Radiation protection dosimetry最新文献

The use of portable computed tomography (PCT) in intensive care units (ICU) allows critically ill patients to undergo brain computed tomography examinations without the need for invasive intrahospital transportations. The aim of this study was to determine the radiation doses around a PCT unit to establish routines for radiation protection. Scattered radiation was measured around a Siemens Somatom On.site PCT system during scans of anthropomorphic and computed tomography dose index (CTDI) phantoms. The ambient dose equivalents in the ICU were estimated by summing the dose contributions from all computed tomography scan locations. Results suggest that ~15 scans per week can be performed in an ICU with six to eight patient beds without exceeding the annual effective dose of 0.1 mSv to members of the public, while also maintaining personnel exposure below 1 mSv. These findings support the feasibility of integrating PCT use into the ICU workflow as a safer alternative to intrahospital transport for neuroimaging.

Cone-beam computed tomography (CBCT) is widely used in radiology and radiotherapy, yet accurate dose assessment remains challenging due to wide X-ray beams, nonstandardized dosimetry protocols, and limited beam-collimation options. This study investigated a practical method for obtaining reference Computed Tomography Dose Index (CTDI) measurements on a CBCT system by introducing an external tungsten slit to generate a narrow beam compatible with the adopted dosimetry formalisms. Computed tomography air-kerma index measurements were performed using a 10-cm pencil ionization chamber, and dose-area product (DAP) values were obtained directly from the CBCT unit. The effective beam width was determined from projection images, and the system's geometric behaviour was characterized to support future modelling. A conversion factor between CTDI and DAP was derived for a head phantom, illustrating the feasibility and limitations of applying CTDI methodology to CBCT. The findings provide experimental data and geometric information that may support future Monte Carlo simulations and contribute to more standardized CBCT dose assessment.

This paper outlines the development of the specialist medical physicist role in Sweden. Since introducing certification in 1999, the need for advanced expertise in patient safety, technology, optimization, and regulation became evident. In response, the Swedish Medical Physicist Association and the Swedish Society for Radiation Physics launched a national specialist training program in 2004, further refined in 2010. The program includes registration, clear competency goals, and supervision requirements and is endorsed by most Swedish hospitals. Following the EU Council Directive 2013/59/Euratom, Sweden committed to establishing the Medical Physics Expert role, prompting a proposed legislative amendment mandating specialist training in radiotherapy, radiology and nuclear medicine, and MRI, as this area is highly specialized within the expertise of Swedish medical physicists. This paper is meant to inspire similar initiatives internationally.

Accurate elemental decomposition in dual-energy computed tomography (DECT) is crucial for precision in radiation therapy planning. We present a comparative study of linear regression and fully connected neural networks (FCNNs) for voxel-wise prediction of tissue elemental composition, using synthetic datasets that incorporate realistic intra- and inter-patient variability. Both models performed well under noise-free conditions, with linear regression yielding slightly lower errors. Under noisy conditions, performance degraded for both models, though the linear model generally retained lower numerical error. The FCNNs, however, consistently produced physically plausible (non-negative) elemental mass-fraction estimates. These models are well suited for integration into model-based iterative reconstruction algorithms to support artificial intelligence-driven radiation treatment planning. Future work should incorporate elemental covariances and spatial context to enhance accuracy and clinical utility.

The optimization process is an important part of image-based diagnostics and treatments, requiring collaboration among medical physicists, radiologists, physicians, radiographers, nurses, engineers, and vendors. However, such teamwork can be challenging in some departments. In spring 2024, questionnaires were sent to Swedish medical physicists working with radiology, interventional radiology, or surgery facilities using radiological equipment. The surveys included modality-specific questions about optimization processes. The aim was to explore differences in optimization processes between modalities and departments, identify common challenges, and understand factors facilitating effective optimization. Results showed variations in optimization processes across different modalities, where successful optimization processes were harder to achieve for stationary fluoroscopy systems and mobile fluoroscopy systems than for the other modalities. Common challenges included limited time and lack of knowledge about image quality issues, while close collaboration, continuous meetings with focus on optimization, and good communication were mentioned as important factors for obtaining successful optimization processes.

Digital chest tomosynthesis refers to the 3D reconstruction of low-dose projection images acquired within a limited angular range. The reconstructions have lower depth resolution and are more prone to motion artifacts compared to computed tomography (CT). While recent deep learning approaches aim to reconstruct full-resolution CT volumes from projections, they are computationally demanding due to the high resolution and inherently 3D nature of the task. In this study, we propose a more efficient alternative. Our deep learning-based framework reconstructs sagittal CT slices from small patches of projection data, significantly lowering memory demands. Rather than predicting continuous Houndsfield unit (HU) values, we segment voxels into air, soft tissue, or bone classes. Our results show that the method captures coarse structural features and depth information with high consistency, but struggles to reconstruct fine details. While not yet suitable for clinical deployment, the approach highlights a promising direction for low-resource tomosynthesis-based volumetric imaging.

Dental radiology accounts for 26% of all medical radiological examinations worldwide and 0.2% of the overall collective dose. The most common dental exposure is intraoral bitewing radiography, which is a procedure used to examine the interproximal surfaces of the teeth and is particularly useful for the detection of dental caries and the evaluation of alveolar bone levels. In Sweden, regulations require patients to have a 0.25 mm lead-equivalent thyroid shield during intraoral exposures. The aim of this project is to do a Monte Carlo simulation of the absorbed doses from a bitewing exposure and investigate the radiation protection effect of a lead collar. In this study, a Monte Carlo simulation was done of an intraoral bitewing examination, using the latest International Commission on Radiological Protection (ICRP) human adult mesh phantom. A thyroid collar was added to the mesh phantom to generate the exposure situation of a bitewing exposure. A 60 kV bitewing exposure was simulated using the Monte Carlo software MCNP6.3. The radiation source was simulated using RQR60 with 107 simulated X-ray photons. The simulations were performed both with and without thyroid collar. Absorbed doses were calculated for different organs and tissues in the ICRP adult mesh phantom. The results are calculated for a bitewing exposure with a tube voltage of 60 kV and an exposure time of 0.25 s on a phosphor plate receptor and 0.05 s for the digital sensors. The thyroid protection collar examined in this study did not significantly impact the absorbed dose to the thyroid. However, the absorbed dose to several other organs and tissues was reduced. The absorbed dose reduction was dependent on the distance to the X-ray primary beam, where a greater distance resulted in a greater reduction. The difference in effective dose between use of lead apron and without lead apron is, however, neglectable.

The nonprewhitening matched filter (NPWMF) is frequently used to assess task-based image quality in computed tomography (CT). However, modern reconstruction algorithms, based on iterative reconstruction (IR) or Deep Learning image reconstruction (DLIR), exhibit properties that undermine Fourier domain approaches. One alternative is to abandon the NPWMF. Here, instead, calculation of the NPWMF in the spatial domain is explored with and without assumption of Gaussian observer response. Model observer predictions of area-under-the-curve were determined for a Revolution CT scanner (GE Healthcare) and a NAEOTOM Alpha scanner (Siemens Healthineers). For the former, the vendor's IR and DLIR were investigated. For the latter, the vendor's IR was used and compared to results from a reader study. Results support the conclusion that Fourier domain calculations can exaggerate benefits of denoising and that spatial domain calculations can provide good agreement with human observers. Assumption of Gaussian observer response did not lead to substantial errors.

DOSESTAT-QC® is a stand-alone automated quality control (QC) system used for daily quality assurance of X-ray equipment in Jönköping Region, Sweden. The software has been implemented for all mammography systems and interventional systems in the region. One of the image analysis included in the DOSESTAT-QC® software is performed in homogenous images and focuses on the standard deviations in mean pixel value (MV) and signal-to-noise ratio (SNR) in the images. Initially, the analyses were performed in 1 cm2 regions of interest (ROIs) and the obtained values in each ROI were compared to the corresponding values for the entire image. While MV remained relatively stable over time, fluctuations in SNR together with imprecise localization of pixel errors, especially in the automatic exposure control (AEC) area, highlighted limitations. In this paper, an improved method for image evaluation is presented, which enables precise SNR baseline settings and clear visualization of deviations and dead pixels. Additionally, the adaption and clinical implementation of DOSESTAT-QC® to conventional X-ray systems in the region are described.

The radiology reading room today is busier than ever with increasing numbers of cases that contain more images, sequences, and complex findings. Consequently, radiologists are becoming more fatigued and burned out. To address this growing problem, studies have been conducted to objectively measure fatigue and its impact on diagnostic accuracy and efficiency, with growing evidence that the impact is negative and significant after just 8 h of clinical work. The impact (increased errors, reduced ability to focus) may be greater for residents than for experienced radiologists. Artificial intelligence (AI) may be a potential solution to address fatigue, but we need to understand exactly how it affects users and their decision-making processes to optimally use it as a decision aid in clinical practice.