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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 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]

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 (VMI 77keV ) and 140 keV (VMI 140keV ), water density (WD) images, and effective Z (Z eff ) images. For SECT, VMI 77keV and VMI 140keV 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 (SPR SECT ) images, VMI 77keV (SPR 77keV ), and VMI 140keV (SPR 140keV ), and it was derived from the WD and Z eff (SPR WD )., Results: In the normal-sized phantom, the overall mean difference between SPR WD and theoretical SPR was -0.3%, and remained below 2% for most reference materials. For the large phantom, the overall mean absolute difference for SPR 140keV (3.0%, p=0.006) and SPR WD (3.2%, p=0.002) for the reference materials was significantly lower than that for SPR SECT (5.9%). For the small phantom, a significant reduction in the mean difference in the SPR calculation was observed in SPR 77keV (1.0%, p=0.001) and SPR 140keV (1.1%, p=0.013) compared with SPR SECT (2.2%)., Conclusion: VMI 140keV 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., (Copyright © 2022 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)