arXiv subjects

Jericho O'Connell

Publications and source records attributed to Jericho O'Connell.

4 recordsLinked to original sources

arXiv2021

Experimental validation of Fastcat kV and MV cone beam CT (CBCT) simulator

Purpose: To experimentally validate the Fastcat cone beam CT (CBCT) simulator against kV and MV CBCT images acquired with a Varian Truebeam linac. Methods: kV and MV CBCT images of a Catphan 504 phantom were acquired using a 100 kVp beam with the on-board imager (OBI) and a 6 MV treatment beam with the electronic portal imaging device (EPID), respectively. The kV Fastcat simulation was performed using detailed models of the x-ray source, bowtie filter, a high resolution voxelized virtual Catphan phantom, anti-scatter grid, and the CsI scintillating detector. Likewise, an MV Fastcat CBCT was simulated with detailed models for the beam energy spectrum, flattening filter, a high resolution voxelized virtual Catphan phantom, and the GOS scintillating detector. Experimental and simulated CBCT images of the phantom were compared with respect to HU values, contrast to noise ratio (CNR),and dose linearity. Detector modulation transfer function (MTF) for the two detectors were also experimentally validated. Fastcat's dose calculations were compared to MC dose calculations performed with Topas. Results: For the kV and MV simulations, respectively: Contrast agreed within 14 and 9 HUs and detector MTF agreed within 4.2% and 2.5%. Likewise, CNR had a root mean squared error (RMSE) of 2.6% and 1.4%. Dose agreed within 2.4% and 1.6% of MC values. The kV and MV CBCT images took 71 and 72 seconds to simulate in Fastcat with 887 and 493 projections, respectively. Conclusions: We present a multi energy experimental validation of a fast and accurate CBCT simulator against a commercial linac. The simulator is open source and all models found in this work can be downloaded from https://github.com/jerichooconnell/fastcat.git

physics.med-ph↗

arXiv2021

FastCAT: Fast Cone Beam CT (CBCT) Simulation

The fastCAT application uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5 and 6 MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modelled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modelled, a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modelled. Energy dependent scatter kernels were created in Topas for two 16-cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types. FastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95 % confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. A fastCAT simulation of the Catphan 515 module with an image size of 1024x1024x10 voxels took 61 seconds on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. The fastCAT application can be found at https://github.com/jerichooconnell/fastCATs.git.

physics.med-ph↗

arXiv2019

Unsupervised Learning Methods in X-ray Spectral Imaging Material Segmentation

In this work, we have investigated a number of unsupervised learning methods for material segmentation in projection x-ray imaging with a spectral detector. A phantom containing two hard materials (glass, steel) and three soft materials (PVC, polypropylene, and PFTE) all embedded in PMMA was imaged with a 5 energy bin spectal detector. The projection images were utilized to test nine unsupervised learning algorithms for automated material segmentation. Each algorithm was investigated using single energy (SE), dual energy (DE) and multi energy (ME) images. Clustering results were scored based on homogeneity and completeness of the clusters, which were combined into the Rosenberg and Hirshberg's V-measure. Principle component analysis (PCA), independent component analysis (ICA), and non-negative matrix factorization (NMF) were tested as dimensional reduction methods. ME, DE and SE material segmentation was performed using five, two, and single energy images, respectively. ME had the highest V-measure on the soft materials using PCA and a novel interpolating bayesian gaussian mixture model (BGMM) clustering with a V-measure of 0.71. This was by 3.5% better than DE and 20.3% better than SE. Conversely, SE imaging was most capable of hard tissue segmentation using the standard BGMM, with a V-measures of 0.84. This was 6.3% better than DE and 5.0% better than ME. This work demonstrated that ME x-ray imaging might be superior in segmenting soft tissues compared to conventional SE x-ray imaging.

physics.med-ph↗

arXiv2018

Cyberhubs: Virtual Research Environments for Astronomy

Collaborations in astronomy and astrophysics are faced with numerous cyber infrastructure challenges, such as large data sets, the need to combine heterogeneous data sets, and the challenge to effectively collaborate on those large, heterogeneous data sets with significant processing requirements and complex science software tools. The cyberhubs system is an easy-to-deploy package for small to medium-sized collaborations based on the Jupyter and Docker technology, that allows web-browser enabled, remote, interactive analytic access to shared data. It offers an initial step to address these challenges. The features and deployment steps of the system are described, as well as the requirements collection through an account of the different approaches to data structuring, handling and available analytic tools for the NuGrid and PPMstar collaborations. NuGrid is an international collaboration that creates stellar evolution and explosion physics and nucleosynthesis simulation data. The PPMstar collaboration performs large-scale 3D stellar hydrodynamics simulation of interior convection in the late phases of stellar evolution. Examples of science that is presently performed on cyberhubs, in the areas 3D stellar hydrodynamic simulations, stellar evolution and nucleosynthesis and Galactic chemical evolution, are presented.

astro-ph.IM↗