Introduction – Procedures related to cardiac catheterization are increasing, resulting in increased radiation exposure for medical staff and the associated health risks. To limit the amount of radiation exposure measures are used. In many cases those measures are not properly used due to the lack of awareness from the medical staff. The primary goal of this research is to integrate a radiation model with a tool, the Azure Kinect, to locate the medical staff and calculate the radiation exposure. The secondary goal is to track the lead shield resulting in the possibility to adjust to the optimal position for the staff while executing the surgery. This study may contribute to real-time dosimetry, the optimal position of both the medical staff and the lead shield, better awareness of radiation exposure, and generally a minimization of the exposure of the medical staff during procedures in the cath lab.
Method – The Azure Kinect DK is used to track the position of the medical staff and, with help of the Aruco markers the lead shield. By using an existing radiation model, the exposure of radiation can be calculated. Firstly, the accuracy of the tracking was ensured. Subsequently, the Azure Kinect’s ability was tested to capture difficult situations. Then, the applicability of the radiation model to the Azure Kinect measurements was tested. Finally, the ability to visualize changes and demonstrate their effect on radiation exposure was examined.
Results – The inaccuracy was higher than the Azure Kinect developers reported due to non-optimal conditions; the accuracy deviation had an absolute value of 6,25%. It was found that the deviation of the coordinates between the different measurement points are moderate, with a few outliers and the y-coordinates decreasing deviation as the cardiologist moves farther away from the phantom. By combining medical staff tracking software with the radiation model, radiation exposure can be calculated. The results show that the software detected a decrease in radiation exposure as the distance towards the source increased and the lead shield is placed closer to the cardiologist.
Discussion and Conclusion – This research aims to improve the awareness of radiation exposure in real-time during a procedure. This software represents a significant improvement over the equipment currently used, such as the Phillips Dose Aware badges and the Dosimetry badges.
Despite the error sensitivity of the Azure Kinect, the combination of the device and software can measure the radiation exposure of the medical staff in simulated procedural scenarios in the cath lab. However, there are some limitations resulting in not yet being applicable during a procedure.
Future research directions should enhance the system to increase the applicability during procedures. Optimization can be achieved by positioning of the Azure Kinect to reduce the change of possible occlusion during a procedure and tracking of the lead shield with the help of AI object detection.
By accomplishing these objectives, the gap can be narrowed between the use of this software in a test environment and its application during a procedure. Ultimately, this will lead to an increased awareness of unnecessary radiation exposure among medical staff during procedures.
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Summary
Introduction
The increasing use of cardiac catheterisation procedures has raised concerns about occupational radiation exposure and its associated health risks for clinicians. Radiation exposure can lead to deterministic and stochastic effects, including cataract and various cancers. To minimise these risks, the ALARA (As Low As Reasonably Achievable) principle must be followed in the clinical setting by applying the available measures. Radiation protection consists of passive and active measures. Passive protection includes architectural shielding, stationary shielding, and personal protective equipment, while active protection focuses on minimising radiation during interventions, employing adjustable lead screens, modifying imaging techniques and provide feedback to improve the usage of these measures. However, current methods for measuring and providing feedback on radiation exposure are insufficient.
The primary goal of this research is to find a comprehensive and improved approach to guidance and implementation of protective measures, to ensure the safety and well-being of healthcare workers who face radiation exposure in their daily practice. An integral component of achieving this goal is to create a model that calculates radiation exposure for clinicians in the cathlab with the use of a lead screen. This research aims to determine the feasibility of using dose rate data from measurements without a lead screen to estimate the dose rate in an environment where a lead screen is employed. Moreover, potential methods for communicating dose rate data to clinicians during a procedure will be explored, aiming to increase their awareness of radiation exposure and encourage the adoption of appropriate protective measures without distracting them from the procedure itself.

Method
Data collection is necessary to construct a model that can predict the radiation scattering. Measurements in the catheterisation laboratory were performed by placing Philips DoseAware Detectors (PDDs) at various locations and heights. Combinations of fluoroscopy or acquisition mode, anteroposterior (AP) view or a 40° tilted view of the C- arm, and the presence or absence of a lead screen, resulted in eight different setups, with each 168 measurements.
In the programming process careful consideration has been given to the ease of refining and improving the model afterwards. Three potential source options for the radiation were considered: the centre of the phantom, a point where 50% of the scattering has occurred, and a 3D representation of the entire phantom. The modelled lead screen together with the source was used to create an attenuation grid, which will be used for the estimation of the effect of the lead screen.
The adequacy of the estimated dose rates with the lead screen was evaluated using boxplots and counting the number of correctly estimated points. Then, the data was interpolated using Kriging. The interpolated data was visualised, and the radiation doses for the assistant and cardiologist were calculated at the chest and head levels.
The exposure calculation was performed for three lead screen positions and a scenario without the lead screen. The placements considered were close to the clinician, close to the phantom, and at the edge of the phantom.
To explore radiation feedback preferences, a literature review and a questionnaire for interventional cardiologists were conducted.

Results
Measurements were taken for eight different setups, each with 168 measurements, including the combinations of fluoroscopy or acquisition mode, anteroposterior (AP) view or a 40° tilted view of the C-arm, and the presence or absence of a lead screen. The interpolated data revealed that the dose rate is higher when the acquisition mode is used, and radiation levels are higher when the C-arm is rotated. Three different source types were incorporated into the measurements, and the accuracy of the estimations was evaluated based on whether they were within a deviation of 0.1 mSv/h from the actual measurements. The 3D source was found to be the most accurate representation of the source in the model. Visualisation of inaccurately estimated values showed that the model overestimated the amount of radiation blocked in certain cases. The radiation exposure for clinicians increased as the lead shield was positioned closer to the radiation source.
A questionnaire was conducted with five interventional cardiologists to investigate their radiation safety practices. The results indicated that radiation safety is deemed important, with an average score of 8/10. The lead screen is an essential shielding component, but its placement varies, and there is uncertainty regarding its optimal positioning. Monthly reports on total radiation dose exposure were provided. The clinicians hardly paid attention to this information, as they believed that any potential harm had already occurred.
The highest-rated techniques were advice on optimal lead screen position (displayed on the lead screen) and a visualisation using augmented reality that displays the scatter pattern on the monitor.

Discussion and Conclusion
This study addresses radiation safety in interventional cardiology by exploring protective measures. A preliminary model was developed to predict radiation exposure based on the location of the lead screen enabling the computation of the radiation dose received by clinicians during a procedure. This dose rate model helps to visualise the impact of lead screen positioning on radiation exposure, which can aid in real-time decision-making during procedures. The model also revealed that placing the lead screen closer to the clinician, rather than closer to the source, could potentially enhance radiation attenuation by a factor of 6.2. This results in a possible dose reduction up to 32.5 times.
The most preferred option by the interventional cardiologists was to display the suggestion of the optimal position of the lead screen directly on the shield. A potential addition to the feedback system could be an augmented reality (AR) visualisation of the radiation effect on the monitor, clearly showing safe areas for personnel. The precise feedback mechanism should be piloted and refined to ensure seamless integration into clinical practice without distracting the clinician from the procedure itself.
Understanding the limitations of the measurements and the model is crucial for optimising its application in practice and the improve the reliability. The accuracy of measurements obtained with the DoseAware badges, while sufficient for this project, could be improved by using more accurate instruments. The phantom used in the study does not accurately represent real patients, and future research should involve patient- specific inputs and more realistic phantoms. One of the key future objectives include collecting more data in different settings and positions of the C-arm, as well as investigating different positions and orientations of the ceiling-mounted lead screen.
The model itself has limitations in its representation of the scattering source and interpolation methods. To improve the model, researchers should account for other sources of scatter, and assess alternative interpolation techniques using more powerful computing resources. Moreover, to calculate the received radiation dose, the model should consider the entire body, accounting for the protection provided by a lead apron and the concept of effective radiation dose.
Accomplishing these objectives will allow the model to play an important role in enhancing cathlab radiation safety, benefiting the health and safety of clinicians. ... Read more

Problem: One of the biggest challenges in hospitals today is improving efficiency, (patient) safety and quality of care while cutting on costs. A current reoccurring challenge in the operation department is the coordination of the components involved in making a surgery successful. One of those components is the set of surgical instruments. They undergo a cyclic process using reprocessing methods during which various challenges arise. A few of these challenges are the complex and time-consuming instrument counts before, during and after surgery.
Research question: Various technological aids have been proposed to automate the instrument counts. Previous technologies all showed their own flaws when they were tested in the operation room (OR). A new research field for the purpose of instrument counting is the use of computer vision. Computer vision shows great promise as it is already widely used to detect and recognize objects in digital images. However, before developing an algorithm to be used specifically for surgical instrument counting in and around the OR, the various activities, working methods and environmental factors are investigated first. This is done using the following research question: "What is the feasibility of using a computer vision algorithm to automatically detect and count surgical instruments and what are potential factors that influence the performance and the implementation in the OR?".

Methods: The research question is answered using a converting thesis structure. Firstly,
the most general steps of the instrument cycle are outlined and a description is given of a SIFT computer vision algorithm. SIFT is the proposed algorithm type for the investigated application. Secondly, the more specific steps of the instrument cycle at the Reinier de Graaf Gasthuis (RdGG) are described. The result of this description are different application options and different design scenarios. Thirdly, one application type and design scenario is selected: instrument counts in the OR. A blueprint is given for testing a SIFT algorithm in the OR. This blueprint could result in numerical results, valuable observations in the OR and staff survey results.
Results: A total of 35 surgeries were attended. Only results from observations and the survey are shown as the algorithm itself was not tested yet. The observations showed factors that could negatively influence the algorithm’s performance. The survey results gave valuable insights into personal opinions on the value, use and implementation of the algorithm. 
Conclusion: The feasibility of a current SIFT algorithm in a current ORs is low as it will not be able to automatically detect and count all instruments. There are a lot of factors that need to be taken into account to improve performance and possible implementation. They are formulated in 5 design focus points: 1) a line of sight between camera and the instrument(s), 2) dealing with instruments being taken away from and added to the table, 3) controlling the light conditions around the instrument table, 4) recognizing the specific type of some instruments and 5) showing clear feedback.
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