MRI Distortion Correction and Robot-to-MRI Scanner Registration for an MRI-Guided Robotic System

Abstract

Magnetic resonance imaging (MRI) guided robotic procedures require safe robotic instrument navigation and precise target localization. This depends on reliable tracking of the instrument from MR images, which requires accurate registration of the robot to the scanner. A novel differential image based robot-to-MRI scanner registration approach is proposed that utilizes a set of active fiducial coils, where background subtraction method is employed for coil detection. In order to use the presented preoperative registration approach jointly with the real-time high speed MRI image acquisition and reconstruction methods in real-time interventional procedures, the effects of the geometric MRI distortion in robot to scanner registration is analyzed using a custom distortion mapping algorithm. The proposed approach is validated by a set of target coils placed within the workspace, employing multi-planar capabilities of the scanner. Registration and validation errors are respectively 2.05 mm and 2.63 mm after the distortion correction showing an improvement of respectively 1.08 mm and 0.14 mm compared to the results without distortion correction.

Keywords: Distortion; magnetic resonance imaging; medical robotics; registration; surgical robotics.

FIGURE 1.

(a) Schematic of the 3D grid phantom used for mapping the MRI distortion field. (b) Schematic of the registration prototype used for the proposed approach. Purple circles indicate the fiducial coils used for registration and validation. (c) Schematic of joint MRI scanner distortion and robot-to-MRI scanner registration method.

FIGURE 2.

The distortion correction process for a single grid. (a) Blue squares show the localized ground truth grid intersections (x_i, y_i). (b) Red circles show the localized grid intersections (${\tilde{x}}_i$, ${\tilde{y}}_i$) in the distorted domain. (c) Shows aligned control points after correction. Any point (${\tilde{x}}_j$, ${\tilde{y}}_j$) in the distorted domain could be transformed with the correction mapping function f (·).

FIGURE 3.

Sequence of morphological operations showing the detection of the control points on the phantom for a distorted MRI image. (a) Original image. (b) Binarization. (c) Closing. (d) Dilation. (e) Skeletonization. (f) Finding branch points. (g) Dilation of branch points. (h) Centroids of the dilated branch points superimposed on the original image.

FIGURE 4.

The coordinate systems defined for the registration process. DCS and PCS are drawn outside the bore for a cleaner visualization.

FIGURE 5.

Flowchart of the proposed registration method.

FIGURE 6.

Foreground (upper left), background (upper right), background subtracted (lower left), and coil detection (lower right) images (coronal orientation) for the same slice.

FIGURE 7.

Active fiducial coil artifacts for consecutive slices in a multi-slice image (coronal orientation). Artifact size changes throughout the slices.

FIGURE 8.

Schematic of the distortion correction phantom used for the proposed approach.

FIGURE 9.

(a) Registration frame prototype used in the experiments. (b) Registration coil. (c) Validation coil.

FIGURE 10.

Experiment setup inside a clinical MRI scanner. The Registration frame prototype is immersed in a phantom filled with distilled water doped with a gadolinium-based contrast agent. Phase array RF coils are placed on top of the prototype.

FIGURE 11.

The mean (a) and maximum (b) distortions of each axis and the overall distortion for each image plane before distortion correction. Image plane position is along z-axis.

FIGURE 12.

The distribution of overall distortion for the image planes located at; (a) z = 18 mm, (b) z = −89 mm.

FIGURE 13.

Shows the planar view of distorted control points overlaid on the detected registration and validation coils for the coronal orientation.