Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.

Objective.Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton rangein-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.Approach.A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.Results.The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous.Significance.Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute toin-vivodose verification measurements.

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic and ex-vivo phantoms.

OBJECTIVE: Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral X-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype. Approach. Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilized a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images. Main results. All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP, (0.28 ± 0.07)%, was obtained with the pCT system, compared to MAPE values of (0.51 ± 0.08)% and (0.80 ± 0.08)% for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT. Significance. The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novel ex-vivo phantom for inter-centre surveys in future.

Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms.

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.

Validation of prompt gamma-ray spectroscopy for proton range verification in tissue-mimicking and porcine samples.

Objective. Proton therapy of cancer improves dose conformality to the target and sparing of surrounding healthy tissues compared to conventional photon treatments. However, proton therapy's advantage could be even larger if proton range uncertainties were reduced. Sources of range uncertainties include computed tomography treatment planning images and variations in patient anatomy and setup. To reduce range uncertainties, we have developed a system for real-timein vivorange monitoring. The system is based on spectroscopy of prompt gamma-rays emitted through proton-nuclear interactions during irradiation. We validated the performance of our prompt gamma-ray spectroscopy detector prototype using tissue-mimicking and porcine samples.Approach. Measurements were performed in water, four tissue-mimicking samples (spongiosa, muscle, adipose tissue, and cortical bone), and two porcine samples (liver and brain). A dose of 0.9 Gy was delivered to a target at a depth of 12.5-17.5 cm. Multi-layer ionization chamber measurements were performed to determine stopping power ratios relative to water and ground truth proton ranges. Ground truth elemental compositions were determined using combustion analysis. Proton ranges and elemental compositions measured using prompt gamma-ray spectroscopy were compared to the ground truth.Main results. For all samples, the mean measured range over all pencil-beam spots differed from the ground truth by less than 1.2 mm. The mean standard deviation was 0.9 mm (range: 0.4-1.6 mm). The mean difference between ground truth and measured elemental compositions was 0.06gcm3(range: 0.00gcm3to 0.12gcm3).Significance. We verified the performance of our prompt gamma-ray spectroscopy detector prototype for proton range verification using tissue-mimicking and porcine samples. Measured proton ranges and elemental sample compositions were in good agreement with the ground truth. These measurements confirm the system's reliability for a variety of tissues and bridge the gap between previously-reported experiments and ongoingin vivopatient measurements.

Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner.

As one of the latest developments in X-ray computed tomography (CT), photon-counting technology allows spectral detection, demonstrating considerable advantages as compared to conventional CT. In this study, we investigated the use of a first-generation clinical photon-counting computed tomography (PCCT) scanner and estimated proton relative (to water) stopping power (RSP) of tissue-equivalent materials from virtual monoenergetic reconstructions provided by the scanner. A set of calibration and evaluation tissue-equivalent inserts were scanned at 120 kVp. Maps of relative electron density (RED) and effective atomic number (EAN) were estimated from the reconstructed virtual monoenergetic images (VMI) using an approach previously applied to a spectral CT scanner with dual-layer detector technology, which allows direct calculation of RSP using the Bethe-Bloch formula. The accuracy of RED, EAN, and RSP was evaluated by root-mean-square errors (RMSE) averaged over the phantom inserts. The reference RSP values were obtained experimentally using a water column in an ion beam. For RED and EAN, the reference values were calculated based on the mass density and the chemical composition of the inserts. Different combinations of low- and high-energy VMIs were investigated in this study, ranging from 40 to 190 keV. The overall lowest error was achieved using VMIs at 60 and 180 keV, with an RSP accuracy of 1.27% and 0.71% for the calibration and the evaluation phantom, respectively.

Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research.

Objective.Image guidance and precise irradiation are fundamental to ensure the reliability of small animal oncology studies. Accurate positioning of the animal and the in-beam monitoring of the delivered radio-therapeutic treatment necessitate several imaging modalities. In the particular context of proton therapy with a pulsed beam, information on the delivered dose can be retrieved by monitoring the thermoacoustic waves resulting from the brief and local energy deposition induced by a proton beam (ionoacoustics). The objective of this work was to fabricate a multimodal phantom (x-ray, proton, ultrasound, and ionoacoustics) allowing for sufficient imaging contrast for all the modalities.Approach.The phantom anatomical parts were extracted from mouse computed tomography scans and printed using polylactic acid (organs) and a granite/polylactic acid composite (skeleton). The anatomical pieces were encapsulated in silicone rubber to ensure long term stability. The phantom was imaged using x-ray cone-beam computed tomography, proton radiography, ultrasound imaging, and monitoring of a 20 MeV pulsed proton beam using ionoacoustics.Main results.The anatomical parts could be visualized in all the imaging modalities validating the phantom capability to be used for multimodal imaging. Ultrasound images were simulated from the x-ray cone-beam computed tomography and co-registered with ultrasound images obtained before the phantom irradiation and low-resolution ultrasound images of the mouse phantom in the irradiation position, co-registered with ionoacoustic measurements. The latter confirmed the irradiation of a tumor surrogate for which the reconstructed range was found to be in reasonable agreement with the expectation.Significance.This study reports on a realistic small animal phantom which can be used to investigate ionoacoustic range (or dose) verification together with ultrasound, x-ray, and proton imaging. The co-registration between ionoacoustic reconstructions of the impinging proton beam and x-ray imaging is assessed for the first time in a pre-clinical scenario.

Anthropomorphic lung phantom based validation of in-room proton therapy 4D-CBCT image correction for dose calculation.

PURPOSE: Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy. METHODS: Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60Gy was generated on the average intensity image of the 4D-CT for an approximated internal target volume (ITV). Dose distributions were then recalculated on individual phases of the 4D-CT and the 4D-vCT based on the optimized plan. Dose accumulation was performed for 4D-vCT and 4D-CT using DIR of each phase to the mid position, which was chosen as reference. Dose based on the 4D-vCT was then evaluated against the dose calculated on 4D-CT both, phase-by-phase as well as accumulated, by comparing dose volume histogram (DVH) values (Dmean, D2%, D98%, D95%) for the ITV, and by a 3D-gamma index analysis (global, 3%/3mm, 5Gy, 20Gy and 30Gy dose thresholds). RESULTS: Good agreement was found between the 4D-CT and 4D-vCT-based ITV-DVH curves. The relative differences ((CT-vCT)/CT) between accumulated values of ITV Dmean, D2%, D95% and D98% for the 4D-CT and 4D-vCT-based dose distributions were -0.2%, 0.0%, -0.1% and -0.1%, respectively. Phase specific values varied between -0.5% and 0.2%, -0.2% and 0.5%, -3.5% and 1.5%, and -5.7% and 2.3%. The relative difference of accumulated Dmean over the lungs was 2.3% and Dmean for the phases varied between -5.4% and 5.8%. The gamma pass-rates with 5Gy, 20Gy and 30Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%. CONCLUSIONS: Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation.

Feasibility of 4DCBCT-based proton dose calculation: An ex vivo porcine lung phantom study.

Inter-fractional variations of breathing pattern and patient anatomy introduce dose uncertainties in proton therapy. One approach to monitor these variations is to utilize the cone-beam computed tomography (CT, CBCT) scans routinely taken for patient positioning, reconstruct them as 4DCBCTs, and generate 'virtual CTs' (vCTs), combining the accurate CT numbers of the diagnostic 4DCT and the geometry of the daily 4DCBCT by using deformable image registration (DIR). In this study different algorithms for 4DCBCT reconstruction and DIR were evaluated. For this purpose, CBCT scans of a moving ex vivo porcine lung phantom with 663 and 2350 projections respectively were acquired, accompanied by an additional 4DCT as reference. The CBCT projections were sorted in 10 phase bins with the Amsterdam-shroud method and reconstructed phase-by-phase using first a FDK reconstruction from the Reconstruction Toolkit (RTK) and again an iterative reconstruction algorithm implemented in the Gadgetron Toolkit. The resulting 4DCBCTs were corrected by DIR of the corresponding 4DCT phases, using both a morphons algorithm from REGGUI and a b-spline deformation from Plastimatch. The resulting 4DvCTs were compared to the 4DCT by visual inspection and by calculating water equivalent thickness (WET) maps from the phantom's surface to the distal edge of a target from various angles. The optimized procedure was successfully repeated with mismatched input phases and on a clinical patient dataset. Proton treatment plans were simulated on the 4DvCTs and the dose distributions compared to the reference based on the 4DCT via gamma pass rate analysis. A combination of iterative reconstruction and morphons DIR yielded the most accurate 4DvCTs, with median WET differences under 2mm and 3%/3mm gamma pass rates per phase between 89% and 99%. These results suggest that image correction of iteratively reconstructed 4DCBCTs with a morphons DIR of the planning CT may yield sufficiently accurate 4DvCTs for daily time resolved proton dose calculations.

Experimental comparison of proton CT and dual energy x-ray CT for relative stopping power estimation in proton therapy.

Proton computed tomography (pCT) has been proposed as an alternative to x-ray computed tomography (CT) for acquiring relative to water stopping power (RSP) maps used for proton treatment planning dose calculations. In parallel, it has been shown that dual energy x-ray CT (DECT) improves RSP accuracy when compared to conventional single energy x-ray CT. This study aimed at directly comparing the RSP accuracy of both modalities using phantoms scanned at an advanced prototype pCT scanner and a state-of-the-art DECT scanner. Two phantoms containing 13 tissue-mimicking inserts of known RSP were scanned at the pCT phase II prototype and a latest generation dual-source DECT scanner (Siemens SOMATOM Definition FORCE). RSP accuracy was compared by mean absolute percent error (MAPE) over all inserts. A highly realistic Monte Carlo (MC) simulation was used to gain insight on pCT image artifacts which degraded MAPE. MAPE was 0.55% for pCT and 0.67% for DECT. The realistic MC simulation agreed well with pCT measurements ([Formula: see text]). Both simulation and experimental results showed ring artifacts in pCT images which degraded the MAPE compared to an ideal pCT simulation ([Formula: see text]). Using the realistic simulation, we could identify sources of artifacts, which are attributed to the interfaces in the five-stage plastic scintillator energy detector and calibration curve interpolation regions. Secondary artifacts stemming from the proton tracker geometry were also identified. The pCT prototype scanner outperformed a state-of-the-art DECT scanner in terms of RSP accuracy (MAPE) for plastic tissue mimicking inserts. Since artifacts tended to concentrate in the inserts, their mitigation may lead to further improvements in the reported pCT accuracy.

Toward a new treatment planning approach accounting for in vivo proton range verification.

Protons with modern pencil-beam scanning delivery are widely used in state-of-the-art radiotherapy. To reduce the unwanted effect of proton range uncertainties, prompt gamma (PG) monitoring is investigated and considered one of the most promising methods for real-time, in vivo range verification. Despite good correlation between the penetration depth of the PG signal and proton range in most cases, mismatch can occur especially because of tissue heterogeneities. Moreover, detectability and reproducibility of the prompt gamma signal critically depends on counting statistics. Nowadays, conventional treatment planning systems do not account for the degree of correlation between dose and PG signal nor the expected PG signal counting statistics, which considerably influences the possibility of a reliable verification of the intended beam range. Hence, in this project, we investigate a new treatment planning approach, in which the spot-by-spot conformities between PG and dose profiles (PG-dose correlation) as well as PG signal detectability and precision are taken into account based on a TPS optimizer. To investigate the feasibility of this idea, a research computational platform, combining Monte Carlo (MC, Geant4) pre-calculated pencil beams with the analytical Matlab-based TPS engine CERR, is used for treatment planning. Geant4 is employed for realistic simulation of the dose delivery and PG generation of all spots in the heterogeneous patient anatomy given by CT images. First of all, a treatment plan is created using a charged particle extension of CERR. Secondly, the PG fall-off positions of all individual pencil beams are evaluated and compared to the 80% distal dose fall-off positions. Thirdly, the PG-dose correlations of all spots are quantified. A new plan, in which a few spots with the best PG-dose correlation are boosted to ensure PG detectability with good precision, is then made. Finally, the optimized plan is fully recalculated on the same patient CT using Geant4, and the result is evaluated considering both plan quality and beam range monitorability. The evaluation shows that the re-optimized treatment plan is comparable to the initial plan in terms of dose distribution, dose averaged LET distribution and robustness, while fulfilling the set statistical conditions for reliable PG monitoring of the few automatically or manually selected spots. The method could thus complement, and for the selected pencil beams even overcome limitations of, alternative suggested approach such as pencil beam aggregation to provide sufficient counting statistics for precise PG range retrieval with good correlation to the treatment dose.