An anthropomorphic multimodality (CT/MRI) head phantom prototype for end-to-end tests in ion radiotherapy.

With the increasing complexity of external beam therapy "end-to-end" tests are intended to cover every step from therapy planning through to follow-up in order to fulfill the higher demands on quality assurance. As magnetic resonance imaging (MRI) has become an important part of the treatment process, established phantoms such as the Alderson head cannot fully be used for those tests and novel phantoms have to be developed. Here, we present a feasibility study of a customizable multimodality head phantom. It is initially intended for ion radiotherapy but may also be used in photon therapy. As basis for the anthropomorphic head shape we have used a set of patient computed tomography (CT) images. The phantom recipient consisting of epoxy resin was produced by using a 3D printer. It includes a nasal air cavity, a cranial bone surrogate (based on dipotassium phosphate), a brain surrogate (based on agarose gel), and a surrogate for cerebrospinal fluid (based on distilled water). Furthermore, a volume filled with normoxic dosimetric gel mimicked a tumor. The entire workflow of a proton therapy could be successfully applied to the phantom. CT measurements revealed CT numbers agreeing with reference values for all surrogates in the range from 2 HU to 978 HU (120 kV). MRI showed the desired contrasts between the different phantom materials especially in T2-weighted images (except for the bone surrogate). T2-weighted readout of the polymerization gel dosimeter allowed approximate range verification.

Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy.

PURPOSE: The authors describe a novel method of predicting mass density and elemental mass fractions of tissues from dual energy CT (DECT) data for Monte Carlo (MC) based dose planning. METHODS: The relative electron density ϱ(e) and effective atomic number Z(eff) are calculated for 71 tabulated tissue compositions. For MC simulations, the mass density is derived via one linear fit in the ϱ(e) that covers the entire range of tissue compositions (except lung tissue). Elemental mass fractions are predicted from the ϱ(e) and the Z(eff) in combination. Since particle therapy dose planning and verification is especially sensitive to accurate material assignment, differences to the ground truth are further analyzed for mass density, I-value predictions, and stopping power ratios (SPR) for ions. Dose studies with monoenergetic proton and carbon ions in 12 tissues which showed the largest differences of single energy CT (SECT) to DECT are presented with respect to range uncertainties. The standard approach (SECT) and the new DECT approach are compared to reference Bragg peak positions. RESULTS: Mean deviations to ground truth in mass density predictions could be reduced for soft tissue from (0.5±0.6)% (SECT) to (0.2±0.2)% with the DECT method. Maximum SPR deviations could be reduced significantly for soft tissue from 3.1% (SECT) to 0.7% (DECT) and for bone tissue from 0.8% to 0.1%. Mean I-value deviations could be reduced for soft tissue from (1.1±1.4%, SECT) to (0.4±0.3%) with the presented method. Predictions of elemental composition were improved for every element. Mean and maximum deviations from ground truth of all elemental mass fractions could be reduced by at least a half with DECT compared to SECT (except soft tissue hydrogen and nitrogen where the reduction was slightly smaller). The carbon and oxygen mass fraction predictions profit especially from the DECT information. Dose studies showed that most of the 12 selected tissues would profit significantly (up to 2.2%) from DECT material decomposition with no noise present. The ϱ(e) associated with an absolute noise of ±0.01 and Z(eff) associated with an absolute noise of ±0.2 resulted in ±10% standard variation in the carbon and oxygen mass fraction prediction. CONCLUSIONS: Accurate stopping power prediction is mainly determined by the correct mass density prediction. Theoretical improvements in range predictions with DECT data in the order of 0.1%-2.1% were observed. Further work is needed to quantify the potential improvements from DECT compared to SECT in measured image data associated with artifacts and noise.

Experimental verification of ion stopping power prediction from dual energy CT data in tissue surrogates.

We present an experimental verification of stopping-power-ratio (SPR) prediction from dual energy CT (DECT) with potential use for dose planning in proton and ion therapy. The approach is based on DECT images converted to electron density relative to water ϱe/ϱe, w and effective atomic number Zeff. To establish a parameterization of the I-value by Zeff, 71 tabulated tissue compositions were used. For the experimental assessment of the method we scanned 20 materials (tissue surrogates, polymers, aluminum, titanium) at 80/140Sn kVp and 100/140Sn kVp (Sn: additional tin filtration) and computed the ϱe/ϱe, w and Zeff with a purely image based algorithm. Thereby, we found that ϱe/ϱe, w (Zeff) could be determined with an accuracy of 0.4% (1.7%) for the tissue surrogates with known elemental compositions. SPRs were predicted from DECT images for all 20 materials using the presented approach and were compared to measured water-equivalent path lengths (closely related to SPR). For the tissue surrogates the presented DECT approach was found to predict the experimental values within 0.6%, for aluminum and titanium within an accuracy of 1.7% and 9.4% (from 16-bit reconstructed DECT images).

MRI-based simulation of treatment plans for ion radiotherapy in the brain region.

PURPOSE: To test the potential of MRI-based treatment plan simulation for ion radiotherapy in the brain region. MATERIALS AND METHODS: A classification-based tissue segmentation method based on discriminant analysis was employed to derive so-called pseudo CT numbers from MR images of three patients with lesions in the head region undergoing ion radiotherapy. Treatment plans for ions, and for comparison purposes also for photons, were subsequently optimized and simulated using both MRI-based pseudo CT and a standard X-ray-based reference CT. RESULTS: Pseudo CTs revealed mean absolute errors in CT number in the range of 141-165 HU. While soft tissue was in good agreement with reference CT values, large deviations appeared at air cavities and bones as well as at interfaces of different tissue types. In simulations of ion treatment plans, pseudo CT optimizations showed small underdosages of target volumes with deviations in the PTV mean dose of 0.4-2.0% in comparison to reference CT optimizations. In contrast, the PTV mean dose in photon treatment plans differed by no more than 0.2%. CONCLUSIONS: The main challenge in deriving pseudo CT numbers from MRI was the correct assignment of air and compact bone. In this study, the impact of deviations on simulations of ion and photon treatment plans in the brain region was small, however for more complicated morphologies a further improvement of the classification method including MR imaging of compact bone is required.

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.

MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach.

BACKGROUND: In order to benefit from the highly conformal irradiation of tumors in ion radiotherapy, sophisticated treatment planning and simulation are required. The purpose of this study was to investigate the potential of MRI for ion radiotherapy treatment plan simulation and adaptation using a classification-based approach. METHODS: Firstly, a voxelwise tissue classification was applied to derive pseudo CT numbers from MR images using up to 8 contrasts. Appropriate MR sequences and parameters were evaluated in cross-validation studies of three phantoms. Secondly, ion radiotherapy treatment plans were optimized using both MRI-based pseudo CT and reference CT and recalculated on reference CT. Finally, a target shift was simulated and a treatment plan adapted to the shift was optimized on a pseudo CT and compared to reference CT optimizations without plan adaptation. RESULTS: The derivation of pseudo CT values led to mean absolute errors in the range of 81 - 95 HU. Most significant deviations appeared at borders between air and different tissue classes and originated from partial volume effects. Simulations of ion radiotherapy treatment plans using pseudo CT for optimization revealed only small underdosages in distal regions of a target volume with deviations of the mean dose of PTV between 1.4 - 3.1% compared to reference CT optimizations. A plan adapted to the target volume shift and optimized on the pseudo CT exhibited a comparable target dose coverage as a non-adapted plan optimized on a reference CT. CONCLUSIONS: We were able to show that a MRI-based derivation of pseudo CT values using a purely statistical classification approach is feasible although no physical relationship exists. Large errors appeared at compact bone classes and came from an imperfect distinction of bones and other tissue types in MRI. In simulations of treatment plans, it was demonstrated that these deviations are comparable to uncertainties of a target volume shift of 2 mm in two directions indicating that especially applications for adaptive ion radiotherapy are interesting.