Using C-Arm X-ray images from marker insertion to confirm the gold fiducial marker identification in an MRI-only prostate radiotherapy workflow

Abstract

Prostate cancer radiotherapy workflows, solely based on magnetic resonance imaging (MRI), are now in clinical use. In these workflows, intraprostatic gold fiducial markers (GFM) show similar signal behavior as calcifications and bleeding in T2-weighted MRI-images. Accurate GFM identification in MRI-only radiotherapy workflows is therefore a major challenge. C-arm X-ray images (CkV-images), acquired at GFM implantation, could provide GFM position information and be used to confirm correct identification in T2-weighted MRI-images. This would require negligible GFM migration between implantation and MRI-imaging. Marker migration was therefore investigated. The aim of this study was to show the feasibility of using CkV-images to confirm GFM identification in an MRI-only prostate radiotherapy workflow. An anterior-posterior digitally reconstructed radiograph (DRR)-image and a mirrored posterior-anterior CkV-image were acquired two weeks apart for 16 patients in an MRI-only radiotherapy workflow. The DRR-image originated from synthetic CT-images (created from MRI-images). A common image geometry was defined between the DRR- and CkV-image for each patient. A rigid registration between the GFM center of mass (CoM) coordinates was performed and the distance between each of the GFM in the DRR- and registered CkV-image was calculated. The same methodology was used to assess GFM migration for 31 patients in a CT-based radiotherapy workflow. The distance calculated was considered a measure of GFM migration. A statistical test was performed to assess any difference between the cohorts. The mean absolute distance difference for the GFM CoM between the DRR- and CkV-image in the MRI-only cohort was 1.7 ± 1.4 mm. The mean GFM migration was 1.2 ± 0.7 mm. No significant difference between the measured total distances of the two cohorts could be detected (P = 0.37). This demonstrated that, a C-Arm X-ray image acquired from the GFM implantation procedure could be used to confirm GFM identification from MRI-images. GFM migration was present but did not constitute a problem.

Keywords: MRI-only prostate; MRI-only radiotherapy; gold fiducial markers; synthetic CT.

Figure 1

C‐arm X‐ray patient positioning. The patient (c) was placed in a lithotomy position during the transperineal ultrasound (f) guided implantation of the GFM. The legs were fixated using a leg support (d). The C‐Arm X‐ray system (a), with the X‐ray detector (e), was placed in a zero degree angle with respect to the patient table (b) to acquire a posterior‐anterior X‐ray for a successful GFM implantation verification.

Figure 2

DRR‐ and CkV‐image. Anterior‐posterior DRR‐image generated from sCT with burned in synthetic markers (a), mirrored posterior‐anterior CkV‐image acquired in connection to GFM implantation (b). The CkV‐image scaling was performed by measuring a horizontal distance from left to right over the pubic symphysis in the DRR‐ and CkV‐image (line in a and b). After the CkV‐image was rescaled to the geometry of the DRR, it was manually registered (translation only) using the GFM as a visual aid and overlaid (c).

Figure 3

Workflow for the proposed method (a) sCTDRR‐ and CkV‐image was acquired, (b) the CkV‐image was rescaled to the image resolution of the sCTDRR‐image, (c) the rescaled CkV‐image was visually and manually overlaid onto the sCTDRR‐image and a rectangular ROI around the GFM was defined and used as an image mask, (d_1‐2) the masked sCTDRR‐image was binarized using a threshold chosen to suppress non‐GFM objects, (e_1‐2) the masked scaled CkV‐image was normalized, inverted and binarized using Otsu's segmentation method,25 (d₃) and (e₃) the 2‐D connected components in the binarized sCTDRR‐ and CkV‐image were identified, a discrimination of the identified connected components was performed and the CoM of the GFM was determined, (f) the GFM CoM coordinates in the sCTDRR‐ and CkV‐image defined two point clouds, (g) a rigid transformation between the point clouds was calculated, (h) the distances between each GFM in the registered point clouds were calculated. The same workflow (a‐h) was applied to CTDRR to assess GFM migration.