Realistic phantoms to characterize dosimetry in pediatric CT

Tailoring the Mass Density of 3D Printing Materials for Accurate X-ray Imaging Simulation by Controlled Underfilling for Radiographic Phantoms

Additive manufacturing and 3D printing allow for the design and rapid production of radiographic phantoms for X-ray imaging, including CT. These are used for numerous purposes, such as patient simulation, optimization of imaging procedures and dose levels, system evaluation and quality assurance. However, standard 3D printing polymers do not mimic X-ray attenuation properties of tissues like soft, adipose, lung or bone tissue, and standard materials like liquid water. The mass density of printing polymers-especially important in CT-is often inappropriate, i.e., mostly too high. Different methods can be applied to reduce mass density. This work examines reducing density by controlled underfilling either realized by using 3D printing materials expanded through foaming during heating in the printing process, or reducing polymer flow to introduce microscopic air-filled voids. The achievable density reduction depends on the base polymer used. When using foaming materials, density is controlled by the extrusion temperature, and ranges from 33 to 47% of the base polymer used, corresponding to a range of -650 to -394 HU in CT with 120 kV. Standard filaments (Nylon, modified PLA and modified ABS) allowed density reductions by 20 to 25%, covering HU values in CT from -260 to 77 (Nylon), -230 to -20 (ABS) and -81 to 143 (PLA). A standard chalk-filled PLA filament allowed reproduction of bone tissue in a wide range of bone mineral content resulting in CT numbers from 57 to 460 HU. Controlled underfilling allowed the production of radiographic phantom materials with continuously adjustable attenuation in a limited but appropriate range, allowing for the reproduction of X-ray attenuation properties of water, adipose, soft, lung, and bone tissue in an accurate, predictable and reproducible manner.

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Size-specific dose estimations for pediatric chest, abdomen/pelvis and head CT scans with the use of GATE

PURPOSE

The purpose of this study is to create an organ dose database for pediatric individuals undergoing chest, abdomen/pelvis, and head computed tomography (CT) examinations, and to report the differences in absorbed organ doses, when anatomical differences exist for pediatric patients.

METHODS

The GATE Monte Carlo (MC) toolkit was used to model the GE BrightSpeed Elite CT model. The simulated scanner model was validated with the standard Computed Tomography Dose Index (CTDI) head phantom. Twelve computational models (2.1-14 years old) were used. First, contributions to effective dose and absorbed doses per CTDI_vol and per 100 mAs were estimated for all organs. Then, doses per CTDI_vol were correlated with patient model weight for the organs inside the scan range for chest and abdomen/pelvis protocols. Finally, effective doses per dose-length product (DLP) were estimated and compared with the conventional conversion k-factors.

RESULTS

The system was validated against experimental CTDI_w measurements. The doses per CTDI_vol and per 100 mAs for selected organs were estimated. The magnitude of the dependency between the dose and the anatomical characteristics was calculated with the coefficient of determination at 0.5-0.7 for the internal scan organs for chest and abdomen/pelvis protocols. Finally, effective doses per DLP were compared with already published data, showing discrepancies between 13 and 29% and were correlated strongly with the total weight (R² > 0.8) for the chest and abdomen protocols.

CONCLUSIONS

Big differences in absorbed doses are reported even for patients of similar age or same gender, when anatomical differences exist on internal organs of the body.

Toward automated and personalized organ dose determination in CT examinations - A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System

PURPOSE

Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated.

METHODS

Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry.

RESULTS

The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs.

CONCLUSIONS

After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.