Ion range estimation by using dual energy computed tomography.

Inaccurate conversion of CT data to water-equivalent path length (WEPL) is one of the most important uncertainty sources in ion treatment planning. Dual energy CT (DECT) imaging might help to reduce CT number ambiguities with the additional information. In our study we scanned a series of materials (tissue substitutes, aluminum, PMMA, and other polymers) in the dual source scanner (Siemens Somatom Definition Flash). Based on the 80kVp/140SnkVp dual energy images, the electron densities ϱe and effective atomic numbers Zeff were calculated. We introduced a new lookup table that translates the ϱe to the WEPL. The WEPL residuals from the calibration were significantly reduced for the investigated tissue surrogates compared to the empirical Hounsfield-look-up table (single energy CT imaging) from (-1.0±1.8)% to (0.1±0.7)% and for non-tissue equivalent PMMA from -7.8% to -1.0%. To assess the benefit of the new DECT calibration, we conducted a treatment planning study for three different idealized cases based on tissue surrogates and PMMA. The DECT calibration yielded a significantly higher target coverage in tissue surrogates and phantom material (i.e. PMMA cylinder, mean target coverage improved from 62% to 98%). To verify the DECT calibration for real tissue, ion ranges through a frozen pig head were measured and compared to predictions calculated by the standard single energy CT calibration and the novel DECT calibration. By using this method, an improvement of ion range estimation from -2.1% water-equivalent thickness deviation (single energy CT) to 0.3% (DECT) was achieved. If one excludes raypaths located on the edge of the sample accompanied with high uncertainties, no significant difference could be observed.

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

[2]  R. Brooks A Quantitative Theory of the Hounsfield Unit and Its Application to Dual Energy Scanning , 1977, Journal of computer assisted tomography.

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

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

[5]  F. Verhaegen,et al.  Dual-energy CT-based material extraction for tissue segmentation in Monte Carlo dose calculations , 2008, Physics in medicine and biology.

[6]  R A Brooks,et al.  Explanation of cerebral white--gray contrast in computed tomography. , 1980, Journal of computer assisted tomography.

[7]  K. Stierstorfer,et al.  First performance evaluation of a dual-source CT (DSCT) system , 2006, European Radiology.

[8]  O Jäkel,et al.  Treatment planning for heavy-ion radiotherapy: physical beam model and dose optimization. , 2000, Physics in medicine and biology.

[9]  T. Bortfeld,et al.  Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. , 2000, Physics in medicine and biology.

[10]  J. H. Hubbell,et al.  XCOM : Photon Cross Sections Database , 2005 .

[11]  Daniel Kolditz,et al.  A formulation of tissue- and water-equivalent materials using the stoichiometric analysis method for CT-number calibration in radiotherapy treatment planning , 2012, Physics in medicine and biology.

[12]  Thomas Haberer,et al.  Radiation Oncology BioMed Central , 2006 .

[13]  M. F. Reiser,et al.  Dual energy CT in clinical practice , 2011 .

[14]  R. Cloutier Tissue Substitutes in Radiation Dosimetry and Measurement. , 1989 .

[15]  O. Jäkel,et al.  Monte Carlo simulations on the water-to-air stopping power ratio for carbon ion dosimetry. , 2009, Medical physics.

[16]  Pedro Andreo,et al.  On the clinical spatial resolution achievable with protons and heavier charged particle radiotherapy beams , 2009, Physics in medicine and biology.

[17]  O Jäkel,et al.  Relation between carbon ion ranges and x-ray CT numbers. , 2001, Medical physics.

[18]  Rainer Raupach,et al.  Erratum: First performance evaluation of a dual-source CT (DSCT) system (European Radiology (2006) vol. 16 (2) (256-268) 10.1007/ s00330-005-2919-2) , 2006 .

[19]  E Pedroni,et al.  The precision of proton range calculations in proton radiotherapy treatment planning: experimental verification of the relation between CT-HU and proton stopping power. , 1998, Physics in medicine and biology.

[20]  J. Wildberger,et al.  Extracting atomic numbers and electron densities from a dual source dual energy CT scanner: experiments and a simulation model. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[21]  M. Reiser,et al.  Material differentiation by dual energy CT: initial experience , 2007, European Radiology.

[22]  G. Chen,et al.  Treatment planning for heavy ion radiotherapy. , 1979, International journal of radiation oncology, biology, physics.

[23]  Konstantin Nikolaou,et al.  Dual Energy CT of the Chest: How About the Dose? , 2010, Investigative radiology.

[24]  D. R. White,et al.  Average soft-tissue and bone models for use in radiation dosimetry. , 1987, The British journal of radiology.

[25]  A. Macovski,et al.  Energy-selective reconstructions in X-ray computerised tomography , 1976, Physics in medicine and biology.

[26]  V. Nečas,et al.  Investigation of the electronic energy loss of hydrogen ions in H2O: influence of the state of aggregation , 1994 .

[27]  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.

[28]  A consistent dielectric response model for water ice over the whole energy–momentum plane , 2007 .

[29]  D. R. White,et al.  The composition of body tissues. , 1986, The British journal of radiology.