A robust empirical parametrization of proton stopping power using dual energy CT.

PURPOSE In this study the authors present a new method for estimation of proton stopping power ratios (SPRs) using dual energy CT (DECT), which is robust toward CT noise. The authors propose a parametrization for SPR based directly on the CT numbers in a DECT image set, whereby the intermediate steps of estimating the relative electron density, ρe, and mean excitation energy, I, are avoided. METHODS The SPR parametrization proposed in this study is a purely empirical fit based on the theoretical SPR values for a list of 34 reference human tissues. To investigate the SPR estimation made with this new method the authors performed a calibration and an evaluation with the method. The authors initially calculated CT numbers using CT energy spectrum characterization parameters obtained from calibration based on a Gammex 467 electron density calibration phantom. These CT numbers were fitted to the theoretical SPR for the reference human tissues using the new SPR parametrization presented in this study. The method was evaluated based on theoretical CT numbers for the reference human tissues. The root-mean-square error (RMSE) of the SPR and the proton range error from the continuous slowing down approximation were calculated for the reference human tissues. To test the stability of the parametrization the authors varied the density and elemental composition of the reference human tissues and calculated their new SPR estimates. Further, clinically realistic noise values were added to the theoretical CT numbers to investigate how CT noise affected the estimated water equivalent range through 10 cm of the reference human tissues. All results for the new SPR parametrization were compared to the results obtained using two previously published DECT methods for SPR estimation. Comparisons were also made to a single energy CT (SECT) SPR estimation method, the stoichiometric method, which is commonly used in clinical practise for proton therapy treatment planning. RESULTS The RMSE for the SPR of the 34 reference human tissues using the new SPR parametrization was 0.12%, compared to 0.19% and 0.28% for the two previously published DECT methods. The SPR parametrization was more stable toward variations of the calcium content in the reference human tissues, but less stable toward density variations and changes to the hydrogen content than the two other DECT methods. When adding noise to the theoretical CT numbers the SPR parametrization gave the lowest water equivalent range errors of all four tested SPR estimation methods (maximum error reduced to 0.4 mm). In all cases tested, the new SPR parametrization outperformed the SECT stoichiometric method. CONCLUSIONS The new SPR parametrization gave lower RMSEs than the two other published DECT methods, and was in particular more robust against added noise. The method has potential for reducing range uncertainty margins in treatment planning of proton therapy.

[1]  R. A. Rutherford,et al.  Measurement of effective atomic number and electron density using an EMI scanner , 2004, Neuroradiology.

[2]  David J. Hawkes,et al.  X-ray attenuation coefficients of elements and mixtures , 1981 .

[3]  H. Woodard The Composition of Human Cortical Bone Effect of Age and of Some Abnormalities , 1964, Clinical orthopaedics and related research.

[4]  G. Poludniowski Calculation of x-ray spectra emerging from an x-ray tube. Part II. X-ray production and filtration in x-ray targets. , 2007, Medical physics.

[5]  D. R. White,et al.  The composition of body tissues (II). Fetus to young adult. , 1991, The British journal of radiology.

[6]  Steffen Greilich,et al.  Ion range estimation by using dual energy computed tomography. , 2013, Zeitschrift fur medizinische Physik.

[7]  Radhe Mohan,et al.  Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration , 2012, Physics in medicine and biology.

[8]  Steffen Greilich,et al.  Experimental verification of ion stopping power prediction from dual energy CT data in tissue surrogates , 2014, Physics in medicine and biology.

[9]  Frank Verhaegen,et al.  ImaSim, a software tool for basic education of medical x-ray imaging in radiotherapy and radiology , 2013, Front. Physics.

[10]  J. Petersen,et al.  Technical Note: Improving proton stopping power ratio determination for a deformable silicone-based 3D dosimeter using dual energy CT. , 2016, Medical physics.

[11]  Katia Parodi,et al.  Comparison of proton therapy treatment planning for head tumors with a pencil beam algorithm on dual and single energy CT images. , 2016, Medical physics.

[12]  B. Whiting,et al.  On two-parameter models of photon cross sections: application to dual-energy CT imaging. , 2006, Medical physics.

[13]  Paolo Farace,et al.  Experimental verification of ion stopping power prediction from dual energy CT data in tissue surrogates , 2014, Physics in medicine and biology.

[14]  Philip M Evans,et al.  Calculation of x-ray spectra emerging from an x-ray tube. Part I. electron penetration characteristics in x-ray targets. , 2007, Medical physics.

[15]  A. van der Schaaf,et al.  Relative electron density determination using a physics based parameterization of photon interactions in medical DECT , 2015, Physics in medicine and biology.

[16]  Frank Verhaegen,et al.  A simulation study on proton computed tomography (CT) stopping power accuracy using dual energy CT scans as benchmark , 2015, Acta oncologica.

[17]  M. Santillán,et al.  Bistable Behavior of the Lac Operon in E. Coli When Induced with a Mixture of Lactose and TMG , 2010, Front. Physiology.

[18]  F Verhaegen,et al.  SpekCalc: a program to calculate photon spectra from tungsten anode x-ray tubes , 2009, Physics in medicine and biology.

[19]  H. Paganetti Range uncertainties in proton therapy and the role of Monte Carlo simulations , 2012, Physics in medicine and biology.

[20]  R Mohan,et al.  Does kV–MV dual-energy computed tomography have an advantage in determining proton stopping power ratios in patients? , 2011, Physics in medicine and biology.

[21]  E R van der Graaf,et al.  Spectra of clinical CT scanners using a portable Compton spectrometer. , 2015, Medical physics.

[22]  Relationship between electron density and effective densities of body tissues for stopping, scattering, and nuclear interactions of proton and ion beams. , 2012, Medical physics.

[23]  J. Seco,et al.  Deriving effective atomic numbers from DECT based on a parameterization of the ratio of high and low linear attenuation coefficients , 2013, Physics in medicine and biology.

[24]  Scott N Penfold,et al.  Dosimetric comparison of stopping power calibration with dual-energy CT and single-energy CT in proton therapy treatment planning. , 2016, Medical physics.

[25]  M. Saito Potential of dual-energy subtraction for converting CT numbers to electron density based on a single linear relationship. , 2012, Medical physics.

[26]  B. R. Pullan,et al.  Calibration and response of an EMI scanner , 2004, Neuroradiology.

[27]  Range prediction for tissue mixtures based on dual-energy CT. , 2016, Physics in medicine and biology.

[28]  Ming Yang DUAL ENERGY COMPUTED TOMOGRAPHY FOR PROTON THERAPY TREATMENT PLANNING , 2011 .

[29]  E. Pedroni,et al.  The calibration of CT Hounsfield units for radiotherapy treatment planning. , 1996, Physics in medicine and biology.

[30]  Alexandra E Bourque,et al.  A stoichiometric calibration method for dual energy computed tomography , 2014, Physics in medicine and biology.

[31]  Jeffrey V Siebers,et al.  A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation. , 2016, Medical physics.

[32]  R. Mohan,et al.  Theoretical variance analysis of single- and dual-energy computed tomography methods for calculating proton stopping power ratios of biological tissues , 2010, Physics in medicine and biology.