Clinical evaluation of soft tissue organ boundary visualization on cone-beam computed tomographic imaging

Abstract

Purpose: Cone-beam computed tomographic images (CBCTs) are increasingly used for setup correction, soft tissue targeting, and image-guided adaptive radiotherapy. However, CBCT image quality is limited by low contrast and imaging artifacts. This analysis investigates the detectability of soft tissue boundaries in CBCT by performing a multiple-observer segmentation study.

Methods and materials: In four prostate cancer patients prostate, bladder and rectum were repeatedly delineated by five observers on CBCTs and fan-beam CTs (FBCTs). A volumetric analysis of contouring variations was performed by calculating coefficients of variation (COV: standard deviation/average volume). The topographical distribution of contouring variations was analyzed using an average surface mesh-based method.

Results: Observer- and patient-averaged COVs for FBCT/CBCT were 0.09/0.19 for prostate, 0.05/0.08 for bladder, and 0.09/0.08 for rectum. Contouring variations on FBCT were significantly smaller than on CBCT for prostate (p < 0.03) and bladder (p < 0.04), but not for rectum (p < 0.37; intermodality differences). Intraobserver variations from repeated contouring of the same image set were not significant for either FBCT or CBCT (p < 0.05). Average standard deviations of individual observers' contour differences from average surface meshes on FBCT vs. CBCT were 1.5 vs. 2.1 mm for prostate, 0.7 vs. 1.4 mm for bladder, and 1.3 vs. 1.5 mm for rectum. The topographical distribution of contouring variations was similar for FBCT and CBCT.

Conclusion: Contouring variations were larger on CBCT than FBCT, except for rectum. Given the well-documented uncertainty in soft tissue contouring in the pelvis, improvement of CBCT image quality and establishment of well-defined soft tissue identification rules are desirable for image-guided radiotherapy.

Figure 1

Cone-beam CT of patient 3 with artifacts caused by moving air in the rectum.

Figure 2

Example of topographical contouring variations on FBCT and CBCT of one patient: Standard deviations of contouring variations are displayed over the average surface meshes. Standard deviations up to the 95 percentile are color-coded in this figure. CBCT: cone-beam computed tomography; FBCT: fan beam computed tomography; Lt: left; Post: posterior; Sup: superior.

Figure 3

Example of inter-observer contouring variation on FBCT and CBCT of the same patient at different levels of the prostate. Each color represents one observer. A large variation is observed at the prostatic apex on FBCT and even more on CBCT. Rectum contouring at this level shows also high uncertainty due to missing contrast from surrounding structures mostly in anterior and posterior direction. Although at the same level of bony anatomy, bladder comes into view at mid prostate level on FBCT. There is a high level of contouring uncertainty at the prostate base where the prostate is in close vicinity with the bladder.

Figure 4

Patient-averaged differences between repeated contouring of the same FBCT and CBCT image sets per observer. Variations were calculated as percentage differences from the first contouring session.

Figure 5

Cumulative distribution function of standard deviations on average organ surface meshes of all mesh points summarized for all patients. Thin lines show standard deviations on FBCTs, thick lines standard deviations on CBCTs.

Figure 5

Cumulative distribution function of standard deviations on average organ surface meshes of all mesh points summarized for all patients. Thin lines show standard deviations on FBCTs, thick lines standard deviations on CBCTs.

Figure 5

Cumulative distribution function of standard deviations on average organ surface meshes of all mesh points summarized for all patients. Thin lines show standard deviations on FBCTs, thick lines standard deviations on CBCTs.