Comparison of single and dual energy CT for stopping power determination in proton therapy of head and neck cancer

Background and purpose Patients with head and neck (HN) cancer may benefit from proton therapy due to the potential for sparing of normal tissue. For planning of proton therapy, dual-energy CT (DECT) has been shown to provide superior stopping power ratio (SPR) determination in phantom materials and organic tissue samples, compared to single-energy CT (SECT). However, the benefit of DECT in HN cancer patients has not yet been investigated. This study therefore compared DECT- and SECT-based SPR estimation for HN cancer patients. Materials and methods Fourteen HN cancer patients were DECT scanned. Eight patients were scanned using a dual source DECT scanner and six were scanned with a conventional SECT scanner by acquiring two consecutive scans. SECT image sets were computed as a weighted summation of the low and high energy DECT image sets. DECT- and SECT-based SPR maps were derived. Water-equivalent path lengths (WEPLs) through the SPR maps were compared in the eight cases with dual source DECT scans. Mean SPR estimates over region-of-interests (ROIs) in the cranium, brain and eyes were analyzed for all patients. Results A median WEPL difference of 1.9 mm (1.5%) was found across the eight patients. Statistically significant SPR differences were seen for the ROIs in the brain and eyes, with the SPR estimates based on DECT overall lower than for SECT. Conclusions Clinically relevant WEPL and SPR differences were found between DECT and SECT, which could imply that the accuracy of treatment planning for proton therapy would benefit from DECT-based SPR estimation.

[1]  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.

[2]  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.

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

[4]  J Schuemann,et al.  Site-specific range uncertainties caused by dose calculation algorithms for proton therapy , 2014, Physics in medicine and biology.

[5]  Steffen Greilich,et al.  Dual-energy CT based proton range prediction in head and pelvic tumor patients. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[6]  Cynthia H McCollough,et al.  Image quality optimization and evaluation of linearly mixed images in dual-source, dual-energy CT. , 2009, Medical physics.

[7]  Johannes A Langendijk,et al.  The potential benefit of radiotherapy with protons in head and neck cancer with respect to normal tissue sparing: a systematic review of literature. , 2011, The oncologist.

[8]  Vicki T Taasti,et al.  A robust empirical parametrization of proton stopping power using dual energy CT. , 2016, Medical physics.

[9]  A comparison of relative proton stopping power measurements across patient size using dual- and single-energy CT , 2017, Acta oncologica.

[10]  Steffen Greilich,et al.  Evaluation of Stopping-Power Prediction by Dual- and Single-Energy Computed Tomography in an Anthropomorphic Ground-Truth Phantom. , 2018, International journal of radiation oncology, biology, physics.

[11]  Rongxiao Zhang,et al.  Experimental validation of two dual‐energy CT methods for proton therapy using heterogeneous tissue samples , 2018, Medical physics.

[12]  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.

[13]  Wolfgang Enghardt,et al.  Clinical Implementation of Dual-energy CT for Proton Treatment Planning on Pseudo-monoenergetic CT scans. , 2017, International journal of radiation oncology, biology, physics.

[14]  A. Deisher,et al.  Validation of proton stopping power ratio estimation based on dual energy CT using fresh tissue samples , 2017, Physics in medicine and biology.

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

[16]  T. Solberg,et al.  Ex vivo validation of a stoichiometric dual energy CT proton stopping power ratio calibration , 2018, Physics in medicine and biology.

[17]  Simon Rit,et al.  Comparison of projection- and image-based methods for proton stopping power estimation using dual energy CT , 2017 .

[18]  Bernhard Krauss,et al.  The Importance of Spectral Separation: An Assessment of Dual-Energy Spectral Separation for Quantitative Ability and Dose Efficiency , 2015, Investigative radiology.

[19]  Shota Sagara,et al.  Simplified derivation of stopping power ratio in the human body from dual‐energy CT data , 2017, Medical physics.

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

[21]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[22]  Steffen Greilich,et al.  Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues , 2017, Physics in medicine and biology.

[23]  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.

[24]  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.