Determination and verification of the x-ray spectrum of a CT scanner

3D printing of realistic body phantoms: Comparison of measured and simulated organ doses on the example of a CT scan on a pregnant woman

BACKGROUND

Medical examinations or treatment of pregnant women using ionizing radiation are sometimes unavoidable. In such cases, the risk of harm to the embryo and fetus after exposure to ionizing radiation must be carefully estimated. However, no commercially available anthropomorphic body phantoms of pregnant women are available for dose measurements. A promising possibility for the production of body phantoms for patient groups that are not adequately represented by the phantoms of reference persons is 3D printing. However, this approach is still in the evaluation phase.

PURPOSE

To print the abdomen of a woman in the late stage of pregnancy and compare the dose distribution measured using thermoluminescence dosimeters (TLDs) in the printed phantom for two different computed tomography (CT) protocols with the corresponding results of Monte Carlo simulations on voxel models of the pregnant woman.

MATERIALS AND METHODS

The physical phantom was produced through multi-material extrusion printing using different print materials identified in previous studies to simulate homogeneous soft tissues and the mean compositions of maternal and fetal bones. The 3D printed abdomen was combined with a conventionally produced anthropomorphic female phantom to obtain a whole-body phantom of a pregnant woman. Dose values resulting from two different CT scans acquired at tube voltages of 80 and 120 kV were measured using TLDs positioned in the physical phantom and cross-validated with the results of Monte Carlo simulations performed for two different voxel models. The first was a voxelized model of the produced phantom itself and the second a realistic digital model of a pregnant woman. Representative CT values of the materials used in the printed phantom were determined from the acquired CT images.

RESULTS

The CT values of maternal and fetal tissue structures in the phantom are comparable to CT values of real human tissues. The difference between most organ doses measured in the 3D printed phantom and simulated in the voxel models was below 20% and equivalent within the measurement uncertainties. Only the dose to the fetal head was up to 50% higher and not equivalent for the realistic model and the 80 kV-protocol. As expected, the agreement was better for the voxelized than for the realistic model. For both models a slight energy dependence was observed, with larger deviations for the 80-kV protocol especially for organs located in the pelvic region.

CONCLUSION

Individualized physical body phantoms, such as that of a pregnant woman, can be produced using 3D printing. The good agreement between measured and simulated doses to the fetus cross-validates both dosimetric methods. Therefore, this study demonstrates the suitability of 3D printing phantoms for patients not adequately represented by commercially available body phantoms of reference persons.

[Model construction and software design of computed tomography radiation system based on visualization]

The Monte Carlo N-Particle (MCNP) is often used to calculate the radiation dose during computed tomography (CT) scans. However, the physical calculation process of the model is complicated, the input file structure of the program is complex, and the three-dimensional (3D) display of the geometric model is not supported, so that the researchers cannot establish an accurate CT radiation system model, which affects the accuracy of the dose calculation results. Aiming at these two problems, this study designed a software that visualized CT modeling and automatically generated input files. In terms of model calculation, the theoretical basis was based on the integration of CT modeling improvement schemes of major researchers. For 3D model visualization, LabVIEW was used as the new development platform, constructive solid geometry (CSG) was used as the algorithm principle, and the introduction of editing of MCNP input files was used to visualize CT geometry modeling. Compared with a CT model established by a recent study, the root mean square error between the results simulated by this visual CT modeling software and the actual measurement was smaller. In conclusion, the proposed CT visualization modeling software can not only help researchers to obtain an accurate CT radiation system model, but also provide a new research idea for the geometric modeling visualization method of MCNP.

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

Gold coated contact lens-type ocular in vivo dosimeter (CLOD) for monitoring of low dose in computed tomography: A Monte Carlo study

PURPOSE

This study reports a sensitivity enhancement of gold-coated contact lens-type ocular in vivo dosimeters (CLODs) for low-dose measurements in computed tomography (CT).

METHODS

Monte Carlo (MC) simulations were conducted to evaluate the dose enhancement from the gold (Au) layers on the CLODs. The human eye and CLODs were modeled, and the X-ray tube voltages were defined as 80, 120, and 140 kVp. The thickness of the Au layer attached to a CLOD ranged from 100 nm to 10 μm. The thickness of the active layer ranged from 20 to 140 μm. The dose ratio between the active layer of the Au-coated CLOD and a CLOD without a layer, i.e., the dose enhancement factor (DEF), was calculated.

RESULTS

The DEFs of the first 20-μm thick active layer of the 5-μm thick Au-coated CLOD were 18.4, 19.7, 20.2 at 80, 120, and 140 kVp, respectively. The DEFs decreased as the thickness of the active layer increased. The DEFs of 100-nm to 5-μm thick Au layers increased from 1.7 to 5.4 for 120-kVp X-ray tube voltage when the thickness of the active layer was 140 μm.

CONCLUSIONS

The MC results presented a higher sensitivity of Au-coated CLODs (∼20-times higher than that of CLODs without a gold layer). Au-coated CLODs can be applied to an evaluation of very low doses (a few cGy) delivered to patients during CT imaging.

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study

Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDI_vol range: 54.7–57.5 mGy) employed by the staff of various Radiology/Neuroradiology departments of our institution for routine head examinations. CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. Noise values ranged from 3.5 HU to 5.7 HU across scanners/acquisition protocols. NPS curves differed in terms of peak position (range: 0.21–0.30 mm^-1). A substantial variation of CNR values with scanner/acquisition protocol was observed for different contrast inserts. The coefficient of variation (standard deviation divided by mean value) of CNR values across scanners/acquisition protocols was 18.3%, 31.4%, 34.2%, 30.4% and 30% for teflon, delrin, LDPE, polystyrene and acrylic insert, respectively. An appreciable difference in MTF curves across scanners/acquisition protocols was revealed, with a coefficient of variation of f_50%/f_10% of MTF curves across scanners/acquisition protocols of 10.1%/7.4%. A relevant difference in LCD performance of different scanners/acquisition protocols was found. The range of contrast threshold for a typical object size of 3 mm was 3.7–5.8 HU. Moreover, appreciable differences in terms of NUI values (range: 4.1%-8.3%) were found. The analysis of several quality indices showed a non-negligible variability in head CT imaging capabilities across different scanners/acquisition protocols. This highlights the importance of a physical in-depth characterization of image quality for each CT scanner as clinically used, in order to optimize CT imaging procedures.