Abstract
INTRODUCTION
Over the last twenty years, image-guided interventions have been greatly expanded by the advances in medical imaging and computing power. A key step for any image-guided intervention is to find the image-to-patient transformation matrix, which is the transformation matrix between the preoperative 3D model of patient anatomy and the real position of the patient in the operating room. In this work, we propose a robust registration algorithm to match ultrasound (US) images with preoperative Magnetic Resonance (MR) images of the Humerus.
MATERIALS AND METHODS
The fusion of preoperative MR images with intra-operative US images is performed through an NDI Spectra® Polaris system and a L12-5L60N TELEMED® ultrasound transducer. The use of an ultrasound probe requires a calibration procedure in order to determine the transformation between an US image pixel and its position according to a global reference system.
After the calibration step, the patient anatomy is scanned with US probe. US images are segmented in real time in order to extract the desired bone contour. The use of an optical measurement system together with trackers and the previously-computed calibration matrix makes it possible to assign a world coordinate position to any pixel of the 2D US image. As a result, the set of US pixels extracted from the images results in a cloud of 3D points which will be registered with the 3D Humerus model reconstructed from MR images.
The proposed registration method is composed of two steps. The first step consists of US 3D points cloud alignment with the 3D bone model. Then, the second step performs the widely-known Iterative Closest Point (ICP) algorithm. In order to perform this, we define the coordinate system of both the 3D Humerus model and the US points cloud. The frame directions correspond to the directions of the principal axes of inertia calculated from the matrices of inertia of both the preoperative 3D model and the US data obtained intra-operatively. Then, we compute the rotation matrix to estimate the transformation between the two coordinate systems previously calculated. Finally the translation is determined by evaluating the distance between the mass centres of the two 3D surfaces.
RESULTS
In order to evaluate the performance of this registration method in terms of precision and accuracy, we performed the US/MRI fusion on 8 patients. The evaluation criterion used for the validation step was the fiducial registration error (FRE) estimation based on 8 anatomic fiducials detected on the Humerus of the patient. The mean, standard deviation, minimum and maximum values of the 8 Fiducial Registration Errors were 4.34, 2.20, 2.81 and 9.48 mm, respectively.
DISCUSSIONS
In this work, we propose a robust registration method of MR and US data. Thanks to the optical system, this fusion will allow us for example to guide and assist surgeons in the positioning of the radiofrequency probe for bone tumor ablation. In addition to the fact that it is completely automatic, the proposed image-to-patient registration method is minimally invasive.
