Details

Autor(en) / Beteiligte

• O'Connell, Jericho
• Bazalova‐Carter, Magdalena

Titel

fastCAT: Fast cone beam CT (CBCT) simulation

Ist Teil von

• Medical physics (Lancaster), 2021-08, Vol.48 (8), p.4448-4458

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Wiley Online Library

Beschreibungen/Notizen

• Purpose To develop fastCAT, a fast cone‐beam computed tomography (CBCT) simulator. fastCAT 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. Methods 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 modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled—a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. 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: brain, deflated lung, compact and cortical bone, adipose, and B‐100. Results 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. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024 × 1024 × 10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. Conclusions We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.