Initial investigations of scatter cross-talk in simultaneous biplane high-speed 1000 frames per second neuro-angiography using Monte Carlo simulations.

The Aries photon counting detector (PCD) by Direct Conversion Inc. can image up to 1000 frames per second and is used to track contrast bolus in neuro-vasculature for hemodynamic calculations. For 3D tracking, synchronized biplane imaging with 1 ms acquisition times is used such that both imaging planes are exposed simultaneously. This leads to cross-scattered radiation being detected and a degradation of image quality compared to single-plane imaging. In this study, we utilize Monte Carlo (MC) methods to quantify the increase in scatter due to cross-talk without the use of a radiographic grid. EGSnrc biplane simulations were performed with the Zubal anthropomorphic head phantom. The total scatter plus primary and cross-scatter was calculated in the imaging planes for two orthogonal AP and lateral beams with a field size consistent with the 7.5×5 cm Aries detector, while the primary was determined with a 1×1 mm beam. The forward scatter was then determined from the difference between total and primary. The scatter is seen to increase by 4%-56% for AP projections and 48%-71% for lateral projections depending on detector orientation during simultaneous exposure. Scatter degradation from cross-talk can be reduced using an anti-scatter grid as well as the energy thresholding capabilities of the Aries PCD.

Using a convolutional neural network for human recognition in a staff dose management software for fluoroscopic interventional procedures.

Staff dose management is a continuing concern in fluoroscopically-guided interventional (FGI) procedures. Being unaware of radiation scatter levels can lead to unnecessarily high stochastic and deterministic risks due to the effects of absorbed dose by staff members. Our group has developed a scattered-radiation display system (SDS) capable of monitoring system parameters in real-time using a controller-area network (CAN) bus interface and displaying a color-coded mapping of the Compton-scatter distribution. This system additionally uses a time-of-flight depth sensing camera to track staff member positional information for dose rate updates. The current work capitalizes on our body tracking methodology to facilitate individualized dose recording via human recognition using 16-bit grayscale depth maps acquired using a Microsoft Kinect V2. Background features are removed from the images using a depth threshold technique and connected component analysis, which results in a body silhouette binary mask. The masks are then fed into a convolutional neural network (CNN) for identification of unique body shape features. The CNN was trained using 144 binary masks for each of four individuals (total of 576 images). Initial results indicate high-fidelity prediction (97.3% testing accuracy) from the CNN irrespective of obstructing objects (face masks and lead aprons). Body tracking is still maintained when protective attire is introduced, albeit with a slight increase in positional data error. Dose reports are then able to be produced which contain cumulative dose to each staff member at the eye lens level, waist level, and collar level. Individualized cumulative dose reporting through the use of a CNN in addition to real-time feedback in the clinic will lead to improved radiation dose management.