Real-time Detection of Patient Head Position and Cephalometric Landmarks from Neuro-Interventional Procedure Images Using Machine Learning for Patient Eye-Lens Dose Prediction.

A deep learning (DL) model has been developed to estimate patient-lens dose in real-time for given exposure and geometric conditions during fluoroscopically-guided neuro-interventional procedures. Parameters input into the DL model for dose prediction include the patient head shift from isocenter and cephalometric landmark locations as a surrogate for head size. Machine learning (ML) models were investigated to automatically detect these parameters from the in-procedure fluoroscopic image. Fluoroscopic images of a Kyoto Kagaku anthropomorphic head phantom were taken at various known X (transverse) and Y (longitudinal) shifts, as well as different magnification modes, to create an image database. For each image, anatomical landmark coordinate locations were obtained manually using ImageJ and are used as ground-truth labels for training. This database was then used to train the two separate ML models. One ML model predicts the patient head shift in both the X and Y directions and the other model predicts the coordinates of the anatomical landmarks. From the coordinates, the distance between these anatomical landmarks is calculated, and input into the DL dose-prediction model. Model performance was evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE) for the head-shift and landmark-coordinate models, respectively. The goal is to implement these two separate models into the Dose Tracking System (DTS) developed by our group. This would allow the DTS to automatically detect the patient head size and position for eye-lens dose prediction and eliminate the need for manual input by the clinical staff.

Estimation of Patient Eye-Lens Dose During Neuro-Interventional Procedures using a Dense Neural Network (DNN).

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.

Peak skin dose measurements for patients in neuro-interventional procedures using radiochromic film / 中华放射医学与防护杂志

Objective:To determine the peak skin dose (PSD) to patients from neuro-interventional procedures and evaluate the risk of the deterministic effect.Methods:Gafchromic XR RV3 films were used in a level A tertiary hospital in Beijing to measure the patients′ PSD from neuro-interventional procedures, mainly three common types of procedures, including vascular embolization, vascular angioplasty and vascular angiography. The films were scanned by Epson Expression 10000XL, read by ImageJ software, and analyzed by Film QA Pro?2014 software.Results:PSD was measured in 23 embolizations, 14 stentings and 12 arteriography. There were 20 patients whose PSD were equal or greater than 2 Gy, including 15 in vascular embolization and 5 in angioplasty. The PSDs to patients in cerebral arteriography were all below 2 Gy. The PSDs to some of the patients were higher than the threshold for deterministic effect recommended by ICRP Publication 118.Conclusions:There is a risk of deterministic effect in neurointerventional procedures. It is suggested that the patients be followed up to observe their radiation injury as well as to know in time the subsequent diagnosis and treatment.