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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.

High temporal resolution images acquired using 1000fps HSAngio can be used to visualize blood flow patterns and derive flow velocities during neurointerventional procedures. In this work we use this technology to quantify the changes in the blood flow velocities inside a cerebral aneurysm after treatment with three different stents with varying degrees of metal coverage density; stent A

High Speed Angiography (HSA) requires imaging detectors with both high-temporal and high-spatial resolution. Both the Aries and Acteon detectors by Direct Conversion (Stockholm, Sweden) are CdTe direct photon-counting detectors (PCD) that have acquisition frame rates of up to 1000-fps and a 100-micrometer pixel pitch; however, the new Aries detector offers a larger field of view (512 × 768 pixels) compared to the smaller Actaeon detector (256 × 256 pixels). An expanded field of view is required for imaging of larger vasculature, thus the Aries offers this advantage. Evaluations were performed of both detectors under Anti-Coincidence Circuitry (ACC-ON) mode, which corrects for charge sharing between pixels. Initial evaluations of instrumentation noise and detector energy-threshold calibration using Am-241 gamma spectroscopy were performed for the new Aries detector. Linearity was also evaluated for the Aries for each of the 12 individual modules that compose the detector field to check for homogeneity in response to exposure throughout the detector. Finally, Normalized Noise Power Spectrum (NNPS), Modulation Transfer Function (MTF) and Detective Quantum Efficiency (DQE) were then compared between the Aries and Actaeon detectors at two different exposures and detector energy thresholds. The detectors are linear up to approximately 1000 μR and have no instrumentation noise above a threshold of 15 keV. As expected, the MTF’s and DQE’s are similar between the Aries and Actaeon detectors, and there are thus no tradeoff’s in image quality to achieve the larger FOV.

High-speed 1000-fps x-ray Angiography (HSAngio) images can be used to visualize blood-flow patterns and derive flow velocities during neurointerventional procedures. In this work, we present for the very first-time, orthogonal views of contrast injection in an aneurysm model acquired simultaneously using biplane HSAngio imaging. 3-D printed in-vitro models A and B of two different internal carotid-artery aneurysms were connected to a flow loop (circulation fluid: 60% water, 40% glycerol solution, circulation flow rate: 8 L/s). An automatic programmable injector (KD Scientific Legato 110) injected iodine contrast agent at a rate of 88 mL/min for a duration of 3 sec. With an RQA5 spectrum, 1000 fps HSAngio sequences of the contrast injection were acquired simultaneously on the frontal plane using the Actaeon detector (Direct Conversion, Stockholm) and on the lateral plane using the Aries (Direct Conversion, Stockholm) detector. The start of contrast injection and simultaneous biplane x-ray exposures and detector image acquisitions were manually synchronized to capture the initial inflow of contrast into the aneurysm region. For model A the frontal plane images gave a better visualization of the flow streamlines in the parent artery in the inflow (average velocity 28 cm/s) and outflow (average velocity 24 cm/s) region of the aneurysm. The vortices within the aneurysm region especially within the aneurysm dome were better visualized in the lateral plane images (average velocity 27 cm/s). Biplane HSAngio imaging techniques can give more accurate representations of 3-D blood flow within the complex vascular pathology of the human brain, compared to single-plane imaging.

PURPOSE: Contrast dilution gradient (CDG) analysis is a technique used to extract velocimetric 2D information from digitally subtracted angiographic (DSA) acquisitions. This information may then be used by clinicians to quantitatively assess the effects of endovascular treatment on flow conditions surrounding pathologies of interest. The method assumes negligible diffusion conditions, making 1000 fps high speed angiography (HSA), in which diffusion between 1 ms frames may be neglected, a strong candidate for velocimetric analysis using CDG. Previous studies have demonstrated the success of CDG analysis in obtaining velocimetric one-dimensional data at the arterial centerline of simple vasculature. This study seeks to resolve velocity distributions across the entire vessel using 2D-CDG analysis with HSA acquisitions. MATERIALS AND METHODS: HSA acquisitions for this study were obtained in vitro with a benchtop flow loop at 1000 fps using the XC-Actaeon (Direct Conversion Inc.) photon counting detector. 2D-CDG analyses were compared with computational fluid dynamics (CFD) via automatic co-registration of the results from each velocimetry method. This comparison was performed using mean absolute error between pixel values in each method (after temporal averaging). RESULTS: CDG velocity magnitudes were slightly under approximated relative to CFD results (mean velocity: 27 cm/s, mean absolute error: 4.3 cm/s) as a result of incomplete contrast filling. Relative 2D spatial velocity distributions in CDG analysis agreed well with CFD distributions qualitatively. CONCLUSIONS: CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided diffusion is negligible relative to convection in the flow, given a continuous gradient of contrast.

OBJECTIVES: The purpose of this study was to evaluate the effect of placing small lead shields on the temple region of the skull to reduce radiation dose to the lens of the eye during interventional fluoroscopically-guided procedures and cone-beam computed tomography (CBCT) scans of the head. METHODS: EGSnrc Monte-Carlo code was used to determine the eye lens dose reduction when using lateral lead shields for single x-ray projections, CBCT scans with different protocols, and interventional neuroradiology procedures with the Zubal computational head phantom. A clinical C-Arm system was used to take radiographic projections and CBCT scans of anthropomorphic head phantoms without and with lead patches, and the images were compared to assess the effect of the shields. RESULTS: For single lateral projections, a 0.1 (0.3) mm thick lead patch reduced the dose to the left-eye lens by 40% to 60% (55% to 80%) from 45° to 90° RAO and to the right-eye lens by around 30% (55%) from 70° to 90° RAO. For different CBCT protocols, the reduction of lens dose with a 0.3 mm thick lead patch ranged from 20% to 53% at 110 kVp. For CBCT scans of the anthropomorphic phantom, the lead patch introduced streak artifacts that were mainly in the orbital regions but were insignificant in the brain region where most neuro-interventional activity occurs. CONCLUSION: The dose to the patient’s eye lens can be reduced considerably by placing small lead shields over the temple region of the head without substantially compromising image quality in neuro-imaging procedures.

Skin dose is dependent on the incident beam angle and corrections are needed for accurate estimation of the risk of deterministic effects of the skin. Angular-correction factors (ACF) were calculated and incorporated into our skin-dose-tracking system (DTS) and the results compared to Monte-Carlo simulations for a neuro-interventional procedure. To obtain the ACF's, EGSnrc Monte-Carlo (MC) software was used to calculate the dose averaged over 0.5, 1, 2, 3, 4 and 5 mm depth into the entrance surface of a water phantom at the center of the field as a function of incident beam to skin angle from 90-10 degrees for beam field sizes from 5-15 cm and for beam energies from 60-120 kVp. These values were normalized to the incident primary dose to obtain the ACF. The angle of incidence at each mesh vertex in the beam on the surface of the DTS patient graphic was calculated as the complement of the angle between the normal vector and the vector of the intersecting ray from the tube focal spot; skin dose at that vertex was calculated using the corresponding ACF. The skin-dose values with angular correction were compared to those calculated using MC with a matching voxelized phantom. The results show the ACF decreases with decreasing incident angle and skin thickness, and increases with increasing field size and kVp. Good agreement was obtained between the skin dose calculated by the angular-corrected DTS and MC, while use of the ACF allows the real-time performance of the DTS to be maintained.

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.