CT Calibration Research Articles

Advanced mathematical modeling for preciseestimation of CT energy spectrum using a calibration phantom

PurposeThis research aims to develop an advanced mathematical model using a CT calibration phantom to accurately estimate the CT energy spectrum in clinical settings, enhancing imaging quality and patient dose management. MethodsData were collected from a CT scanner using a CT calibration phantom at various energy levels (80, 100, 120, and 135 kVp). The data was optimized to refine the energy spectrum model, followed by cross-validation with Monte Carlo simulations. ResultsThe developed model demonstrated high precision in estimating the CT energy spectrum at all tested energy levels, with R-squared values above 0.9738 and an R-squared value of 0.9829 at 100 kVp. The model also showed low Normalized Root Mean Square Deviation (NRMSD) ranging from 0.6698 % to 1.8745 %. The Mean Energy Difference (ΔE) between the estimated and simulated spectrum consistently remained under 0.01 keV. These results were comparable to recent studies, which reported higher NRMSD and ΔE. ConclusionsThis study presents a significantly improved model for estimating the CT energy spectrum, offering greater accuracy than existing models. Its strengths include high precision and the use of standard equipment and algorithmic values. While the current use of 13 plugs is adequate, incorporating plugs with varied densities could enhance accuracy. This model has potential for improving imaging quality and optimizing patient dosing in clinical applications. Future trends may include extending energy spectrum estimation to megavoltage domains and integrating technologies like EPID and MVCT for better dose distribution prediction in high-energy photon beam therapy.

Quantitative calcium-based assessment of osteoporosis in dual-layer spectral CT

ObjectivesTo evaluate a novel calcium-only imaging technique (VCa) with subtracted bone marrow in osteoporosis in dual-layer CT (DLCT) compared to conventional CT images (CI) and dual-energy X-ray absorptiometry (DXA). Material and methodsImages of a multi-energy CT phantom with calcium inserts, quantitative CT calibration phantom, and of 55 patients (mean age: 64.6 ± 11.5 years) were acquired on a DLCT to evaluate bone mineral density (BMD). CI, calcium-suppressed images, and VCa were calculated. For investigating the association of VCa and CI with DXA a subsample of 30 patients (<90 days between DXA and CT) was used. Multiple regression analysis was performed to identify further factors improving the prediction of DXA BMD. ResultsThe calcium concentrations of the CT phantom inserts were significantly associated with CT numbers from VCa (R2 = 0.94) and from CI (R2 = 0.89–0.92). VCa showed significantly higher CT numbers than CI in the phantom (p ≤ 0.001) and clinical setting (p < 0.001). CT numbers from VCa were significantly associated with CI (R2 = 0.95, p < 0.001) and with DXA (R2 = 0.31, p = 0.007), whereas no significant association between DXA and CI was found. Prediction of DXA BMD based on CT numbers derived from VCa yielded R2 = 0.76 in multiple regression analysis. ROC for the differentiation of normal from pathologic BMD in VCa yielded an AUC of 0.7, and a cut-off value of 126HU (sensitivity: 0.90; specificity: 0.47). ConclusionVCa images showed better agreement with DXA and known calcium concentrations than CI, and could be used to estimate BMD. A VCa cut-off of 126HU could be used to identify abnormal bone mineral density.

Proton CT on biological phantoms for x-ray CT calibration in proton treatment planning

Objective. To present and characterize a novel method for x-ray computed tomography (xCT) calibration in proton treatment planning, based on proton CT (pCT) measurements on biological phantoms. Approach. A pCT apparatus was used to perform direct measurements of 3D stopping power relative to water (SPR) maps on stabilized, biological phantoms. Two single-energy xCT calibration curves—i.e. tissue substitutes and stoichiometric—were compared to pCT data. Moreover, a new calibration method based on these data was proposed, and verified against intra- and inter-species variability, dependence on stabilization, beam-hardening conditions, and analysis procedures. Main results. Biological phantoms were verified to be stable in time, with a dependence on temperature conditions, especially in the fat region: (−2.5 土 0.5) HU °C−1. The pCT measurements were compared with standard xCT calibrations, revealing an average SPR discrepancy within ±1.60% for both fat and muscle regions. In the bone region the xCT calibrations overestimated the pCT-measured SPR of the phantom, with a maximum discrepancy of about +3%. As a result, a new cross-calibration curve was directly extracted from the pCT data. Overall, the SPR uncertainty margin associated with this curve was below 3%; fluctuations in the uncertainty values were observed across the HU range. Cross-calibration curves obtained with phantoms made of different animal species and anatomical parts were reproducible with SPR discrepancies within 3%. Moreover, the stabilization procedure did not affect the resulting curve within a 2.2% SPR deviation. Finally, the cross-calibration curve was affected by the beam-hardening conditions on xCTs, especially in the bone region, while dependencies below 2% resulted from the image registration procedure. Significance. Our results showed that pCT measurements on biological phantoms may provide an accurate method for the verification of current xCT calibrations and may represent a tool for the implementation of a new calibration method for proton treatment planning.

Linac- and CyberKnife-based MRI-only treatment planning of prostate SBRT using an optimized synthetic CT calibration curve.

CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT. Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms. For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94±20HU. Gamma values mean±std (min-max) were 98.9%±0.9% (97.1%-100%) and 97.7%±1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively. MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning.

First clinical experience following the consensus guide for calibrating a proton stopping power ratio curve in a new proton centre

Background and PurposeThis work introduces the first assessment of CT calibration following the ESTRO’s consensus guidelines and validating the HLUT through the irradiation of biological material. MethodsTwo electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider’s and ESTRO’s methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored. ResultsThe results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider’s and ESTRO’s methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%. ConclusionThis is the first report on calibrating an HLUT following the ESTRO’s guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO’s guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.

Commissioning and clinical implementation of an independent dose calculation system for scanned proton beams.

Experimental patient-specific QA (PSQA) is a time and resource-intensive process, with a poor sensitivity in detecting errors. Radiation therapy facilities aim to substitute it by means of independent dose calculation (IDC) in combination with a comprehensive beam delivery QA program. This paper reports on the commissioning of the IDC software tool myQAiON (IBA Dosimetry) for proton therapy and its clinical implementation at the MedAustron Ion Therapy Center. The IDC commissioning work included the validation of the beam model, the implementation and validation of clinical CT protocols, and the evaluation of patient treatment data. Dose difference maps, gamma index distributions, and pass rates (GPR) have been reviewed. The performance of the IDC tool has been assessed and clinical workflows, simulation settings, and GPR tolerances have been defined. Beam model validation showed agreement of ranges within±0.2mm, Bragg-Peak widths within±0.1mm, and spot sizes at various air gaps within±5% compared to physical measurements. Simulated dose in 2D reference fields deviated by-0.3%±0.5%, while 3D dose distributions differed by 1.8% on average to measurements. Validation of the CT calibration resulted in systematic differences of 2.0% between IDC and experimental data for tissue like samples. GPRs of 99.4±0.6% were found for head, head and neck, and pediatric CT protocols on a 2%/2mm gamma criterion. GPRs for the adult abdomen protocol were at 98.9% on average with 3%/3mm. Root causes of GPR outliers, for example, implants were identified and evaluated. IDC has been successfully commissioned and integrated into the MedAustron clinical workflow for protons in 2021. IDC has been stepwise and safely substituting experimental PSQA since February 2021. The initial reduction of proton experimental PSQA was about 25% and reached up to 90% after 1 year.

Proton Online Adaptation Using Novel Cone-Beam Computed Tomography System

Cone beam computed tomography (CBCT) has been used in clinic frequently to provide quick, online, and handy three-dimensional images. However, due to its significant artifacts and inaccurate Hounsfield values (HU), CBCT based online proton adaptation is infeasible. Recently, a novel in-room imaging solution was developed to allow larger images, better contrast and faster CBCT imaging acquisition. Our work demonstrated for the first time the feasibility of using this novel CBCT images for direct intensity modulated proton planning. Three patients and three CIRS phantoms were scanned using novel imaging technique (CBCTp) for this work. CT curves were acquired by scanning a CIRS electron density phantom. Stopping power ratio was computed using the stoichiometric method. Proton plans were made on treatment simulation CT and further evaluated on CBCTp to compare the differences. The table below shows three patients and three phantoms plan comparisons. Multiple sites and dose levels were studied. The planning target volume (PTV) coverage (D95%) and mean dose difference between simulation CT and CBCTp are 0.8% and 0.3%, correspondingly. Dosimetrically, phantom and patient plans are almost identical between two imaging techniques. Lung patient plan show the largest variation due to patient tumor change and the quality of breathing. The novel and high quality of CBCT is feasible for direct proton planning with accurate CT calibration. It provides a reliable alternative solution for proton online adaptive therapy.

Accuracy of Electron Density and Planning Dosimetry in a Novel High-Quality CBCT Imaging System

A high-quality Cone-Beam Computed Tomography (CBCT) imaging system has been FDA approved for imaging guidance and dose calculation in radiotherapy. This study aims to evaluate the accuracy of the relative electron density in CBCT images acquired in this CBCT imaging system in a phantom study and its dosimetric impact on treatment planning in a patient study. Astoichiometric CT calibration was performed with a CIRS phantom (SunNuclear, Model 062M) to generate the HU-electron density curve for two tube voltages, 125kVp and 140 kVp, respectively. The phantom has a longitudinal length of 26.5 cm and is equipped with interchangeable inserts of various compositions, supplied by the vendor. Measurements were taken with solid water plates added to both ends of the phantom to allow adequate scattering and repeated for various clinical protocols with different combinations of tube voltages and exposures. The accuracy of the relative electron density of the CBCT imaging system was verified by comparing the calculated electron density from the Hounsfield Units (HU) measurements obtained from a Gammex phantom to the relative electron densities provided in vendor's specifications. To benchmark the relative electron density of the CBCT imaging system against a standard helical CT simulator, ten clinical plans that were created on CT simulation images were copied and recalculated on the CBCT images acquired immediately after the CT simulation, the latter of which was a standard procedure in current radiotherapy care for all patients who had given their consent to participate in the IRB-approved imaging study. The dose grids used in these calculations were 2.5mm x 2.5mm x 3mm. The Gamma passing rate was calculated using a standard 3mm/3% criterion with a 10% threshold. Ourresults showed the difference between the averaged CBCT calibration curves acquired at tube voltages of 125 kVP and 140 kVp was less than 2%. The mean discrepancy of the relative electron densities from vendor's specification was 0.0045 with a range between -0.02 and 0.04. Relative electron densities in all inserts were within 2% from the vendor's specifications except the cortical bone insert. Gamma passing rate was between 96.02% and 98.49% with mean value of 97.4% and a standard deviation of 0.95%. We consider this reflects the fact that the CT simulation and CBCT imaging were performed in separated rooms, which resulted in slight anatomical deformation that could negatively impact the Gamma passing rate. The CBCT imaging system provides sufficient accuracy of electron density for dose calculation, and the dose distribution calculated on the CBCT images is clinically equivalent to those calculated on helical CT images. The enhanced imaging quality of CBCT could further extend the role of imaging guidance to planning for adaptive radiotherapy, potentially reducing the need for re-simulation and interruptions in the radiotherapy course.

A spot-specific range uncertainty framework for robust optimization of proton therapy treatments.

An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and "dry" material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot-specific range uncertainty (SSRU) determination. Second, we use the water-based formalism to reduce range uncertainties and, potentially, to spare better the organs atrisk. The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray-tracing method was used to propagate molecular I-values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head-and-neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically aspossible. For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level ofrobustness. Promising results of the SSRU framework were observed on three head-and-neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient-specifictreatments.

Keep Attention to the Mapping:Application of AI for Geometric X-Ray CT Scan Calibration

This paper presents a method for mapping markers from CT calibration bodies in X-ray projections. In twin robotic CT systems, determining projection geometry from projection views is essential for reconstruction. Many algorithms, such as direct linear transformation (DLT), use a calibration body for this purpose. This consists of individual markers that must be individually assigned in a single X-ray projection. In the described approach, a transformation model is used to map the coordinates of the 2D marker centers to the correct index. This allows the use of freely designed calibration bodies, where the arrangement of the markers is not limited to geometric arrangements.

Evaluation of dosimetric effects of metallic artifact reduction and tissue assignment on Monte Carlo dose calculations for 125 I prostate implants.

Monte Carlo (MC) simulation studies, aimed at evaluating the magnitude of tissue heterogeneity in 125 I prostate permanent seed implant brachytherapy (BT), customarily use clinical post-implant CT images to generate a virtual representation of a realistic patient model (virtual patient model). Metallic artifact reduction (MAR) techniques and tissue assignment schemes (TAS) are implemented on the post-implant CT images to mollify metallic artifacts due to BT seeds and to assign tissue types to the voxels corresponding to the bright seed spots and streaking artifacts, respectively. The objective of this study is to assess the combined influence of MAR and TAS on MC absorbed dose calculations in post-implant CT-based phantoms. The virtual patient models used for 125 I prostate implant MC absorbed dose calculations in this study are derived from the CT images of an external radiotherapy prostate patient without BT seeds and prostatic calcifications, thus averting the need to implement MAR and TAS. The geometry of the IsoSeed I25.S17plus source is validated by comparing the MC calculated results of the TG-43 parameters for the line source approximation with the TG-43U1S2 consensus data. Four MC absorbed dose calculations are performed in two virtual patient models using the egs_brachy MC code: (1) TG-43-based Dw,w-TG 43 , (2) Dw,w-MBDC that accounts for interseed scattering and attenuation (ISA), (3) Dm,m that examines ISA and tissue heterogeneity by scoring absorbed dose in tissue, and (4) Dw,m that unlike Dm,m scores absorbed dose in water. The MC absorbed doses (1) and (2) are simulated in a TG-43 patient phantom derived by assigning the densities of every voxel to 1.00gcm-3 (water), whereas MC absorbed doses (3) and (4) are scored in the TG-186 patient phantom generated by mapping the mass density of each voxel to tissue according to a CT calibration curve. The MC absorbed doses calculated in this study are compared with VariSeed v8.0 calculated absorbed doses. To evaluate the dosimetric effect of MAR and TAS, the MC absorbed doses of this work (independent of MAR and TAS) are compared to the MC absorbed doses of different 125 I source models from previous studies that were calculated with different MC codes using post-implant CT-based phantoms generated by implementing MAR and TAS on post-implant CT images. The very good agreement of TG-43 parameters of this study and the published consensus data within 3% validates the geometry of the IsoSeed I25.S17plus source. For the clinical studies, the TG-43-based calculations show a D90 overestimation of more than 4% compared to the more realistic MC methods due to ISA and tissue composition. The results of this work generally show few discrepancies with the post-implant CT-based dosimetry studies with respect to the D90 absorbed dose metric parameter. These discrepancies are mainly Type B uncertainties due to the different 125 I source models and MC codes. The implementation of MAR and TAS on post-implant CT images have no dosimetric effect on the 125 I prostate MC absorbed dose calculation in post-implant CT-based phantoms.

Precision dosimetry in yttrium-90 radioembolization through CT imaging of radiopaque microspheres in a rabbit liver model

PurposeTo perform precision dosimetry in yttrium-90 radioembolization through CT imaging of radiopaque microspheres in a rabbit liver model and to compare extracted dose metrics to those produced from conventional PET-based dosimetry.Materials and methodsA CT calibration phantom was designed containing posts with nominal microsphere concentrations of 0.5 mg/mL, 5.0 mg/mL, and 25.0 mg/mL. The mean Hounsfield unit was extracted from the post volumes to generate a calibration curve to relate Hounsfield units to microsphere concentration. A nominal bolus of 40 mg of microspheres was administered to the livers of eight rabbits, followed by PET/CT imaging. A CT-based activity distribution was calculated through the application of the calibration curve to the CT liver volume. Post-treatment dosimetry was performed through the convolution of yttrium-90 dose-voxel kernels and the PET- and CT-based cumulated activity distributions. The mean dose to the liver in PET- and CT-based dose distributions was compared through linear regression, ANOVA, and Bland–Altman analysis.ResultsA linear least-squares fit to the average Hounsfield unit and microsphere concentration data from the calibration phantom confirmed a strong correlation (r2 > 0.999) with a slope of 14.13 HU/mg/mL. A poor correlation was found between the mean dose derived from CT and PET (r2 = 0.374), while the ANOVA analysis revealed statistically significant differences (p < 10−12) between the MIRD-derived mean dose and the PET- and CT-derived mean dose. Bland–Altman analysis predicted an offset of 15.0 Gy between the mean dose in CT and PET. The dose within the liver was shown to be more heterogeneous in CT than in PET with an average coefficient of variation equal to 1.99 and 1.02, respectively.ConclusionThe benefits of a CT-based approach to post-treatment dosimetry in yttrium-90 radioembolization include improved visualization of the dose distribution, reduced partial volume effects, a better representation of dose heterogeneity, and the mitigation of respiratory motion effects. Post-treatment CT imaging of radiopaque microspheres in yttrium-90 radioembolization provides the means to perform precision dosimetry and extract accurate dose metrics used to refine the understanding of the dose–response relationship, which could ultimately improve future patient outcomes.

Machine learning based oxygen and carbon concentration derivation using dual-energy CT for PET-based dose verification in proton therapy.

Online dose verification based on proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g., C and O). We developed a machine learning framework for deriving oxygen and carbon concentration based on dual-energy CT (DECT). Digital phantoms at the head site were constructed based on single-energy CT (SECT) and stoichiometric calibration. DECT images (80 and 140kVp) were synthesized using two methods: (1) theoretical CT numbers with Gaussian noise (method 1) and (2) forward/backward image reconstruction with poly-energetic energy spectrum and Poisson noise modeled (method 2). Two architectures of convolutional neural networks, UNet and ResNet, were investigated to map from DECT images to C/O weights. Four cases (UNet-1: Method 1+UNet, ResNet-1: Method 1+ResNet, UNet-2: Method 2+UNet, and ResNet-2: Method 2 +ResNet) were tested for different tissue types and different noise levels. Monte-Carlo simulation was employed to identify the impact of fluctuation in oxygen and carbon concentration on activity/dose distribution. When no noise is present, all four cases are able to obtain <2% mean absolute errors and <4% root mean square error (RMSE). For the worst image quality (e.g., lowest image SNR), the RMSE for O among all tissue types is 3.02% (UNet-1), 4.46% (ResNet-1), 4.38% (UNet-2), and 6.31% (ResNet-2), respectively. For UNet-1 and ResNet-1, the model performed slightly better in terms of RMSE for skeletal tissue than soft tissues. Such a trend is not observed for UNet-2 and ResNet-2. With regard to the comparison between UNet and ResNet, different accuracy and noise immunity are observed. The activity profiles exhibit 3%-5% difference in terms of mean relative error between the ground truth and machine learning outcome. We explored the feasibility of a machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Two machine learning models, UNet and ResNet, are able to utilize spatial correlation and obtain accurate carbon and oxygen concentration. This study lays a foundation for us to apply the proposed approach to clinical DECT images.

Improving dose calculation accuracy in preclinical radiation experiments using multi-energy element resolved cone-beam CT

Objective. Cone-beam CT (CBCT) in modern pre-clinical small-animal radiation research platforms provides volumetric images for image guidance and experiment planning purposes. In this work, we implemented multi-energy element-resolved (MEER) CBCT using three scans with different kVps on a SmART platform (Precision x-ray Inc.) to determine images of relative electron density (rED) and elemental composition (EC) that are needed for Monte Carlo-based radiation dose calculation. Approach. We performed comprehensive calibration tasks to achieve sufficient accuracy for this quantitative imaging purpose. For geometry calibration, we scanned a ball bearing phantom and used an analytical method together with an optimization approach to derive gantry angle specific geometry parameters. Intensity calibration and correction included the corrections for detector lag, glare, and beam hardening. The corrected CBCT projection images acquired at 30, 40, and 60 kVp in multiple scans were used to reconstruct CBCT images using the Feldkamp–Davis–Kress reconstruction algorithm. After that, an optimization problem was solved to determine images of rED and EC. We demonstrated the effectiveness of our CBCT calibration steps by showing improvements in image quality and successful material decomposition in cases with a small animal CT calibration phantom and a plastinated mouse phantom. Main results. It was found that artifacts induced by geometry inaccuracy, detector lag, glare, and beam hardening were visually reduced. CT number mean errors were reduced from 19% to 5%. In the CT calibration phantom case, median errors in H, O, and Ca fractions for all the inserts were below 1%, 2%, and 4% respectively, and median error in rED was less than 5%. Compared to the standard approach deriving material type and rED via CT number conversion, our approach improved Monte Carlo simulation-based dose calculation accuracy in bone regions. Mean dose error was reduced from 47.5% to 10.9%. Significance. The MEER-CBCT implemented on an existing CBCT system of a small animal irradiation platform achieved accurate material decomposition and significantly improved Monte Carlo dose calculation accuracy.

Clinical Workflow for Planning and Treating Palliative Patients on Diagnostic Images

Care coordination for inpatients requiring palliative radiotherapy remains a challenge. One such challenge is timely CT simulation. Previously published data demonstrate that rapid radiotherapy delivery is associated with improved patient satisfaction and reduced duration of hospitalization. The purpose of this study was to assess the feasibility of eliminating RT simulation from the current clinical workflow for palliative patients by using diagnostic images as the primary image dataset.A cohort of 11 previously treated palliative patients were identified. Treatment sites evaluated included spine (4), sacrum (2), shoulder (1), lung (1), humerus (1), pelvis (1) and whole brain (1). Diagnostic CT images on a curved couch without RT immobilization were used to generate diagnostic plans. The diagnostic plans were copied and recalculated on corresponding simulation CT images, which were the clinically used datasets for planning and treatment. For the whole brain patient who only had a diagnostic MRI, a synthetic CT scan was generated with brain, muscle, adipose and bone overridden following ICRU 44. Anatomic surrogates were aligned to couch indexing notches to avoid large shift during patient setup. The quality of diagnostic imaging based treatment plans were compared against traditional CT simulation based plans by a group of physicians.All diagnostic plans, except the shoulder, were clinically acceptable. Minor dose distribution deviation from the previously approved clinical plans were noted, but were not clinically significant, as illustrated in the table. The average change in maximum point dose was 0.1 Gy, ranged between -0.8 Gy and 0.5 Gy. Change in PTV D95 and V95 ranged from -1.3% to 0.5%, and from 0% to 2.8%, respectively. The rejected shoulder case was secondary to a limited field of view (FOV) on the diagnostic images. However, a limited FOV may be corrected through estimation of missing tissue or more appropriate diagnostic image selection. The impact from CT calibration and anatomy change on different couches was negligible.A clinical workflow to generate treatment plans on diagnostic imaging was developed. This workflow eliminates the need for formal CT simulation, allowing for expedited radiotherapy. Diagnostic imaging based treatment plans were deemed to be clinically acceptable after careful quality and dosimetric assessment against traditional CT simulation based treatment plans. Limitations of the new workflow were identified and iterative improvements are currently underway.

Estimating Proton Stopping Power Ratio of a Novel Iodinated Rectal Hydrogel Spacer Using Dual Energy Computed Tomography

A novel iodinated rectal hydrogel spacer (I-RS) has been developed to make the gel radiopaque on CT compared to the standard rectal hydrogel spacers (S-RS). As the iodine in I-RS strongly absorbs kV photons, the accuracy of currently employed CT calibration built into a single energy CT (SECT) becomes inaccurate to determine the proton stopping power ratio (PSR) of the injected gel. Direct measurements of PSR mediated by I-RS with proton beams has been reported; however, those measurements were conducted under ideal conditions without fully accounting for density heterogeneity mediated by air bubbles co-injected with the gel during placement. This study used dual-energy CT (DECT) to estimate I-RS PSR in patients undergoing CT simulation for prostate cancer (PCa) radiotherapy (RT).Sequential scans at 90 and 140 kVp were acquired for 7 intact PCa (I-RS n = 5, S-RS n = 2) on a Philips Brilliance scanner. PSR was reconstructed following the method proposed by Torikoshi and improved by Yang, and compared to similar measurements when I-RS was replaced with S-RS. A pre-calculated table correlating effective atomic number with all possible Hounsfield Unit (HU) combinations from two sequential scans was implemented to accelerate reconstruction. PSRs were compared between DECT and SECT, and between I-RS and S-RS.In gel, HU measurements were heterogeneous (range -372 - +358). The mean PSR on reconstruction when using DECT was 0.966 (0.940-0.980) for I-RS. Replacement of I-RS with S-RS HU density resulted in a mean PSR of 0.937 (0.926-0.941) by DECT. PSR for I-RS on SECT was 1.107, and 1.014 for S-RS. The estimated difference between PSR in I-RS and S-RS on DECT vs. SECT were 14% and 8%, respectively.These data demonstrate that the use of I-RS in proton RT treatment planning could overestimate proton range due to underestimating PSR when CT simulation is performed using standard SECT. Notably, PSR absolute value reported here is different compared to previously reported in vitro data under ideal conditions and may be related to differences in gel heterogeneity in vivo. Heterogeneity is highlighted by the wide range of gel HU measurements. Direct measurements of PSR will be obtained in a planned future experiment. These data suggest that PSR overestimation due to I-RS should be considered when planning PCa treatment using proton therapy for patients who get I-RS to reduce treatment toxicity.

Effects of lung tissue characterization in radiotherapy of breast cancer under deep inspiration breath hold when using Monte Carlo dosimetry.

To investigate the sensitivity of Monte Carlo (MC) calculated lung dose distributions to lung tissue characterization in external beam radiotherapy of breast cancer under Deep Inspiration Breath Hold (DIBH). EGSnrc based MC software was employed. Mean lung densities for one hundred patients were analysed. CT number frequency and clinical dose distributions were calculated for 15 patients with mean lung density below 0.14g/cm3. Lung volume with a pre-defined CT numbers was also considered. Lung tissue was characterized by applying different CT calibrations in the low-density region and air-lung tissue thresholds. Dose impact was estimated by Dose Volume Histogram (DVH) parameters. Mean lung densities below 0.14g/cm3 were found in 10% of the patients. CT numbers below -960 HU dominated the CT frequency distributions with a high rate of CT numbers at -990 HU. Mass density conversion approach influenced the DVH shape. V4Gy and V8Gy varied by 7% and 5% for the selected patients and by 9% and 3.5% for the pre-defined lung volume. V16Gy and V20Gy, were within 2.5%. Regions above 20Gy were affected. Variations in air- lung tissue differentiation resulted in DVH parameters within 1%. Threshold at -990 HU was confirmed by the CT number frequency distributions. Lung dose distributions were more sensitive to variations in the CT calibration curve below lung (inhale) density than to air-lung tissue differentiation. Low dose regions were mostly affected. The dosimetry effects were found to be potentially important to 10% of the patients treated under DIBH.

Dose calculation validation of a convolution algorithm in a solid water phantom.

The dose calculated using a convolution algorithm should be validated in a simple homogeneous water-equivalent phantom before clinical use. The dose calculation accuracy within a solid water phantom was investigated. The specific Gamma knife design requires a dose rate calibration within a spherical solid water phantom. The TMR10 algorithm, which approximates the phantom material as liquid water, correctly computes the absolute dose in water. The convolution algorithm, which considers electron density miscalculates the dose in water as the phantom Hounsfield units were converted into higher electron density when the original CT calibration curve was used. To address this issue, the electron density of liquid water was affected by modifying the CT calibration curve. The absolute dose calculated using the convolution algorithm was compared with that computed by the TMR10. The measured depth dose profiles were also compared to those computed by the convolution and TMR10 algorithms. A patient treatment was recalculated in the solid-water phantom and the delivery quality assurance was checked. The convolution algorithm and the TMR10 calculate an absolute dose within 1% when using the modified CT calibration curve. The dose depth profile calculated using the convolution algorithms was superimposed on the TMR10 and measured dose profiles when the modified CT calibration curve was applied. The Gamma index was better than 93%. Dose calculation algorithms, which consider electron density, require a CT calibration curve adapted to the phantom material to correctly compute the dose in water.

OC-0201 Breast specific cone-beam CT calibration for daily dose verification in proton therapy

OC-0201 Breast specific cone-beam CT calibration for daily dose verification in proton therapy

OC-0520 CT calibration for precise proton therapy planning in children

OC-0520 CT calibration for precise proton therapy planning in children