To develop a general method for human tissue characterization with dual- and multi-energy CT and evaluate its performance in determining elemental compositions and quantities relevant to radiotherapy Monte Carlo dose calculation. Ideal materials to describe human tissue are obtained applying principal component analysis on elemental weight and density data available in literature. The theory is adapted to elemental composition for solving tissue information from CT data. A novel stoichiometric calibration method is integrated to the technique to make it suitable for a clinical environment. The performance of the method is compared with two techniques known in literature using theoretical CT data. In determining elemental weights with dual-energy CT, the method is shown to be systematically superior to the water-lipid-protein material decomposition and comparable to the parameterization technique. In determining proton stopping powers and energy absorption coefficients with dual-energy CT, the method generally shows better accuracy and unbiased results. The generality of the method is demonstrated simulating multi-energy CT data to show the potential to extract more information with multiple energies. The method proposed in this paper shows good performance to determine elemental compositions from dual-energy CT data and physical quantities relevant to radiotherapy dose calculation. The method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.

Purpose: The purpose of this work is to evaluate the performance of dual‐energy CT (DECT) for determining proton stopping power ratios (SPRs) in an experimental environment and to demonstrate its potential advantages over conventional single‐energy CT (SECT) in clinical conditions.Methods: Water equivalent range (WER) measurements of 12 tissue‐equivalent plastic materials and 12 fresh animal tissue samples are performed in a 195 MeV broad proton beam using the dose extinction method. SECT and DECT scans of the samples are performed with a dual‐source CT scanner (Siemens SOMATOM Definition Flash). The methods of Schneider et al. (1996), Bourque et al. (2014), and Lalonde et al. (2017) are used to predict proton SPR on SECT and DECT images. From predicted SPR values, the WER of the proton beam through the sample is predicted for SECT and DECT using Monte Carlo simulations and compared to the measured WER.Results: For homogeneous tissue‐equivalent plastic materials, results with DECT are consistent with experimental measurements and show a systematic reduction of SPR uncertainty compared to SECT, with root‐mean‐square errors of 1.59% versus 0.61% for SECT and DECT, respectively. Measurements with heterogeneous animal samples show a clear reduction of the bias on range predictions in the presence of bones, with −0.88% for SECT versus −0.58% and −0.14% for both DECT methods. An uncertainty budget allows isolating the effect of CT number conversion to SPR and predicts improvements by DECT over SECT consistently with theoretical predictions, with 0.34% and 0.31% for soft tissues and bones in the experimental setup compared to 0.34% and 1.14% with the theoretical method.Conclusions: The present work uses experimental measurements in a realistic clinical environment to show potential benefits of DECT for proton therapy treatment planning. Our results show clear improvements over SECT in tissue‐equivalent plastic materials and animal tissues. Further work towards using Monte Carlo simulations for treatment planning with DECT data and a more detailed investigation of the uncertainties on I‐value and limitations on the Bragg additivity rule could potentially further enhance the benefits of this imaging technology for proton therapy.

Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds.

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OA _W ), and daily online adaptation (OA _D ). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OA _D mitigated random delivery errors more effectively than OA _W on a fraction per fraction basis, both OA _W and OA _D achieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D _98% ranged between 97.15% and 99.73% of the prescription dose for OA _D , with a median of 98.07%. For OA _W , values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OA _W could be used as an alternative approach to OA _D for most patients in order to reduce the clinical workload.