Calibration of a portal imaging device for high-precision dosimetry: a Monte Carlo study

Today electronic portal imaging devices (EPID's) are used primarily to verify patient positioning. They have, however, also the potential as 2D-dosimeters and could be used as such for transit dosimetry or dose reconstruction. It has been proven that such devices, especially liquid filled ionization chambers, have a stable dose response relationship which can be described in terms of the physical properties of the EPID and the pulsed linac radiation. For absolute dosimetry however, an accurate method of calibration to an absolute dose is needed. In this work, we concentrate on calibration against dose in a homogeneous water phantom. Using a Monte Carlo model of the detector we calculated dose spread kernels in units of absolute dose per incident energy fluence and compared them to calculated dose spread kernels in water at different depths. The energy of the incident pencil beams varied between 0.5 and 18 MeV. At the depth of dose maximum in water for a 6 MV beam (1.5 cm) and for a 18 MV beam (3.0 cm) we observed large absolute differences between water and detector dose above an incident energy of 4 MeV but only small relative differences in the most frequent energy range of the beam energy spectra. It is shown that for a 6 MV beam the absolute reference dose measured at 1.5 cm water depth differs from the absolute detector dose by 3.8%. At depth 1.2 cm in water, however, the relative dose differences are almost constant between 2 and 6 MeV. The effects of changes in the energy spectrum of the beam on the dose responses in water and in the detector are also investigated. We show that differences larger than 2% can occur for different beam qualities of the incident photon beam behind water slabs of different thicknesses. It is therefore concluded that for high-precision dosimetry such effects have to be taken into account. Nevertheless, the precise information about the dose response of the detector provided in this Monte Carlo study forms the basis of extracting directly the basic radiometric quantities photon fluence and photon energy fluence from the detector's signal using a deconvolution algorithm. The results are therefore promising for future application in absolute transit dosimetry and absolute dose reconstruction.

Non-photorealistic rendering of virtual implant models for computer-assisted fluoroscopy-based surgical procedures

Surgical navigation systems visualize the positions and orientations of surgical instruments and implants as graphical overlays onto a medical image of the operated anatomy on a computer monitor. The orthopaedic surgical navigation systems could be categorized according to the image modalities that are used for the visualization of surgical action. In the so-called CT-based systems or 'surgeon-defined anatomy' based systems, where a 3D volume or surface representation of the operated anatomy could be constructed from the preoperatively acquired tomographic data or through intraoperatively digitized anatomy landmarks, a photorealistic rendering of the surgical action has been identified to greatly improve usability of these navigation systems. However, this may not hold true when the virtual representation of surgical instruments and implants is superimposed onto 2D projection images in a fluoroscopy-based navigation system due to the so-called image occlusion problem. Image occlusion occurs when the field of view of the fluoroscopic image is occupied by the virtual representation of surgical implants or instruments. In these situations, the surgeon may miss part of the image details, even if transparency and/or wire-frame rendering is used. In this paper, we propose to use non-photorealistic rendering to overcome this difficulty. Laboratory testing results on foamed plastic bones during various computer-assisted fluoroscopybased surgical procedures including total hip arthroplasty and long bone fracture reduction and osteosynthesis are shown.

Calibration of the HAWC Gamma-Ray Observatory

The High-Altitude Water Cherenkov (HAWC) Experiment is a gamma-ray observatory that utilizes water silos as Cherenkov detectors to measure the electromagnetic air showers created by gamma rays. The experiment consists of an array of closely packed water Cherenkov detectors (WCDs), each with four photomultiplier tubes (PMTs). The direction of the gamma ray will be reconstructed using the times when the electromagnetic shower front triggers PMTs in each WCD. To achieve an angular resolution as low as 0.1 degrees, a laser calibration system will be used to measure relative PMT response times. The system will direct 300ps laser pulses into two fiber-optic networks. Each network will use optical fan-outs and switches to direct light to specific WCDs. The first network is used to measure the light transit time out to each pair of detectors, and the second network sends light to each detector, calibrating the response times of the four PMTs within each detector. As the relative PMT response times are dependent on the number of photons in the light pulse, neutral density filters will be used to control the light intensity across five orders of magnitude. This system will run both continuously in a low-rate mode, and in a high-rate mode with many intensity levels. In this thesis, the design of the calibration system and systematic studies verifying its performance are presented.

Calibration of a surgical microscope with automated zoom lenses using an active optical tracker

BACKGROUND: In this paper, we present a new method for the calibration of a microscope and its registration using an active optical tracker. METHODS: Practically, both operations are done simultaneously by moving an active optical marker within the field of view of the two devices. The IR LEDs composing the marker are first segmented from the microscope images. By knowing their corresponding three-dimensional (3D) position in the optical tracker reference system, it is possible to find the transformation matrix between the referential of the two devices. Registration and calibration parameters can be extracted directly from that transformation. In addition, since the zoom and focus can be modified by the surgeon during the operation, we propose a spline based method to update the camera model to the new setup. RESULTS: The proposed technique is currently being used in an augmented reality system for image-guided surgery in the fields of ear, nose and throat (ENT) and craniomaxillofacial surgeries. CONCLUSIONS: The results have proved to be accurate and the technique is a fast, dynamic and reliable way to calibrate and register the two devices in an OR environment.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.