PURPOSE: Virtual reality and simulation training improve skill acquisition by allowing trainees the opportunity to deliberately practice procedures in a safe environment. The purpose of this study was to find if there was a difference in the amount of improvement the Perk Tutor, an augmented reality training tool, provided depending on the complexity of the procedure. METHODS: We conducted two sets of spinal procedure experiments with different levels of complexity with regards to instrument handling and mental reconstruction – the lumbar puncture and the facet joint injection. In both experiments subjects were randomized into two groups, Control or Perk Tutor. They were guided through a tutorial, given practice attempts with or without Perk Tutor, followed by testing without Perk Tutor augmentation. RESULTS: The Perk Tutor significantly improved trainee outcomes in the facet joint experiment, while the Perk Tutor and the control group performed comparably in the lumbar puncture experiment. CONCLUSION: Perk Tutor and other augmented training systems may be more beneficial for more complex skills that require mental reconstruction of 2-dimensional images or non-palpable anatomy.
OCT angiography (OCTA) has recently garnered immense interest in clinical ophthalmology, permitting ocular vasculature to be viewed in exquisite detail, in vivo, and without the injection of exogenous dyes. However, commercial OCTA systems provide little information about actual erythrocyte speeds; instead, OCTA is typically used to visualize the presence and/or absence of vasculature. This is an important limitation because in many ocular diseases, including diabetic retinopathy (DR) and age-related macular degeneration (AMD), alterations in blood flow, but not necessarily only the presence or absence of vasculature, are thought to be important in understanding pathogenesis. To address this limitation, we have developed an algorithm, variable interscan time analysis (VISTA), which is capable of resolving different erythrocyte speeds. VISTA works by acquiring >2 repeated B-scans, and then computing multiple OCTA signals corresponding to different effective interscan times. The OCTA signals corresponding to different effective interscan times contain independent information about erythrocyte speed. In this study we provide a theoretical overview of VISTA, and investigate the utility of VISTA in studying blood flow alterations in ocular disease. OCTA-VISTA images of eyes with choroidal neovascularization, geographic atrophy, and diabetic retinopathy are presented.
The purpose of this study is to evaluate the suitability of five different anesthetic protocols (isoflurane, isoflurane–xylazine, pentobarbital, ketamine–xylazine, and ketamine–xylazine–vecuronium) for functional blood flow imaging in the rat eye. Total retinal blood flow was measured at a series of time points using an ultrahigh-speed Doppler OCT system. Additionally, each anesthetic protocol was qualitatively evaluated according to the following criteria: (1) time-stability of blood flow, (2) overall rate of blood flow, (3) ocular immobilization, and (4) simplicity. We observed that different anesthetic protocols produced markedly different blood flows. Different anesthetic protocols also varied with respect to the four evaluated criteria. These findings suggest that the choice of anesthetic protocol should be carefully considered when designing and interpreting functional blood flow studies in the rat eye.
Motivation: In prostate brachytherapy, intra-operative dosimetry would be ideal to allow for rapid evaluation of
the implant quality while the patient is still in the treatment position. Such a mechanism, however, requires 3-D
visualization of the currently deposited seeds relative to the prostate. Thus, accurate, robust, and fully-automatic
seed segmentation is of critical importance in achieving intra-operative dosimetry. Methodology: Implanted
brachytherapy seeds are segmented by utilizing a region-based implicit active contour approach. Overlapping
seed clusters are then resolved using a simple yet effective declustering technique. Results: Ground-truth
seed coordinates were obtained via a published segmentation technique. A total of 248 clinical C-arm images
from 16 patients were used to validate the proposed algorithm resulting in a 98.4% automatic detection rate
with a corresponding 2.5% false-positive rate. The overall mean centroid error between the ground-truth and
automatic segmentations was measured to be 0.42 pixels, while the mean centroid error for overlapping seed
clusters alone was measured to be 0.67 pixels. Conclusion: Based on clinical data evaluation and validation,
robust, accurate, and fully-automatic brachytherapy seed segmentation can be achieved through the implicit
active contour framework and subsequent seed declustering method.
Motivation: In prostate brachytherapy, real-time dosimetry would be ideal to allow for rapid evaluation of the implant
quality intra-operatively. However, such a mechanism requires an imaging system that is both real-time and which
provides, via multiple C-arm fluoroscopy images, clear information describing the three-dimensional position of the
seeds deposited within the prostate. Thus, accurate tracking of the C-arm poses proves to be of critical importance to the
process. Methodology: We compute the pose of the C-arm relative to a stationary radiographic fiducial of known
geometry by employing a hybrid registration framework. Firstly, by means of an ellipse segmentation algorithm and a
2D/3D feature based registration, we exploit known FTRAC geometry to recover an initial estimate of the C-arm pose.
Using this estimate, we then initialize the intensity-based registration which serves to recover a refined and accurate
estimation of the C-arm pose. Results: Ground-truth pose was established for each C-arm image through a published and
clinically tested segmentation-based method. Using 169 clinical C-arm images and a ±10° and ±10 mm random
perturbation of the ground-truth pose, the average rotation and translation errors were 0.68° (std = 0.06°) and 0.64 mm
(std = 0.24 mm). Conclusion: Fully automated C-arm pose estimation using a 2D/3D hybrid registration scheme was
found to be clinically robust based on human patient data.