuttle- based MR-linac connection to provide MR-guided radiotherapy , 2014, Radiation oncology.

[29]  Steve Webb,et al.  Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone. , 2003, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[30]  Christopher M. Rank,et al.  MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach , 2013, Radiation Oncology.