Efficient scatter distribution estimation and correction in CBCT using concurrent Monte Carlo fitting. Issue 1 (16th December 2014)

• Record Type: Journal Article
• Authors: Bootsma, G. J.; Verhaegen, F.; Jaffray, D. A.
• Is Part Of: Medical physics. Volume 42:Issue 1(2015)
• Page Start: 54
• Page End: 68
• Publication Date: 2014-12-16
• Subjects: computerised tomography -- correlation methods -- curve fitting -- error analysis -- fast Fourier transforms -- feature extraction -- image denoising -- image reconstruction -- low‐pass filters -- medical image processing -- Monte Carlo methods -- phantoms -- quality management -- X‐ray scattering; Computed tomography -- Reconstruction -- Noise -- Monte Carlo simulations; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; x‐ray scatter -- cone‐beam CT -- Monte Carlo -- image quality; Monte Carlo methods -- X‐ray scattering -- Photons -- Cone beam computed tomography -- Photon scattering -- Medical image quality -- Interpolation -- Medical image reconstruction -- Image reconstruction; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4903260
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: X‐ray scatter is a significant impediment to image quality improvements in cone‐beam CT (CBCT). The authors present and demonstrate a novel scatter correction algorithm using a scatter estimation method that simultaneously combines multiple Monte Carlo (MC) CBCT simulations through the use of a concurrently evaluated fitting function, referred to as concurrent MC fitting (CMCF). Methods: The CMCF method uses concurrently run MC CBCT scatter projection simulations that are a subset of the projection angles used in the projection set, P, to be corrected. The scattered photons reaching the detector in each MC simulation are simultaneously aggregated by an algorithm which computes the scatter detector response, S MC . S MC is fit to a function, SF, and if the fit of SF is within a specified goodness of fit (GOF), the simulations are terminated. The fit, SF, is then used to interpolate the scatter distribution over all pixel locations for every projection angle in the set P . The CMCF algorithm was tested using a frequency limited sum of sines and cosines as the fitting function on both simulated and measured data. The simulated data consisted of an anthropomorphic head and a pelvis phantom created from CT data, simulated with and without the use of a compensator. The measured data were a pelvis scan of a phantom and patient taken on an Elekta Synergy platform. The simulated data were used to evaluate various GOF metrics as well as determine a suitable fitness value. The simulated data were also used to quantitatively evaluate the image quality improvements provided by the CMCF method. A qualitative analysis was performed on the measured data by comparing the CMCF scatter corrected reconstruction to the original uncorrected and corrected by a constant scatter correction reconstruction, as well as a reconstruction created using a set of projections taken with a small cone angle. Results: Pearson's correlation, r, proved to be a suitable GOF metric with strong correlation with the actual error of the scatter fit, SF . Fitting the scatter distribution to a limited sum of sine and cosine functions using a low‐pass filtered fast Fourier transform provided a computationally efficient and accurate fit. The CMCF algorithm reduces the number of photon histories required by over four orders of magnitude. The simulated experiments showed that using a compensator reduced the computational time by a factor between 1.5 and 1.75. The scatter estimates for the simulated and measured data were computed between 35–93 s and 114–122 s, respectively, using 16 Intel Xeon cores (3.0 GHz). The CMCF scatter correction improved the contrast‐to‐noise ratio by 10%–50% and reduced the reconstruction error to under 3% for the simulated phantoms. Conclusions: The novel CMCF algorithm significantly reduces the computation time required to estimate the scatter distribution by reducing the statistical noise in the MC scatter estimate and limiting the number of projection angles that must be simulated. Using the scatter estimate provided by the CMCF algorithm to correct both simulated and real projection data showed improved reconstruction image quality.