Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data.

Spatial and soft tissue information provided by magnetic resonance imaging can be very valuable during image-guided procedures, where usually only real-time two-dimensional (2D) x-ray images are available. Registration of 2D x-ray images to three-dimensional (3D) magnetic resonance imaging (MRI) data, acquired prior to the procedure, can provide optimal information to guide the procedure. However, registering x-ray images to MRI data is not a trivial task because of their fundamental difference in tissue contrast. This paper presents a technique that generates pseudo-computed tomography (CT) data from multi-spectral MRI acquisitions which is sufficiently similar to real CT data to enable registration of x-ray to MRI with comparable accuracy as registration of x-ray to CT. The method is based on a k-nearest-neighbors (kNN)-regression strategy which labels voxels of MRI data with CT Hounsfield Units. The regression method uses multi-spectral MRI intensities and intensity gradients as features to discriminate between various tissue types. The efficacy of using pseudo-CT data for registration of x-ray to MRI was tested on ex vivo animal data. 2D-3D registration experiments using CT and pseudo-CT data of multiple subjects were performed with a commonly used 2D-3D registration algorithm. On average, the median target registration error for registration of two x-ray images to MRI data was approximately 1 mm larger than for x-ray to CT registration. The authors have shown that pseudo-CT data generated from multi-spectral MRI facilitate registration of MRI to x-ray images. From the experiments it could be concluded that the accuracy achieved was comparable to that of registering x-ray images to CT data.

Analytical guide wire motion algorithm for simulation of endovascular interventions.

Performing minimally invasive vascular interventions requires proper training, as a guide wire needs to be manipulated, by the tail, under fluoroscopic guidance. To provide a training environment, the motion of the guide wire inside the human vasculature can be simulated by computer. Such a simulation needs to be based on an algorithm that is both realistic and fast. To meet these two demands, an analytical solution to the problem of guide wire motion has been derived, using a new parametrisation of guide wire shape. The algorithm is highly generic, is entirely based on elementary physics and has good convergence properties (accuracy of 22 micron after two iterations). In an experimental validation of the algorithm in a planar model, the RMS of the spatial discrepancy between the real and simulated catheter positions was about 10% of the lumen size. Comparison of the simulated guide wire motion with 3D rotational angiography data of a real guide wire advanced in a plastic phantom of the cerebral vasculature showed that the new algorithm produced realistic results.