Kaleidoscopes and contradictions : the legitimacy of social media for public relations

If the current discourses of progress are to be believed, the new or social media promise a kaleidoscope of opportunity for connecting and informing citizens. This is by allegedly revitalizing the fading legitimacy and practice of institutions and providing an agent for social interaction. However, as social media adoption has increased, it has revealed a wealth of contradictions both of its own making and reproduction of past action. This has created a crisis for traditional media as well as for public relations. For example, social media such as WikiLeaks have bypassed official channels about government information. In other cases, social media such as Facebook and Twitter informed BBC coverage of the Rio Olympics. Although old media are unlikely to go away, social media have had an impact with several large familybased media companies collapsing or being reintegrated into the new paradigm. To use Walter Lippman’s analogy of the phantom public, the social media contradictorily serve to both disparate the phantom in part and reinforce it...

An investigation into factors affecting electron density calibration for a megavoltage cone-beam CT system

There is a growing interest in the use of megavoltage cone-beam computed tomography (MV CBCT) data for radiotherapy treatment planning. To calculate accurate dose distributions, knowledge of the electron density (ED) of the tissues being irradiated is required. In the case of MV CBCT, it is necessary to determine a calibration-relating CT number to ED, utilizing the photon beam produced for MV CBCT. A number of different parameters can affect this calibration. This study was undertaken on the Siemens MV CBCT system, MVision, to evaluate the effect of the following parameters on the reconstructed CT pixel value to ED calibration: the number of monitor units (MUs) used (5, 8, 15 and 60 MUs), the image reconstruction filter (head and neck, and pelvis), reconstruction matrix size (256 by 256 and 512 by 512), and the addition of extra solid water surrounding the ED phantom. A Gammex electron density CT phantom containing EDs from 0.292 to 1.707 was imaged under each of these conditions. The linear relationship between MV CBCT pixel value and ED was demonstrated for all MU settings and over the range of EDs. Changes in MU number did not dramatically alter the MV CBCT ED calibration. The use of different reconstruction filters was found to affect the MV CBCT ED calibration, as was the addition of solid water surrounding the phantom. Dose distributions from treatment plans calculated with simulated image data from a 15 MU head and neck reconstruction filter MV CBCT image and a MV CBCT ED calibration curve from the image data parameters and a 15 MU pelvis reconstruction filter showed small and clinically insignificant differences. Thus, the use of a single MV CBCT ED calibration curve is unlikely to result in any clinical differences. However, to ensure minimal uncertainties in dose reporting, MV CBCT ED calibration measurements could be carried out using parameter-specific calibration measurements.

An English-translated parallel corpus for the CJK Wikipedia collections

In this paper, we describe a machine-translated parallel English corpus for the NTCIR Chinese, Japanese and Korean (CJK) Wikipedia collections. This document collection is named CJK2E Wikipedia XML corpus. The corpus could be used by the information retrieval research community and knowledge sharing in Wikipedia in many ways; for example, this corpus could be used for experimentations in cross-lingual information retrieval, cross-lingual link discovery, or omni-lingual information retrieval research. Furthermore, the translated CJK articles could be used to further expand the current coverage of the English Wikipedia.

A pre-treatment verification tool for IMRT based on EPID measurements and a GEANT4 back projection application

The aim of this work is to develop software that is capable of back projecting primary fluence images obtained from EPID measurements through phantom and patient geometries in order to calculate 3D dose distributions. In the first instance, we aim to develop a tool for pretreatment verification in IMRT. In our approach, a Geant4 application is used to back project primary fluence values from each EPID pixel towards the source. Each beam is considered to be polyenergetic, with a spectrum obtained from Monte Carlo calculations for the LINAC in question. At each step of the ray tracing process, the energy differential fluence is corrected for attenuation and beam divergence. Subsequently, the TERMA is calculated and accumulated to an energy differential 3D TERMA distribution. This distribution is then convolved with monoenergetic point spread kernels, thus generating energy differential 3D dose distributions. The resulting dose distributions are accumulated to yield the total dose distribution, which can then be used for pre-treatment verification of IMRT plans. Preliminary results were obtained for a test EPID image comprised of 100 9 100 pixels of unity fluence. Back projection of this field into a 30 cm9 30 cm 9 30 cm water phantom was performed, with TERMA distributions obtained in approximately 10 min (running on a single core of a 3 GHz processor). Point spread kernels for monoenergetic photons in water were calculated using a separate Geant4 application. Following convolution and summation, the resulting 3D dose distribution produced familiar build-up and penumbral features. In order to validate the dose model we will use EPID images recorded without any attenuating material in the beam for a number of MLC defined square fields. The dose distributions in water will be calculated and compared to TPS predictions.

Verification of patient position and delivery of IMRT by electronic portal imaging

Background and purpose: The purpose of the work presented in this paper was to determine whether patient positioning and delivery errors could be detected using electronic portal images of intensity modulated radiotherapy (IMRT). Patients and methods: We carried out a series of controlled experiments delivering an IMRT beam to a humanoid phantom using both the dynamic and multiple static field method of delivery. The beams were imaged, the images calibrated to remove the IMRT fluence variation and then compared with calibrated images of the reference beams without any delivery or position errors. The first set of experiments involved translating the position of the phantom both laterally and in a superior/inferior direction a distance of 1, 2, 5 and 10 mm. The phantom was also rotated 1 and 28. For the second set of measurements the phantom position was kept fixed and delivery errors were introduced to the beam. The delivery errors took the form of leaf position and segment intensity errors. Results: The method was able to detect shifts in the phantom position of 1 mm, leaf position errors of 2 mm, and dosimetry errors of 10% on a single segment of a 15 segment IMRT step and shoot delivery (significantly less than 1% of the total dose). Conclusions: The results of this work have shown that the method of imaging the IMRT beam and calibrating the images to remove the intensity modulations could be a useful tool in verifying both the patient position and the delivery of the beam.

The use of electronic portal imaging to verify patient position during intensity-modulated radiotherapy delivered by the dynamic MLC technique

Purpose: The precise shape of the three-dimensional dose distributions created by intensity-modulated radiotherapy means that the verification of patient position and setup is crucial to the outcome of the treatment. In this paper, we investigate and compare the use of two different image calibration procedures that allow extraction of patient anatomy from measured electronic portal images of intensity-modulated treatment beams. Methods and Materials: Electronic portal images of the intensity-modulated treatment beam delivered using the dynamic multileaf collimator technique were acquired. The images were formed by measuring a series of frames or segments throughout the delivery of the beams. The frames were then summed to produce an integrated portal image of the delivered beam. Two different methods for calibrating the integrated image were investigated with the aim of removing the intensity modulations of the beam. The first involved a simple point-by-point division of the integrated image by a single calibration image of the intensity-modulated beam delivered to a homogeneous polymethyl methacrylate (PMMA) phantom. The second calibration method is known as the quadratic calibration method and required a series of calibration images of the intensity-modulated beam delivered to different thicknesses of homogeneous PMMA blocks. Measurements were made using two different detector systems: a Varian amorphous silicon flat-panel imager and a Theraview camera-based system. The methods were tested first using a contrast phantom before images were acquired of intensity-modulated radiotherapy treatment delivered to the prostate and pelvic nodes of cancer patients at the Royal Marsden Hospital. Results: The results indicate that the calibration methods can be used to remove the intensity modulations of the beam, making it possible to see the outlines of bony anatomy that could be used for patient position verification. This was shown for both posterior and lateral delivered fields. Conclusions: Very little difference between the two calibration methods was observed, so the simpler division method, requiring only the single extra calibration measurement and much simpler computation, was the favored method. This new method could provide a complementary tool to existing position verification methods, and it has the advantage that it is completely passive, requiring no further dose to the patient and using only the treatment fields.

Effect of CT electron density identification on Monte Carlo simulations of radiotherapeutic treatments

Introduction: The accurate identification of tissue electron densities is of great importance for Monte Carlo (MC) dose calculations. When converting patient CT data into a voxelised format suitable for MC simulations, however, it is common to simplify the assignment of electron densities so that the complex tissues existing in the human body are categorized into a few basic types. This study examines the effects that the assignment of tissue types and the calculation of densities can have on the results of MC simulations, for the particular case of a Siemen’s Sensation 4 CT scanner located in a radiotherapy centre where QA measurements are routinely made using 11 tissue types (plus air). Methods: DOSXYZnrc phantoms are generated from CT data, using the CTCREATE user code, with the relationship between Hounsfield units (HU) and density determined via linear interpolation between a series of specified points on the ‘CT-density ramp’ (see Figure 1(a)). Tissue types are assigned according to HU ranges. Each voxel in the DOSXYZnrc phantom therefore has an electron density (electrons/cm3) defined by the product of the mass density (from the HU conversion) and the intrinsic electron density (electrons /gram) (from the material assignment), in that voxel. In this study, we consider the problems of density conversion and material identification separately: the CT-density ramp is simplified by decreasing the number of points which define it from 12 down to 8, 3 and 2; and the material-type-assignment is varied by defining the materials which comprise our test phantom (a Supertech head) as two tissues and bone, two plastics and bone, water only and (as an extreme case) lead only. The effect of these parameters on radiological thickness maps derived from simulated portal images is investigated. Results & Discussion: Increasing the degree of simplification of the CT-density ramp results in an increasing effect on the resulting radiological thickness calculated for the Supertech head phantom. For instance, defining the CT-density ramp using 8 points, instead of 12, results in a maximum radiological thickness change of 0.2 cm, whereas defining the CT-density ramp using only 2 points results in a maximum radiological thickness change of 11.2 cm. Changing the definition of the materials comprising the phantom between water and plastic and tissue results in millimetre-scale changes to the resulting radiological thickness. When the entire phantom is defined as lead, this alteration changes the calculated radiological thickness by a maximum of 9.7 cm. Evidently, the simplification of the CT-density ramp has a greater effect on the resulting radiological thickness map than does the alteration of the assignment of tissue types. Conclusions: It is possible to alter the definitions of the tissue types comprising the phantom (or patient) without substantially altering the results of simulated portal images. However, these images are very sensitive to the accurate identification of the HU-density relationship. When converting data from a patient’s CT into a MC simulation phantom, therefore, all possible care should be taken to accurately reproduce the conversion between HU and mass density, for the specific CT scanner used. Acknowledgements: This work is funded by the NHMRC, through a project grant, and supported by the Queensland University of Technology (QUT) and the Royal Brisbane and Women's Hospital (RBWH), Brisbane, Australia. The authors are grateful to the staff of the RBWH, especially Darren Cassidy, for assistance in obtaining the phantom CT data used in this study. The authors also wish to thank Cathy Hargrave, of QUT, for assistance in formatting the CT data, using the Pinnacle TPS. Computational resources and services used in this work were provided by the HPC and Research Support Group, QUT, Brisbane, Australia.

The development of Monte Carlo techniques for the verification of radiotherapy treatments

Introduction: Recent advances in the planning and delivery of radiotherapy treatments have resulted in improvements in the accuracy and precision with which therapeutic radiation can be administered. As the complexity of the treatments increases it becomes more difficult to predict the dose distribution in the patient accurately. Monte Carlo (MC) methods have the potential to improve the accuracy of the dose calculations and are increasingly being recognised as the ‘gold standard’ for predicting dose deposition in the patient [1]. This project has three main aims: 1. To develop tools that enable the transfer of treatment plan information from the treatment planning system (TPS) to a MC dose calculation engine. 2. To develop tools for comparing the 3D dose distributions calculated by the TPS and the MC dose engine. 3. To investigate the radiobiological significance of any errors between the TPS patient dose distribution and the MC dose distribution in terms of Tumour Control Probability (TCP) and Normal Tissue Complication Probabilities (NTCP). The work presented here addresses the first two aims. Methods: (1a) Plan Importing: A database of commissioned accelerator models (Elekta Precise and Varian 2100CD) has been developed for treatment simulations in the MC system (EGSnrc/BEAMnrc). Beam descriptions can be exported from the TPS using the widespread DICOM framework, and the resultant files are parsed with the assistance of a software library (PixelMed Java DICOM Toolkit). The information in these files (such as the monitor units, the jaw positions and gantry orientation) is used to construct a plan-specific accelerator model which allows an accurate simulation of the patient treatment field. (1b) Dose Simulation: The calculation of a dose distribution requires patient CT images which are prepared for the MC simulation using a tool (CTCREATE) packaged with the system. Beam simulation results are converted to absolute dose per- MU using calibration factors recorded during the commissioning process and treatment simulation. These distributions are combined according to the MU meter settings stored in the exported plan to produce an accurate description of the prescribed dose to the patient. (2) Dose Comparison: TPS dose calculations can be obtained using either a DICOM export or by direct retrieval of binary dose files from the file system. Dose difference, gamma evaluation and normalised dose difference algorithms [2] were employed for the comparison of the TPS dose distribution and the MC dose distribution. These implementations are spatial resolution independent and able to interpolate for comparisons. Results and Discussion: The tools successfully produced Monte Carlo input files for a variety of plans exported from the Eclipse (Varian Medical Systems) and Pinnacle (Philips Medical Systems) planning systems: ranging in complexity from a single uniform square field to a five-field step and shoot IMRT treatment. The simulation of collimated beams has been verified geometrically, and validation of dose distributions in a simple body phantom (QUASAR) will follow. The developed dose comparison algorithms have also been tested with controlled dose distribution changes. Conclusion: The capability of the developed code to independently process treatment plans has been demonstrated. A number of limitations exist: only static fields are currently supported (dynamic wedges and dynamic IMRT will require further development), and the process has not been tested for planning systems other than Eclipse and Pinnacle. The tools will be used to independently assess the accuracy of the current treatment planning system dose calculation algorithms for complex treatment deliveries such as IMRT in treatment sites where patient inhomogeneities are expected to be significant. Acknowledgements: Computational resources and services used in this work were provided by the HPC and Research Support Group, Queensland University of Technology, Brisbane, Australia. Pinnacle dose parsing made possible with the help of Paul Reich, North Coast Cancer Institute, North Coast, New South Wales.

Pixel value to electron density conversion for a Megavoltage Cone Beam CT System

Introduction: The motivation for developing megavoltage (and kilovoltage) cone beam CT (MV CBCT) capabilities in the radiotherapy treatment room was primarily based on the need to improve patient set-up accuracy. There has recently been an interest in using the cone beam CT data for treatment planning. Accurate treatment planning, however, requires knowledge of the electron density of the tissues receiving radiation in order to calculate dose distributions. This is obtained from CT, utilising a conversion between CT number and electron density of various tissues. The use of MV CBCT has particular advantages compared to treatment planning with kilovoltage CT in the presence of high atomic number materials and requires the conversion of pixel values from the image sets to electron density. Therefore, a study was undertaken to characterise the pixel value to electron density relationship for the Siemens MV CBCT system, MVision, and determine the effect, if any, of differing the number of monitor units used for acquisition. If a significant difference with number of monitor units was seen then pixel value to ED conversions may be required for each of the clinical settings. The calibration of the MV CT images for electron density offers the possibility for a daily recalculation of the dose distribution and the introduction of new adaptive radiotherapy treatment strategies. Methods: A Gammex Electron Density CT Phantom was imaged with the MVCB CT system. The pixel value for each of the sixteen inserts, which ranged from 0.292 to 1.707 relative electron density to the background solid water, was determined by taking the mean value from within a region of interest centred on the insert, over 5 slices within the centre of the phantom. These results were averaged and plotted against the relative electron densities of each insert with a linear least squares fit was preformed. This procedure was performed for images acquired with 5, 8, 15 and 60 monitor units. Results: The linear relationship between MVCT pixel value and ED was demonstrated for all monitor unit settings and over a range of electron densities. The number of monitor units utilised was found to have no significant impact on this relationship. Discussion: It was found that the number of MU utilised does not significantly alter the pixel value obtained for different ED materials. However, to ensure the most accurate and reproducible MV to ED calibration, one MU setting should be chosen and used routinely. To ensure accuracy for the clinical situation this MU setting should correspond to that which is used clinically. If more than one MU setting is used clinically then an average of the CT values acquired with different numbers of MU could be utilized without loss in accuracy. Conclusions: No significant differences have been shown between the pixel value to ED conversion for the Siemens MV CT cone beam unit with change in monitor units. Thus as single conversion curve could be utilised for MV CT treatment planning. To fully utilise MV CT imaging for radiotherapy treatment planning further work will be undertaken to ensure all corrections have been made and dose calculations verified. These dose calculations may be either for treatment planning purposes or for reconstructing the delivered dose distribution from transit dosimetry measurements made using electronic portal imaging devices. This will potentially allow the cumulative dose distribution to be determined through the patient’s multi-fraction treatment and adaptive treatment strategies developed to optimize the tumour response.

Verification of patient position and delivery of IMRT by electronic Portal Imaging

We have taken a new method of calibrating portal images of IMRT beams and used this to measure patient set-up accuracy and delivery errors, such as leaf errors and segment intensity errors during treatment. A calibration technique was used to remove the intensity modulations from the images leaving equivalent open field images that show patient anatomy that can be used for verification of the patient position. The images of the treatment beam can also be used to verify the delivery of the beam in terms of multileaf collimator leaf position and dosimetric errors. A series of controlled experiments delivering an IMRT anterior beam to the head and neck of a humanoid phantom were undertaken. A 2mm translation in the position of the phantom could be detected. With intentional introduction of delivery errors into the beam this method allowed us to detect leaf positioning errors of 2mm and variation in monitor units of 1%. The method was then applied to the case of a patient who received IMRT treatment to the larynx and cervical nodes. The anterior IMRT beam was imaged during four fractions and the images calibrated and investigated for the characteristic signs of patient position error and delivery error that were shown in the control experiments. No significant errors were seen. The method of imaging the IMRT beam and calibrating the images to remove the intensity modulations can be a useful tool in verifying both the patient position and the delivery of the beam.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

The effect of organ motion of radiotherapy plans

Organ motion as a result of respiration is an important field of research for medical physics. Knowledge of magnitude and direction of this motion is necessary to allow for more accurate radiotherapy treatment planning. This will result in higher doses to the tumour whilst sparing healthy tissue. This project involved human trials, where the radiation therapy patient's kidneys were CT scanned under three different conditions; whilst free breathing (FB), breath-hold at normal tidal inspiration (BHIN), and breath-hold at normal tidal expiration (BHEX). The magnitude of motion was measured by recording the outline of the kidney from a Beam's Eye View (BEV). The centre of mass of this 2D shape was calculated for each set using "ImageJ" software and the magnitude of movement determined from the change in the centroid's coordinates between the BHIN and BHEX scans. The movement ranged from, for the left and right kidneys, 4-46mm and 2-44mm in the superior/inferior (axial) plane, 1-21mm and 2- 16mm in the anterior/posterior (coronal) plane, and 0-6mm and 0-8mm in the lateral/medial (sagittal) plane. From exhale to inhale, the kidneys tended to move inferiorly, anteriorly and laterally. A standard radiotherapy plan, designed to treat the para-aortics with opposed lateral fields was performed on the free breathing (planning) CT set. The field size and arrangement was set up using the same parameters for each subject. The prescription was to deliver 45 Gray in 25 fractions. This field arrangement and prescription was then copied over to the breath hold CT sets, and the dosimetric differences were compared using Dose Volume Histograms (DVH). The point of comparison for the three sets was recorded as the percentage volume of kidney receiving less than or equal to 10 Gray. The QUASAR respiratory motion phantom was used with the range of motion determined from the human study. The phantom was imaged, planned and treated with a linear accelerator with dose determined by film. The effect of the motion was measured by the change in the penumbra of the film and compared to the penumbra from the treatment planning system.

Evaluation of MegaVoltage Cone Beam CT image quality with an unmodified Elekta Precise Linac and EPID : a feasibility study

In order to increase the accuracy of patient positioning for complex radiotherapy treatments various 3D imaging techniques have been developed. MegaVoltage Cone Beam CT (MVCBCT) can utilise existing hardware to implement a 3D imaging modality to aid patient positioning. MVCBCT has been investigated using an unmodified Elekta Precise linac and 15 iView amorphous silicon electronic portal imaging device (EPID). Two methods of delivery and acquisition have been investigated for imaging an anthropomorphic head phantom and quality assurance phantom. Phantom projections were successfully acquired and CT datasets reconstructed using both acquisition methods. Bone, tissue and air were 20 clearly resolvable in both phantoms even with low dose (22 MU) scans. The feasibility of MegaVoltage Cone beam CT was investigated using a standard linac, amorphous silicon EPID and a combination of a free open source reconstruction toolkit as well as custom in-house software written in Matlab. The resultant image quality has 25 been assessed and presented. Although bone, tissue and air were resolvable 2 in all scans, artifacts are present and scan doses are increased when compared with standard portal imaging. The feasibility of MVCBCT with unmodified Elekta Precise linac and EPID has been considered as well as the identification of possible areas for future development in artifact correction techniques to 30 further improve image quality.

Towards quantitative 3D broadband ultrasound characterisation of breast lesions

This thesis describes the development and scientific validation of a real-time quantitative 3D flat-bed ultrasound scanner. Novel short-time Fourier transform software facilitated broadband ultrasound attenuation maps of a breast phantom, enabling detection and identification of both cystic and solid lesions.