A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy

Abstract

Cone-beam CT (CBCT) has become the standard image guidance tool for patient setup in image-guided radiation therapy. However, due to its large illumination field, scattered photons severely degrade its image quality. While kernel-based scatter correction methods have been used routinely in the clinic, it is still desirable to develop Monte Carlo (MC) simulation-based methods due to their accuracy. However, the high computational burden of the MC method has prevented routine clinical application. This paper reports our recent development of a practical method of MC-based scatter estimation and removal for CBCT. In contrast with conventional MC approaches that estimate scatter signals using a scatter-contaminated CBCT image, our method used a planning CT image for MC simulation, which has the advantages of accurate image intensity and absence of image truncation. In our method, the planning CT was first rigidly registered with the CBCT. Scatter signals were then estimated via MC simulation. After scatter signals were removed from the raw CBCT projections, a corrected CBCT image was reconstructed. The entire workflow was implemented on a GPU platform for high computational efficiency. Strategies such as projection denoising, CT image downsampling, and interpolation along the angular direction were employed to further enhance the calculation speed. We studied the impact of key parameters in the workflow on the resulting accuracy and efficiency, based on which the optimal parameter values were determined. Our method was evaluated in numerical simulation, phantom, and real patient cases. In the simulation cases, our method reduced mean HU errors from 44 to 3 HU and from 78 to 9 HU in the full-fan and the half-fan cases, respectively. In both the phantom and the patient cases, image artifacts caused by scatter, such as ring artifacts around the bowtie area, were reduced. With all the techniques employed, we achieved computation time of less than 30 s including the time for both the scatter estimation and CBCT reconstruction steps. The efficacy of our method and its high computational efficiency make our method attractive for clinical use.

Figure 1

Work flow of the CBCT scatter correction process with MC simulations. Shaded boxes are key operations, whereas dashed boxes are data sets in this process.

Figure 2

Illustration of minimum projection number analysis for MC simulations.

Figure 3

a)–c) Relationship between f_mea and f_cal for downsampling with an area of 1 × 1 (no downsampling), 4 × 4, and 16 × 16 pixels. c) R value for the linear fitting.

Figure 4

Function form for the clipping function of STR in scatter subtraction.

Figure 5

Scatter estimation error and computation time as a function of photon histories number for (a) full-fan and (b) half-fan cases.

Figure 6

Scatter estimation error and computation time as a function of downsampling times for (a) full-fan and (b) half-fan cases.

Figure 7

Histogram of minimum number of views to yield sufficient accuracy after interpolation for (a) full-fan and (b) half-fan cases.

Figure 8

Scatter estimation error and computation time as a function of view number for (a) full-fan and (b) half-fan cases.

Figure 9

(a) Ground truth image. (b) Registered planning CT image. (c) CBCT before scatter correction. (d) CBCT after scatter correction. Display window [−125, 225] HU. (e) Profiles on the blue line in (a)~(d).

Figure 10

(a) Ground truth image. (b) Registered planning CT image. (c) CBCT before scatter correction. (d) CBCT after scatter correction. Display window [−125, 225] HU. (e) Profiles on the blue line in (a)~(d).

Figure 11

From top to bottom: planning CT image, CBCT image before scatter correction, CBCT image after scatter correction, and profiles along the horizontal line in the first row. The three columns are for pelvis phantom experiment, full-fan clinical case, and half-fan clinical case, respectively. Display windows are [−500, 500] HU, [−300, 300] HU, and [−400, 300] HU for the three columns.

Figure 12

(a)–(c) Three CT images taken at planning, Fx 21, and Fx 43. (d) CBCT image before scatter correction. (e) CBCT image after scatter correction with CT at Fx 21. (f) CBCT image after scatter correction with CT at Fx 43. Display window [−125 225] HU. (g) Profiles on the dash line in the image.

Figure 13

(a) A measured projection image. (b) A Boolean image showing STR>0.8 (white) or STR<0.8 (black). (c) Profile of STR along the dash line in (b). (d) Corrected primary signal along the dash line. (e) CBCT image after scatter correction with hard cutoff mapping. (f) CBCT image after scatter correction with soft cutoff mapping. Display window [−500 500] HU.

Figure 14

(a) Reconstructed image without scatter correction. (b) CBCT reconstructed by the OBI system in Varian TrueBeam. (c) CBCT reconstructed by our proposed method. Display window [−300 300] HU.