Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Medical physics (Lancaster), 2021-11, Vol.48 (11), p.6869-6880

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Wiley Online Library

Beschreibungen/Notizen

• Purpose: To experimentally validate the Fastcat cone beam computed tomography (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 gadolinium oxysulfide (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 Monte Carlo (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 s to simulate in Fastcat with 887 and 493 projections, respectively. Conclusions: We present a multienergy 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.