Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter – Part II: System modeling, scatter correction, and optimization. Issue 5 (23rd March 2018)

• Record Type: Journal Article
• Authors: Wang, Adam; Maslowski, Alexander; Messmer, Philippe; Lehmann, Mathias; Strzelecki, Adam; Yu, Elaine; Paysan, Pascal; Brehm, Marcus; Munro, Peter; Star‐Lack, Josh; Seghers, Dieter
• Is Part Of: Medical physics. Volume 45:Issue 5(2018)
• Page Start: 1914
• Page End: 1925
• Publication Date: 2018-03-23
• Subjects: cone‐beam CT -- Monte Carlo -- scatter; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12849
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To correct for scatter in kV cone‐beam CT (CBCT) projection data on a clinical system using a new tool, Acuros ® CTS, that estimates scatter images rapidly and accurately by deterministically solving the linear Boltzmann transport equation. Methods: Phantom and patient CBCT scans were acquired on TrueBeam ® radiotherapy machines. A first‐pass reconstruction was used to create water and bone density maps of the imaged object, which was updated to include a more accurate representation of the patient couch. The imaging system model accounted for the TrueBeam x‐ray source (polychromatic spectrum, beam filtration, bowtie filter, and collimation hardware) and x‐ray detection system (antiscatter grid, flat‐panel imager). Acuros CTS then used the system and object models to estimate the scatter component of each projection image, which was subtracted from the measured projections. The corrected projections were then reconstructed to produce the final result. We examined the tradeoff between run time and accuracy using a Pareto optimization of key parameters, including the voxel size of the down‐sampled object model, the number of pixels in the down‐sampled detector, and the number of scatter images (angular down‐sampling). All computations and reconstructions were performed on a research workstation containing two graphics processing units (GPUs). In addition, we established a method for selecting a subset of projections for which scatter images were calculated. The projections were selected to minimize interpolation errors in the remaining projections. Image quality improvement was assessed by measuring the accuracy of the reconstructed phantom and patient images. Results: The Pareto optimization yielded a set of parameters with an average run time of 26 seconds for scatter correction while maintaining high accuracy of scatter estimation. This was achieved in part by means of optimizing the projection angles that were processed, thus favoring the use of more angles in the lateral (i.e., horizontal) direction and fewer angles in the AP direction. In a 40 cm solid water phantom reconstruction, nonuniformities were decreased from 217 HU without scatter correction to 51 HU with conventional (kernel‐based) scatter correction to 17 HU with Acuros CTS‐based scatter correction. In clinical pelvis scans, nonuniformities in the bladder were reduced from 85 HU with conventional scatter correction to 14 HU with Acuros CTS. Conclusions: Acuros CTS is a promising new tool for fast and accurate scatter correction for CBCT imaging. By carefully modeling the imaging chain and optimizing several parameters, we achieved high correction accuracies with computation times compatible with the clinical workflow. The improvement in image quality enables better soft‐tissue visualization and potentially enables applications such as adaptive radiotherapy.