Browsing by Subject "Fluoroscopy"

Objective

Methods

Results

Conclusions

AbstractProject 1: Measuring the Effects on Operator Dose of Changing Clinical Settings Purpose: This study was initiated as part of a multi-faceted investigation of occupational dose to Interventional Radiologists consequential to their role as operators of fluoroscopy equipment. This project aims to qualitatively evaluate general dose reduction techniques, including clinical protocol settings on different interventional fluoroscopes to determine the specific impact on operator dose at Duke University Hospital. Materials and Methods: For each unit, analogous baseline settings were selected with a general abdominal protocol. The patient table was set to a source-to-object distance (SOD) of 62.23 cm (24.5 in) and a patient phantom was placed in the beam as a scatter medium similar to a typical patient abdomen. An anthropomorphic “operator” phantom was draped with a lead apron and positioned to one side of the patient table with an ion chamber placed at collar level. The ion chamber was placed such that the center of the active volume was 38.1 cm (15 in) lateral to and 63.5 cm (25 in) inferior from the center of the flat-paneled detector. A series of scans was taken on each unit, with each one having a selected variable changed, and the exposure readings from the ion chamber were recorded for comparison. Results: The effects on operator exposure rate of personnel height, contour shield use, cine mode, magnification, low dose mode, and source-to-image distance (SID) were analyzed. Operator height was found to have a larger effect on exposure rate reduction with distance than anticipated. Use of the contour shield reduced the operator exposure rate by over 90% on each unit. Use of cine mode drastically increased the exposure rate to the operator, while magnification, low dose mode, and decreasing SID all resulted in lower exposure rates. Conclusions: Operators can utilize these results to contextualize the effects of their own dose reduction techniques. Knowledge and familiarity of the techniques which offer the best exposure rate reduction can guide radiation protection practices among staff and help to optimize occupational doses. Project 2: Developing a Conceptual Framework for Analyzing the Radiation Dose Structured Report Purpose: When investigating occupational dose to Interventional Radiologists, it is important to be able to accurately compare metrics related to dose from historical procedures. The Radiation Dose Structured Report (RDSR) provides characteristic data from historical procedures. With an appropriate framework for analyzing RDSR data, performance metrics between operators or units can be compared, and identified trends can be used to develop dose reduction techniques specific to the organization. Materials and Methods: RDSR data from five interventional fluoroscopy systems (K1 – K5) was extracted for a three-year period from July 2019 through August 2022, and multiple metrics of comparison were selected for analysis. To determine differences in machine output, air kerma rates of similar procedures were compared, as well as the overall machine utilization for each year. Differences in operator-selectable variable were compared through air kerma rate per procedure, fluoroscopy time per procedure (limited to central line procedures), and operator caseload makeup. Results: Machine comparison of air kerma rates showed a consistently higher median and variability on the Philips Allura systems compared to the other three units. The Philips AlluraClarity unit in suite K2 was noticeably under-utilized by Interventional Radiology staff due to it being the primary fluoroscope used by Neurosurgery staff who were outside the scope of this investigation. Operator air kerma rates were compared from August 2021 through August 2022 and largely showed similar median values and variability. Fluoroscopy time per procedure fit to lognormal distributions and compared through their distribution parameter μ showed a median value which dipped during the second year for most providers. One operater also had a consistently higher median time per procedure for all three years. Conclusions: The analysis described by this framework provides a means of utilizing RDSR data to compare performance of interventional procedures. Continual local analysis of these metrics can be used to guide operator training to ensure that occupational doses are optimized to be as low as reasonably achievable. This is an initial approach that can be expanded through investigation and further characterization of procedure data included in the RDSR.

Introduction

Methods and results

Conclusions

Target localization precision is crucial for the treatment outcome of radiation therapy. In lung stereotatic body radiation therapy (SBRT), verifying target motion in the real time 2D fluoro images is often used as a vital tool to ensure adequate coverage of the target volume before the treatment delivery starts. However, accurate target localization in 2D fluoroscopy images is very challenging due to the overlapping anatomical structures in the projection images. The localization is often visually performed by physicians and physicists, which is a subjective process that depends on the experience of the clinician. In this paper, we have developed a deep learning network for automatic target localization to improve the efficiency and robustness of the process. Specifically, the deep learning network adopts a Unet architecture with a coarse-to-fine structure. In addition, we innovatively incorporate convolutional Long Short-Term Memory (LSTM) layer into the network to utilize the time correlation between the fluoro images. A Generative Adversarial method was used to train the network to further improve its localization accuracy. A hybrid loss was used to improve the feature learning during the training. The model was tested on a large amount of data generated by the digital X-CAT phantom. Various patient sizes, respiratory amplitudes, and tumor sizes and locations were simulated in the X-CAT phantoms to test the accuracy and robustness of the method. Our model has been proved with great accuracy not only on massive samples but also on specific set of samples. On massive samples, our model achieves IOU 0.92 and centroid of mass difference 0.16 and 0.07 cm in vertical and horizontal direction. On unique set of samples, the IOU is even higher to be 0.98. The centroid of mass difference could be amazingly 0.03 and 0.007 cm. In summary, our results demonstrated the feasibility of using this deep learning network for real target tracking in fluoro images, which will be crucial for target verification before or during lung SBRT treatments.