Development of a CT number calibration audit phantom in photon radiation therapy: A pilot study

Application of stoichiometric CT number calibration method for dose calculation of tissue heterogeneous volumes in boron neutron capture therapy

BACKGROUND

Monte Carlo simulation code is commonly used for the dose calculation of boron neutron capture therapy. In the past, dose calculation was performed assuming a homogeneous mass density and elemental composition inside the tissue, regardless of the patient's age or sex. Studies have shown that the mass density varies with patient to patient, particularly for those that have undergone surgery or radiotherapy. A method to convert computed tomography numbers into mass density and elemental weights of tissues has been developed and applied in the dose calculation process using Monte Carlo codes. A recent study has shown the variation in the computed tomography number between different scanners for low- and high-density materials.

PURPOSE

The aim of this study is to investigate the effect of the elemental composition inside each calculation voxel on the dose calculation and the application of the stoichiometric CT number calibration method for boron neutron capture therapy planning.

METHODS

Monte Carlo simulation package Particle and Heavy Ion Transport code System was used for the dose calculation. Firstly, a homogeneous cubic phantom with the material set to ICRU soft tissue (four component), muscle, fat, and brain was modelled and the NeuCure BNCT system accelerator-based neutron source was used. The central axis depth dose distribution was simulated and compared between the four materials. Secondly, a treatment plan of the brain and the head and neck region was simulated using a dummy patient dataset. Three models were generated; (1) a model where only the fundamental materials were considered (simple model), a model where each voxel was assigned a mass density and elemental weight using (2) the Nakao20 model, and (3) the Schneider00 model. The irradiation conditions were kept the same between the different models (irradiation time and irradiation field size) and the near maximum (D1%) and mean dose to the organs at risk were calculated and compared.

RESULTS

A maximum percentage difference of approximately 5% was observed between the different materials for the homogeneous phantom. With the dummy patient plan, a large dose difference in the bone (greater than 12%) and region near the low-density material (mucosal membrane, 7%-11%) was found between the different models.

CONCLUSIONS

A stoichiometric CT number calibration method using the newly developed Nakao20 model was applied to BNCT dose calculation. The results indicate the importance of calibrating the CT number to elemental composition for each individual CT scanner for the purpose of BNCT dose calculation along with the consideration of heterogeneity of the material composition inside the defined region of interest.

CT number calibration audit in photon radiation therapy

BACKGROUND

Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs.

PURPOSE

The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe ).

METHODS

A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute-specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute-specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute-specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m , respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT-ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT-ρ calibration curves because the available tissue-equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM , respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT-ρ or CT-ρe calibration curve.

RESULTS

The mean ± standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were -1.1 ± 1.2 g/cm3 and -0.2 ± 1.1, -0.3 ± 0.9 g/cm3 and 0.8 ± 1.3, and -0.9 ± 1.3 g/cm3 and 1.0 ± 1.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm and Δρe,m for the CIRS phantom were 0.3 ± 0.8 g/cm3 and 0.9 ± 0.9, 0.6 ± 0.6 g/cm3 and 1.4 ± 0.8, and 0.2 ± 0.5 g/cm3 and 1.6 ± 0.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm for Tomotherapy TPSs was 2.1 ± 1.4 g/cm3 for soft tissues, which is larger than those for other TPSs. The mean ± SD of Δρe,TEM for the Gammex brain phantom (BRN-SR2) was -1.8 ± 0.4, implying that the tissue equivalency of the BRN-SR2 plug was slightly inferior to that of other plugs.

CONCLUSIONS

Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes.

A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation

Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.

Development of an Anthropomorphic Heterogeneous Female Pelvic Phantom and Its Comparison with a Homogeneous Phantom in Advance Radiation Therapy: Dosimetry Analysis

BACKGROUND

Accurate dosimetry is crucial in radiotherapy to ensure optimal radiation dose delivery to the tumor while sparing healthy tissues. Traditional dosimetry techniques using homogeneous phantoms may not accurately represent the complex anatomical variations in cervical cancer patients, highlighting the need to compare dosimetry results obtained from different phantom models.

PURPOSE

The aim of this study is to design and evaluate an anthropomorphic heterogeneous female pelvic (AHFP) phantom for radiotherapy quality assurance in cervical cancer treatment.

MATERIALS AND METHOD

Thirty RapidArc plans designed for cervical cancer patients were exported to both the RW3 homogeneous phantom and the anthropomorphic heterogeneous pelvic phantom. Dose calculations were performed using the anisotropic analytic algorithm (AAA), and the plans were delivered using a linear accelerator (LA). Dose measurements were obtained using a 0.6 cc ion chamber. The percentage (%) variation between planned and measured doses was calculated and analyzed. Additionally, relative dosimetry was performed for various target locations using RapidArc and IMRT treatment techniques. The AHFP phantom demonstrated excellent agreement between measured and expected dose distributions, making it a reliable quality assurance tool in radiotherapy.

RESULTS

The results reveal that the percentage variation between planned and measured doses for all RapidArc quality assurance (QA) plans using the AHFP phantom is 10.67% (maximum value), 2.31% (minimum value), and 6.89% (average value), with a standard deviation (SD) of 2.565 (t = 3.21604, p = 0.001063). Also, for the percentage of variation between homogeneous and AHFP phantoms, the t-value is -11.17016 and the p-value is <0.00001. The result is thus significant at p < 0.05. We can see that the outcomes differ significantly due to the influence of heterogeneous media. Also, the average gamma values in RapidArc plans are 0.29, 0.32, and 0.35 (g ≤ 1) and IMRT plans are 0.45, 0.44, and 0.42 (g ≤ 1) for targets 1, 2, and 3, respectively.

CONCLUSION

The AHFP phantom results show more dose variability than homogenous phantom outcomes. Also, the AHFP phantom was found to be suitable for QA evaluation.

The automated measurement of CT number linearity using an ACR accreditation phantom

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R², were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R²> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR²= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR²< 0.99.

Classification of X-Ray Attenuation Properties of Additive Manufacturing and 3D Printing Materials Using Computed Tomography From 70 to 140 kVp

Additive manufacturing and 3D printing is particularly useful in the production of phantoms for medical imaging applications including determination and optimization of (diagnostic) image quality and dosimetry. Additive manufacturing allows the leap from simple slab and stylized to (pseudo)-anthropomorphic phantoms. This necessitates the use of materials with x-ray attenuation as close as possible to that of the tissues or organs mimicked. X-ray attenuation properties including their energy dependence were determined for 35 printing materials comprising photocured resins and thermoplastic polymers. Prior to measuring x-ray attenuation in CT from 70 to 140 kVp, printing parameters were thoroughly optimized to ensure maximum density avoiding too low attenuation due to microscopic or macroscopic voids. These optimized parameters are made available. CT scanning was performed in a water filled phantom to guarantee defined scan conditions and accurate HU value determination. The spectrum of HU values covered by polymers printed using fused deposition modeling reached from −258 to +1,063 at 120 kVp (−197 to +1,804 at 70 kVp, to −266 to +985 at 140 kVp, respectively). Photocured resins covered 43 to 175 HU at 120 kVp (16–156 at 70, and 57–178 at 140 kVp). At 120 kVp, ASA mimics water almost perfectly (+2 HU). HIPS (−40 HU) is found close to adipose tissue. In all photocurable resins, and 17 printing filaments HU values decreased with increasing beam hardness contrary to soft tissues except adipose tissue making it difficult to mimic water or average soft tissue in phantoms correctly over a range of energies with one single printing material. Filled filaments provided both, the HU range, and an appropriate energy dependence mimicking bone tissues. A filled material with almost constant HU values was identified potentially allowing mimicking soft tissues by reducing density using controlled under-filling. The measurements performed in this study can be used to design phantoms with a wide range of x-ray contrasts, and energy dependence of these contrasts by combining appropriate materials. Data provided on the energy dependence can also be used to correct contrast or contrast to noise ratios from phantom measurements to real tissue contrasts or CNRs.