Robust automatic rigid registration of MRI and X-ray using external fiducial markers for XFM-guided interventional procedures

Abstract

Purpose: In X-ray fused with MRI, previously gathered roadmap MRI volume images are overlaid on live X-ray fluoroscopy images to help guide the clinician during an interventional procedure. The incorporation of MRI data allows for the visualization of soft tissue that is poorly visualized under X-ray. The widespread clinical use of this technique will require fully automating as many components as possible. While previous use of this method has required time-consuming manual intervention to register the two modalities, in this article, the authors present a fully automatic rigid-body registration method.

Methods: External fiducial markers that are visible under these two complimentary imaging modalities were used to register the X-ray images with the roadmap MR images. The method has three components: (a) The identification of the 3D locations of the markers from a full 3D MR volume, (b) the identification of the 3D locations of the markers from a small number of 2D X-ray fluoroscopy images, and (c) finding the rigid-body transformation that registers the two point sets in the two modalities. For part (a), the localization of the markers from MR data, the MR volume image was thresholded, connected voxels were segmented and labeled, and the centroids of the connected components were computed. For part (b), the X-ray projection images, produced by an image intensifier, were first corrected for distortions. Binary mask images of the markers were created from the distortion-corrected X-ray projection images by applying edge detection, pattern recognition, and image morphological operations. The markers were localized in the X-ray frame using an iterative backprojection-based method which segments voxels in the volume of interest, discards false positives based on the previously computed edge-detected projections, and calculates the locations of the true markers as the centroids of the clusters of voxels that remain. For part (c), a variant of the iterative closest point method was used to find correspondences between and register the two sets of points computed from MR and X-ray data. This knowledge of the correspondence between the two point sets was used to refine, first, the X-ray marker localization and then the total rigid-body registration between modalities. The rigid-body registration was used to overlay the roadmap MR image onto the X-ray fluoroscopy projections.

Results: In 35 separate experiments, the markers were correctly registered to each other in 100% of the cases. When half the number of X-ray projections was used (10 X-ray projections instead of 20), the markers were correctly registered in all 35 experiments. The method was also successful in all 35 experiments when the number of markers was (retrospectively) halved (from 16 to 8). The target registration error was computed in a phantom experiment to be less than 2.4 mm. In two in vivo experiments, targets (interventional devices with pointlike metallic structures) inside the heart were successfully registered between the two modalities.

Conclusions: The method presented can be used to automatically register a roadmap MR image to X-ray fluoroscopy using fiducial markers and as few as ten X-ray projections.

Figures

Figure 1

Outline of XFM system.

Figure 2

Bead localization in MR. (a) Coronal MIP and (b) axial MIP. The white x’s denote the locations of the markers found by the MR marker localization algorithm.

Figure 3

Segmenting beads: (a) XF projection image. (b) Corresponding edge mask. (c) Bead mask extracted from edge mask. Dashed circles identify overlapping beads.

Figure 4

Identifying bead corner (a) Direction vs distance. (b) Case I: Good. (c) Case II: Nonellipsoidal. (d) Case HI: Ellipsoidal but not a bead shadow. The thick line represents the segment of the trace that is identified as a bead corner.

Figure 5

Fitting a bead outline to an edge. (a) Edge map. The thick line represents the bead corner that has been identified. (b) The two straight edges adjacent to the bead corner are identified. (c) The predicted bead (dashed line) outline is formed by extending the bead corners and edges in a polygonal manner. (d) The bead outline is formed by adjusting the bead-outline prediction by the closest edge pixels. The closed bead outline is filled to form the bead mask.

Figure 6

XF bead finding iterative algorithm.

Figure 7

Scoring of candidate beads. (a) The template bead outline, denoted by open circles, is overlaid onto the edge mask. (b) The corrected bead outline, denoted by the black dots.

Figure 8

Segmented XF projection with the 3D coordinates of the localized beads (dots) reprojected and overlaid. The dashed lines denote the outlines of the segmented beads found by the method described in Sec. 2C1. Note that the segmentation misidentifies and underidentifies the beads in this particular projection. Despite the inaccurate segmentation, all 16 beads are correctly identified by the full marker localization method.

Figure 9

Registration. (a) The two sets of markers before the ICP is applied. (b) The two sets of correctly registered markers after the application of the ICP.

Figure 10

Refining the segmentation of XF projections. (a) The boundaries of the beads (dashed black lines) that have been computed from the MR volume, overlaid on a MIP of the MR volume. (b) Detail of the corresponding edge mask of the XF projection. (c) Correctly segmented bead outline (solid black line) found using a template from the MR data. Compare to the originally incorrectly segmented bead outline (lighter gray line).

Figure 11

Fused XFM image. The rigid registration parameters found using our method are used to overlay information from the MR data onto the XF projection. The contours, representing tissue boundaries, are extracted from the MR volume image.

Figure 12

Phantom experiment to measure target registration error. (a) Representative X-ray projection. The centroids of the fiducial markers are marked by the white x’s and the targets are marked by black dashed squares. Detail of MIPs of the MR volume (b) along the transverse direction and (c) the coronal direction, showing the two targets (within the dashed white boxes). The white x’s mark the true centroid of the targets in the MR image and the white +’s mark the location of the centroids that have been transformed from the X-ray coordinate system using the registration parameters computed by our automatic method.

Figure 13

In vivo target registration experiment. (a) Target (closure device) in X-ray projection. (b) Target in X-ray projection. (c) Target from (a) transformed into MR coordinates and overlaid on slice. (d) Target from (b) transformed into MR coordinates and overlaid on slice.