Machine learning (ML) models were investigated to automatically detect the patient head shift from isocenter and cephalometric landmark locations as a surrogate for head size. Fluoroscopic images of a Kyoto Kagaku anthropomorphic head phantom were taken at various head shifts and magnification modes, to create an image database. One ML model predicts the patient head shift and the other model predicts the coordinates of the anatomical landmarks. The goal is to implement these two separate models into the Dose Tracking System (DTS) developed by our group for eye-lens dose prediction and eliminate the need for manual input by clinical staff.
The patient’s eye-lens dose changes for each projection view during fluoroscopically-guided neuro-interventional procedures. Monte-Carlo (MC) simulation can be done to estimate lens dose but MC cannot be done in real-time to give feedback to the interventionalist. Deep learning (DL) models were investigated to estimate patient-lens dose for given exposure conditions to give real-time updates. MC simulations were done using a Zubal computational phantom to create a dataset of eye-lens dose values for training the DL models. Six geometric parameters (entrance-field size, LAO gantry angulation, patient x, y, z head position relative to the beam isocenter, and whether patient’s right or left eye) were varied for the simulations. The dose for each combination of parameters was expressed as lens dose per entrance air kerma (mGy/Gy). Geometric parameter combinations associated with high-dose values were sampled more finely to generate more high-dose values for training purposes. Additionally, dose at intermediate parameter values was calculated by MC in order to validate the interpolation capabilities of DL. Data was split into training, validation and testing sets. Stacked models and median algorithms were implemented to create more robust models. Model performance was evaluated using mean absolute percentage error (MAPE). The goal for this DL model is that it be implemented into the Dose Tracking System (DTS) developed by our group. This would allow the DTS to infer the patient’s eye-lens dose for real-time feedback and eliminate the need for a large database of pre-calculated values with interpolation capabilities.
The imaging parameters used in neurointerventional procedures were evaluated to better understand the exposure techniques used clinically and their impact on patient dose. All parameters are available on the imaging system’s network bus in real time for each exposure pulse during a procedure. The Canon Dose Tracking System (DTS), which we developed, records the parameters of each exposure event in a raw data log file of controller area network (CAN) packets. We have collected such log files for 120 neurointerventional cases. Parameters are extracted by converting the raw data log file to a reformatted text file using a MATLAB script. The text file is input into a Microsoft Visual Studio project which outputs a new text file, which, with a reference table, allows the parameters to be identified. A written Python script is used to extract the specific parameters that were to be evaluated and output a .csv file. These were then input into MATLAB for analysis. The parameters extracted were the kVp, beam filter type, mAs, and the cranial/caudal angle as well as the RAO/LAO angle for the frontal and lateral gantries for DA and pulsed fluoroscopy (PF) modes. The gantry angles ranged from 34⁰ CRA to 42⁰ CAU and from 114⁰ RAO to 91⁰ LAO for DA and PF, respectively. The median kVp was 84 and 73 and the average per frame mAs was about 11 and 1.8 for DA and PF, respectively. This analysis should allow a better understanding of clinical practice in order to relate technique to patient and staff dose.
The eye lens is a very radiosensitive organ and is at risk for cataractogenesis during neuro-interventional procedures. It is paramount that the lens is exposed to the x-ray beam as little as possible while still being able to complete the clinical task. In this preliminary investigation, a convolutional neural network (CNN) has been created in order identify if the lens is within the x-ray projection image and where it is located with the intent to facilitate lens dose estimation. The model was trained using a database of patient cases of radiographic skull images, which had different views, in order to generalize the data. The size of the dataset was increased by rotating the images at various angles and masks were created for each corresponding image by hand-contouring the eye socket in the image. For image segmentation, a U-Net model was used which consisted of a down-block, bottleneck, and up-block. Different network parameters were tested and receiver operating curves (ROCs), with Jaccard indices, were assessed to identify the best model. The end goal of this project is model implementation into the real-time Canon Dose Tracking System (DTS) during interventional fluoroscopic procedures. This will allow the DTS to have a more accurate identification of where the lens is, whether fully in the beam or only partially. With this information, a more accurate calculation of the eye lens dose can be done which allows for patients’ dose to be more carefully monitored.
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