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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

Background: Extended field‐of‐view (eFOV) methods have been proposed to generate larger demonstration FOVs for computed tomography (CT) simulators with a limited scanning FOV (sFOV) size in order to ensure accurate dose calculation and patient collision avoidance. Although the efficacy of these strategies has been evaluated for photon applications, the effect of stopping power ratio (SPR) estimation on proton therapy has not been studied. This study investigated the effect of an eFOV approach on the accuracy of SPR to water estimation in homogeneous and heterogeneous phantoms. Materials and Methods: To simulate patient geometries, tissue‐equivalent material (TEM) and customized extension phantoms were used. The TEM phantom supported various rod arrangements through predefined holes. Images were reconstructed to three FOV sizes using a commercial eFOV technique. A single‐energy CT stoichiometric method was used to generate Hounsfield unit (HU) to SPR (HU‐to‐SPR) conversion curves for each FOV. To investigate the effect of rod location in the sFOV and eFOV regions, eight TEM rods were placed at off‐center distances in the homogeneous phantom and scanned individually. Similarly, 16 TEM rods were placed in the heterogeneous TEM phantom and scanned simultaneously. Results: The conversion curves derived from the sFOV and eFOV data were identical. The average SPR differences of soft‐tissue, bone, and lung materials for rods placed at various off‐center locations were 3.3%, 4.8%, and 39.6%, respectively. In the heterogeneous phantom, the difference was within 1.0% in the absence of extension. However, in the presence of extension, the difference increased to 2.8% for all rods, except for lung materials, whose difference was 4.8%. Conclusions: When an eFOV method is used, the SPR variation in phantoms considerably increases for all TEM rods, especially for lung TEM rods. This phenomenon may substantially increase the uncertainty of HU‐to‐SPR conversion. Therefore, image reconstruction with a standard FOV size is recommended. [ABSTRACT FROM AUTHOR]

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual‐energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual‐layer spectral CT (DLCT). DECT‐based direct SPR predictions of dental materials via spectral image data were compared to conventional single‐energy CT (SECT)‐based SPR predictions obtained via indirect CT‐number‐to‐SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT‐based SPR predictions using a projection‐based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT‐based predictions. DECT‐based SPR prediction may improve the management of certain non‐tissue dental implant and restoration materials and subsequently increase dose prediction accuracy. [ABSTRACT FROM AUTHOR]

The errors on the stopping power ratio (SPR) of mouthpiece samples from ERKODENT were evaluated. Erkoflex and Erkoloc‐pro from ERKODENT and samples that combined Erkoflex and Erkoloc‐pro were computed tomography (CT)‐scanned using head and neck (HN) protocol at the East Japan Heavy Ion Center (EJHIC), and the values were averaged to obtain the CT number. The integral depth dose of the Bragg curve with and without these samples was measured for 292.1, 180.9, and 118.8 MeV/u of the carbon‐ion pencil beam using an ionization chamber with concentric electrodes at the horizontal port of the EJHIC. The average value of the water equivalent length (WEL) of each sample was obtained from the difference between the range of the Bragg curve and the thickness of the sample. To calculate the difference between the theoretical and measured values, the theoretical CT number and SPR value of the sample were calculated using the stoichiometric calibration method. Compared with the Hounsfield unit (HU)‐SPR calibration curve used at the EJHIC, the SPR error on each measured and theoretical value was calculated. The WEL value of the mouthpiece sample had an error of approximately 3.5% in the HU‐SPR calibration curve. From this error, it was evaluated that for a mouthpiece with a thickness of 10 mm, a beam range error of approximately 0.4 mm can occur, and for a mouthpiece with a thickness of 30 mm, a beam range error of approximately 1 mm can occur. For a beam passing through the mouthpiece in HN treatment, it would be practical to consider a mouthpiece margin of 1 mm to avoid beam range errors if ions pass through the mouthpiece. [ABSTRACT FROM AUTHOR]

An important source of uncertainty in proton therapy treatment planning is the assignment of stopping‐power ratio (SPR) from CT data. A commercial product is now available that creates an SPR map directly from dual‐energy CT (DECT). This paper investigates the use of this new product in proton treatment planning and compares the results to the current method of assigning SPR based on a single‐energy CT (SECT). Two tissue surrogate phantoms were CT scanned using both techniques. The SPRs derived from single‐energy CT and by DirectSPR™ were compared to measured values. SECT‐based values agreed with measurements within 4% except for low density lung and high density bone, which differed by 13% and 8%, respectively. DirectSPR™ values were within 2% of measured values for all tissues studied. Both methods were also applied to scanned containers of three types of animal tissue, and the expected range of protons of two different energies was calculated in the treatment planning system and compared to the range measured using a multi‐layer ion chamber. The average difference between range measurements and calculations based on SPR maps from dual‐ and single‐energy CT, respectively, was 0.1 mm (0.07%) versus 2.2 mm (1.5%). Finally, a phantom was created using a layer of various tissue surrogate plugs on top of a 2D ion chamber array. Dose measurements on this array were compared to predictions using both single‐ and dual‐energy CTs and SPR maps. While standard gamma pass rates for predictions based on DECT‐derived SPR maps were slightly higher than those based on single‐energy CT, the differences were generally modest for this measurement setup. This study showed that SPR maps created by the commercial product from dual‐energy CT can successfully be used in RayStation to generate proton dose distributions and that these predictions agree well with measurements. [ABSTRACT FROM AUTHOR]

Purpose: To assess treatment planning system (TPS) accuracy in estimating the stopping‐power ratio (SPR) of immobilization devices commonly used in proton therapy and to evaluate the dosimetric effect of SPR estimation error for a set of clinical treatment plans. Methods: Computed tomography scans of selected clinical immobilization devices were acquired. Then, the water‐equivalent thickness (WET) and SPR values of these devices based on the scans were estimated in a commercial TPS. The reference SPR of each device was measured using a multilayer ion chamber (MLIC), and the differences between measured and TPS‐estimated SPRs were calculated. These findings were utilized to calculate corrected dose distributions of 15 clinical proton plans for three treatment sites: extremity, abdomen, and head‐and‐neck. The original and corrected dose distributions were compared using a set of target and organs‐at‐risk (OARs) dose–volume histogram (DVH) parameters. Results: On average, the TPS‐estimated SPR was 19.5% lower (range, −35.1% to 0.2%) than the MLIC‐measured SPR. Due to the relatively low density of most immobilization devices used, the WET error was typically <1 mm, but up to 2.2 mm in certain devices. Overriding the SPR of the immobilization devices to the measured values did not result in significant changes in the DVH metrics of targets and most OARs. However, some critical OARs showed noticeable changes of up to 6.7% in maximum dose. Conclusions: The TPS tends to underestimate the SPR of selected proton immobilization devices by an average of about 20%, but this does not induce major WET errors because of the low density of the devices. The dosimetric effect of this SPR error was negligible for most treatment sites, although the maximum dose of a few OARs exhibited noticeable variations. [ABSTRACT FROM AUTHOR]

The accurate determination of mean ionization potential (I m) has the potential to reduce range uncertainty based margins and therefore allow for more focal treatments in proton radiotherapy. Many methods have been proposed to reduce uncertainty in I m and stopping power ratios (SPR), each with varying degrees of accuracy and issues. In this work, we present a simple parameterized model to determine I m in human biological tissue, allowing for the computation of patient-specific I m at the voxel level using magnetic resonance imaging (MRI). The model requires the measurement of three parameters by MRI, with only two parameters, mass percent water content and mass percent hydrogen content in organic molecules, required for the special case of soft tissues. The accuracy of this I m determination method was evaluated in available 'standard' (ICRU Report #44, (ICRU 1989 Tissue Substitutes in Radiation Dosimetry and Measurement (Bethesda, MD: International Commission on Radiation Units and Measurements))) human tissues. The sensitivity of this I m determination method to in vivo perturbations was also tested by calculating the effect of 10% variations in the experimentally measurable parameters on I m and SPR. For the human tissues modeled in this work, a high level of accuracy with low susceptibility to perturbations in measurement error was achieved in the prediction of I m. Root-mean-square errors in I m were within 0.77% and 1.8% for both soft and bony tissues, and were 0.09% and 0.2% for the SPR of soft and bony tissues, respectively, assuming knowledge of electron density. Proof of principle MR measurements and model-based computations of I m and SPR were taken in phantom for a series of hydrogenous solutions and compared against expected I m and SPR calculations from known elemental composition. MR determined I m and SPR values in a known composition solution were determined to within 5% and 0.52%, respectively. We present a novel model to accurately calculate mean ionization potential from measurements acquirable by MRI and show its feasibility in a phantom.

In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems. [ABSTRACT FROM AUTHOR]

Purpose: To present a proton computed tomography (pCT) reconstruction approach that models the integral depth dose (IDD) of the clinical scanning proton beam into beamlets. Using a multilayer ionization chamber (MLIC) as the imager, the proposed pCT system and the reconstruction approach can minimize extra ambient neutron dose and simplify the beamline design by eliminating an additional collimator to confine the proton beam. Methods: Monte Carlo simulation was applied to digitally simulate the IDDs of the exiting proton beams detected by the MLIC. A forward model was developed to model each IDD into a weighted sum of percentage depth doses of the constituent beamlets separated laterally by 1 mm. The water equivalent path lengths (WEPLs) of the beamlets were determined by iteratively minimizing the squared L2‐norm between the forward projected and simulated IDDs. The final WEPL values were reconstructed to pCT images, that is, proton stopping power ratio (SPR) maps, through simultaneous algebraic reconstruction technique with total variation regularization. The reconstruction process was tested with a digital cylindrical water‐based phantom and an ICRP adult reference computational phantom. The mean of SPR within regions of interest (ROIs) and the WEPL along a 4 mm‐wide beam (WEPL4mm${\rm{WEP}}{{\rm{L}}_{4{\rm{mm}}}}$) were compared with the reference values. The spatial resolution was analyzed at the edge of a cortical insert of the cylindrical phantom. Results: The percentage deviations from reference SPR were within ±1% in all selected ROIs. The mean absolute error of the reconstructed SPR was 0.33%, 0.19%, and 0.27% for the cylindrical phantom, the adult phantom at the head and lung region, respectively. The corresponding percentage deviations from reference WEPL4mm${\rm{WEP}}{{\rm{L}}_{4{\rm{mm}}}}$ were 0.48 ± 0.64%, 0.28 ± 0.48%, and 0.22 ± 0.49%. The full width at half maximum of the line spread function (LSF) derived from the radial edge spread function (ESF) of a cortical insert was 0.13 cm. The frequency at 10% of the modulation transfer function (MTF) was 6.38 cm–1. The mean signal‐to‐noise ratio (SNR) of all the inserts was 2.45. The mean imaging dose was 0.29 and 0.25 cGy at the head and lung region of the adult phantom, respectively. Conclusion: A new pCT reconstruction approach was developed by modeling the IDDs of the uncollimated scanning proton beams in the pencil beam geometry. SPR accuracy within ±1%, spatial resolution of better than 2 mm at 10% MTF, and imaging dose at the magnitude of mGy were achieved. Potential side effects caused by neutron dose were eliminated by removing the extra beam collimator. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and quantify the potential benefits associated with the use of stopping-power-ratio (SPR) images created from dual-energy computed tomography (DECT) images for proton dose calculation in a clinical proton treatment planning system (TPS). Materials and Methods: The DECT and single-energy computed tomography (SECT) scans obtained for 26 plastic tissue surrogate plugs were placed individually in a tissue-equivalent plastic phantom. Relative-electron density (ρe) and effective atomic number (Zeff) images were reconstructed from the DECT scans and used to create an SPR image set for each plug. Next, the SPR for each plug was measured in a clinical proton beam for comparison of the calculated values in the SPR images. The SPR images and SECTs were then imported into a clinical TPS, and treatment plans were developed consisting of a single field delivering a 10 × 10 × 10-cm3 spread-out Bragg peak to a clinical target volume that contained the plugs. To verify the accuracy of the TPS dose calculated from the SPR images and SECTs, treatment plans were delivered to the phantom containing each plug, and comparisons of point-dose measurements and 2-dimensional γ-analysis were performed. Results: For all 26 plugs considered in this study, SPR values for each plug from the SPR images were within 2% agreement with measurements. Additionally, treatment plans developed with the SPR images agreed with the measured point dose to within 2%, whereas a 3% agreement was observed for SECT-based plans. γ-Index pass rates were > 90% for all SECT plans and > 97% for all SPR image–based plans. Conclusion: Treatment plans created in a TPS with SPR images obtained from DECT scans are accurate to within guidelines set for validation of clinical treatment plans at our center. The calculated doses from the SPR image–based treatment plans showed better agreement to measured doses than identical plans created with standard SECT scans.

In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.

Background/Aim: This study evaluated the calculation accuracy of the stopping power ratio (SPR) using dual-energy computed tomography with fast kilovoltage switching (FKSCT) for particle therapy. Materials and Methods: A tissue characterization phantom with various reference materials was scanned to obtain single-energy computed tomography (SECT) images and generate virtual monochromatic images at 77 keV (VMI77keV) and 140 keV (VMI140keV), water density (WD) images, and effective Z (Zeff) images. For SECT, VMI77keV and VMI140keV lookup tables were generated to convert the measured Hounsfield value into the theoretical SPR for a normal phantom size. Subsequently, the reference materials were scanned in small and large phantoms. The SPR was calculated using the lookup tables of SECT (SPRSECT) images, VMI77keV (SPR77keV), and VMI140keV (SPR140keV), and it was derived from the WD and Zeff (SPRWD). Results: In the normal-sized phantom, the overall mean difference between SPRWD and theoretical SPR was −0.3%, and remained below 2% for most reference materials. For the large phantom, the overall mean absolute difference for SPR140keV (3.0%, p=0.006) and SPRWD (3.2%, p=0.002) for the reference materials was significantly lower than that for SPRSECT (5.9%). For the small phantom, a significant reduction in the mean difference in the SPR calculation was observed in SPR77keV (1.0%, p=0.001) and SPR140keV (1.1%, p=0.013) compared with SPRSECT (2.2%). Conclusion: VMI140keV generated using FKSCT significantly improves the estimation accuracy of SPR compared with SECT. Thus, FKSCT may be used to improve the dose calculation accuracy for treatment planning of particle therapy. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a novel method for the calculation of three‐dimensional (3D) Bragg–Gray water‐to‐detector stopping power ratio (sw,det) distributions for proton and carbon ion beams. Methods: Contrary to previously published fluence‐based calculations of the stopping power ratio, the sw,det calculation method used in this work is based on the specific way GATE/Geant4 scores the energy deposition. It only requires the use of the so‐called DoseActor, as available in GATE, for the calculation of the sw,det at any point of a 3D dose distribution. The simulations are performed using GATE‐RTion v1.0, a dedicated GATE release that was validated for the clinical use in light ion beam therapy. Results: The Bragg–Gray water‐to‐air stopping power ratio (sw,air) was calculated for monoenergetic proton and carbon ion beams with the default stopping power data in GATE‐RTion v1.0 and the new ICRU90 recommendation. The sw,air differences between the use of the default and the ICRU90 configuration were 0.6% and 5.4% at the physical range (R80 — 80% dose level in the distal dose fall‐off) for a 70 MeV proton beam and a 120 MeV/u carbon ion beam, respectively. For protons, the sw,det results for lithium fluoride, silicon, gadolinium oxysulfide, and the active layer material of EBT2 (radiochromic film) were compared with the literature and a reasonable agreement was found. For a real patient treatment plan, the 3D distributions of sw,det in proton beams were calculated. Conclusions: Our method was validated by comparison with available literature data. Its equivalence with Bragg–Gray cavity theory was demonstrated mathematically. The capability of GATE‐RTion v1.0 for the sw,det calculation at any point of a 3D dose distribution for simple and complex proton and carbon ion plans was presented. [ABSTRACT FROM AUTHOR]

To explore the optimal combination of monochromatic images derived from dual-energy CT (DECT) in calculating the stopping power ratio (SPR) of human tissues. Monochromatic CT numbers ranging from 40 keV to 140 keV (in intervals of 10 keV) for 34 standard human tissues were theoretically computed based on the NIST database. These values were then paired, resulting in 55 different dual-energy combinations for calculating the stopping power ratio (SPR). The combinations adhered to the rule of a lower energy (E low) paired with a higher X-ray energy (E high). Subsequently, each energy combination was utilized to predict the SPR of human tissues, and the accuracy of each pairing was assessed. Additionally, an analysis of the impact of uncertainty in the attenuation coefficient was conducted. When E low is ≤ 70 keV, an optimal energy combination emerges. These optimal energy combinations within the 40 keV, 50 keV, 60 keV, and 70 keV groups are 40–100 keV, 50–90 keV, 60–80 keV, and 70–80 keV, respectively, with corresponding mean absolute errors (MAE) of SPR at 0.12%, 0.11%, 0.12%, and 0.15%, respectively. The impact of attenuation coefficient uncertainty on these findings is minimal; even with a 1–2% uncertainty, the maximum change in MAE for SPR error is only 0.04%. Dual-energy CT utilizing energy pairs within the low energy range (40–70 keV) exhibits greater advantages in predicting the stopping power ratio of human tissues. In our investigation, the four energy combinations—40-100 keV, 50–90 keV, 60–80 keV, and 70-80 keV—demonstrated the highest accuracy in predicting SPR. • Reconstructed monochromatic CT numbers of materials using NIST database. • There is optimal energy combination when predicting the proton range by DECT. • 40–100 keV, 50–90 keV, 60–80 keV and 70–80 keV have the best SPR prediction accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To study the accuracy with which proton stopping power ratio (SPR) can be determined with dual‐energy computed tomography (DECT) for small structures and bone–tissue–air interfaces like those found in the head or in the neck. Methods: Hollow cylindrical polylactic acid (PLA) plugs (3 cm diameter, 5 cm height) were 3D printed containing either one or three septa with thicknesses tsepta = 0.8, 1.6, 3.2, and 6.4 mm running along the length of the plug. The cylinders were inserted individually into a tissue‐equivalent head phantom (16 cm diameter, 5 cm height). First, DECT scans were obtained using a Siemens SOMATOM Definition Edge CT scanner. Effective atomic number (Zeff) and electron density (ρe) images were reconstructed from the DECT to produce SPR‐CT images of each plug. Second, independent elemental composition analysis of the PLA plastic was used to determine the Zeff and ρe for calculating the theoretical SPR (SPR‐TH) using the Bethe–Bloch equation. Finally, for each plug, a direct measurement of SPR (SPR‐DM) was obtained in a clinical proton beam. The values of SPR‐CT, SPR‐TH, and SPR‐DM were compared. Results: The SPR‐CT for PLA agreed with SPR‐DM for tsepta ≥ 3 mm (for CT slice thicknesses of 0.5, 1.0, and 3.0 mm). The density of PLA was found to decrease with thickness when tsepta < 3 mm. As tsepta (and density) decreased, the SPR‐CT values also decreased, in good agreement with SPR‐DM and SPR‐TH. Conclusion: Overall, the DECT‐based SPR‐CT was within 3% of SPR‐TH and SPR‐DM in the high‐density gradient regions of the 3D‐printed plugs for septa greater than ~ 3mm in thickness. [ABSTRACT FROM AUTHOR]

Purpose: In proton therapy planning, the accuracy of the Stopping Power Ratios (SPR) calculated in the stoichiometric CT calibration is affected by, among others, uncertainties on the mean excitation energies (I‐values) of human tissues and water. Traditionally, the contribution of these uncertainties on the SPR has been conservatively estimated of the order of 1% or more for a reference tissue of known composition. This study provides a methodology that enables a finer estimation of this uncertainty, eventually showing that the traditional estimates of the uncertainty are too conservative. Methods: Since human tissues contain water, a correlation exists between the I‐values of tissues and water. As the SPR is the ratio of the tissue stopping power to that of water, this correlation decreases the uncertainty of the SPR. Our formalism considers this by expressing the I‐value of the tissue as a function of the water weight fraction and the I‐value of water, while applying Bragg's additivity rule only to the nonaqueous mixture of the tissue. For 22 reference tissue compositions, the SPR uncertainty was estimated by randomly sampling Gaussian distributions, based on ICRU data, for the I‐values of water and the nonaqueous mixture, as well as for the water weight fraction. Results: The relative standard deviation of the SPR, estimated at 150 MeV, is in the range of 0.1%–0.3% for soft tissues with an average water weight percentage of at least 60%. For tissues with a low water content (e.g., adipose and bones), this uncertainty is in the range of 0.5%–0.7%. Conclusion: Uncertainties on the I‐values of human tissues and water appear to have a significantly lower impact on the SPR uncertainty than traditionally expected. In the future, this may provide a rationale for using smaller distal and proximal margins on the target volume, provided that all other range uncertainty components are correctly estimated too. [ABSTRACT FROM AUTHOR]

Purpose: Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual‐energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi‐energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary‐based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation. Method: A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non‐negative combination of only a few tissues in the dictionary. We formulated this as a non‐convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies. Results: Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method. Conclusion: The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy. [ABSTRACT FROM AUTHOR]

Purpose The main objective of this study is to propose an alternative parameterization for the empirical relation between mean excitation energies ( I-value) and effective atomic numbers ( Zeff) of human tissues, and to present a simplified formulation (which we called DEEDZ- SPR) for deriving the stopping power ratio ( SPR) from dual-energy ( DE) CT data via electron density ( ρe) and Zeff calibration. Methods We performed a numerical analysis of this DEEDZ- SPR method for the human-body-equivalent tissues of ICRU Report 46, as objects of interest with unknown SPR and ρe. The attenuation coefficients of these materials were calculated using the XCOM photon cross-sections database. We also applied the DEEDZ- SPR conversion to experimental DECT data available in the literature, which was measured for the tissue-characterization phantom using a dual-source CT scanner at 80 kV and 140 kV/Sn. Results It was found that the DEEDZ- SPR conversion enables the calculation of SPR simply by means of the weighted subtraction of an electron-density image and a low- or high- kV CT image. The simulated SPRs were in excellent agreement with the reference values over the SPR range from 0.258 (lung) to 3.638 (bone mineral-hydroxyapatite). The relative deviations from the reference SPR were within ±0.6% for all ICRU-46 human tissues, except for the thyroid that presented a −1.1% deviation. The overall root-mean-square error was 0.21%. Application to experimental DECT data confirmed this agreement within the experimental accuracy, which demonstrates the practical feasibility of the method. Conclusions The DEEDZ- SPR conversion method could facilitate the construction of SPR images as accurately as a recent DECT-based calibration procedure of SPR parameterization based directly on the CT numbers in a DECT data set. [ABSTRACT FROM AUTHOR]

Purpose: The accuracy of proton dose calculation is dependent on the ability to correctly characterize patient tissues with medical imaging. The most common method is to correlate computed tomography (CT) numbers obtained via single-energy CT (SECT) with proton stopping power ratio (SPR). CT numbers, however, cannot discriminate between a change in mass density and change in chemical composition of patient tissues. This limitation can have consequences on SPR calibration accuracy. Dual-energy CT (DECT) is receiving increasing interest as an alternative imaging modality for proton therapy treatment planning due to its ability to discriminate between changes in patient density and chemical composition. In the current work we use a phantom of known composition to demonstrate the dosimetric advantages of proton therapy treatment planning with DECT over SECT. Methods: A phantom of known composition was scanned with a clinical SECT radiotherapy CT-simulator. The phantom was rescanned at a lower X-ray tube potential to generate a complimentary DECT image set. A set of reference materials similar in composition to the phantom was used to perform a stoichiometric calibration of SECT CT number to proton SPRs. The same set of reference materials was used to perform a DECT stoichiometric calibration based on effective atomic number. The known composition of the phantom was used to assess the accuracy of SPR calibration with SECT and DECT. Intensity modulated proton therapy (IMPT) treatment plans were generated with the SECT and DECT image sets to assess the dosimetric effect of the imaging modality. Isodose difference maps and root mean square (RMS) error calculations were used to assess dose calculation accuracy. Results: SPR calculation accuracy was found to be superior, on average, with DECT relative to SECT. Maximum errors of 12.8% and 2.2% were found for SECT and DECT, respectively. Qualitative examination of dose difference maps clearly showed the dosimetric advantages of DECT imaging, compared to SECT imaging for IMPT dose calculation for the case investigated. Quantitatively, the maximum dose calculation error in the SECT plan was 7.8%, compared to a value of 1.4% in the DECT plan. When considering the high dose target region, the root mean square (RMS) error in dose calculation was 2.1% and 0.4% for SECT and DECT, respectively. Conclusions: DECT-based proton treatment planning in a commercial treatment planning system was successfully demonstrated for the first time. DECT is an attractive imaging modality for proton therapy treatment planning owing to its ability to characterize density and chemical composition of patient tissues. SECT and DECT scans of a phantom of known composition have been used to demonstrate the dosimetric advantages obtainable in proton therapy treatment planning with DECT over the current approach based on SECT. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate whether the stopping power ratio (SPR) of a deformable, silicone-based 3D dosimeter could be determined more accurately using dual energy (DE) CT compared to using conventional methods based on single energy (SE) CT. The use of SECT combined with the stoichiometric calibration method was therefore compared to DECT-based determination. Methods: The SPR of the dosimeter was estimated based on its Hounsfield units (HUs) in both a SECT image and a DECT image set. The stoichiometric calibration method was used for converting the HU in the SECT image to a SPR value for the dosimeter while two published SPR calibration methods for dual energy were applied on the DECT images. Finally, the SPR of the dosimeter was measured in a 60 MeV proton by quantifying the range difference with and without the dosimeter in the beam path. Results: The SPR determined from SECT and the stoichiometric method was 1.10, compared to 1.01 with both DECT calibration methods. The measured SPR for the dosimeter material was 0.97. Conclusions: The SPR of the dosimeter was overestimated by 13% using the stoichiometric method and by 3% when using DECT. If the stoichiometric method should be applied for the dosimeter, the HU of the dosimeter must be manually changed in the treatment planning system in order to give a correct SPR estimate. Using a wrong SPR value will cause differences between the calculated and the delivered treatment plans. [ABSTRACT FROM AUTHOR]

Many papers discussed the I value for water given by the ICRU, concluding that a value of about 80±2 eV instead of 67.2 eV would reproduce measured ion depth-dose curves. A change in the I value for water would have an effect on the stopping power and, hence, on the water-to-air stopping power ratio, which is important in clinical dosimetry of proton and ion beams. For energies ranging from 50 to 330 MeV/u and for one spread out Bragg peak, the authors compare the impact of the I value on the water-to-air stopping power ratio. The authors calculate ratios from different ICRU stopping power tables and ICRU reports. The stopping power ratio is calculated via track-length dose calculation with SHIELD-HIT07. In the calculations, the stopping power ratio is reduced to a value of 1.119 in the plateau region as compared to the cited value of 1.13 in IAEA TRS-398. At low energies the stopping power ratio increases by up to 6% in the last few tenths of a mm toward the Bragg peak. For a spread out Bragg peak of 13.5 mm width at 130 mm depth, the stopping power ratio increases by about 1% toward the distal end. [ABSTRACT FROM AUTHOR]

Abstract: We constructed a cluster ion acceleration system of the 5.8m (the beam line is 5.5m) overall length. This system was composed of an electron impact type ion source, focusing systems, an acceleration column, analysis systems, a scattering chamber and vacuum pumping systems. In the electron impact type ion source mounted on a high voltage terminal, , and fullerene ions were generated. These fullerene ions were selected by the first analyzer magnet and the targeted ions were accelerated up to 100kV through the acceleration column. The mass spectrometry and the energy spectrometry of the accelerated fullerene ions were performed using the second analyzer magnet. We observed various sizes of smaller carbon cluster ions generated by fragmentation of the C60 parent ions. The fragment ions (n =2,3,5,10,13,17,21) were separated using the second analyzer magnet and 1.5–40keV/atom (1.6×105–8.0×105 m/s) slow carbon cluster ion beams were formed. The energy losses of the carbon cluster ions propagated through a 1.0μg/cm2 thin carbon foil in a scattering chamber were measured using the third analyzer magnet. The nonlinear effects in the energy loss process of the cluster ions were clarified from the stopping power ratios. The stopping power ratios in all cluster sizes were smaller than unity. We first observed the strong nonlinear effects of the stopping power in the low energy region. [Copyright &y& Elsevier]