Three-dimensional to two-dimensional (3D-2D) image registration is key to fusion and simultaneous visualization of valuable information contained in a 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. The accuracy and robustness of any 3D-2D registration method, to be used in a clinical setting, is influenced by: 1) the method itself, 2) uncertainty of initial pose of the 3D image from which registration starts, 3) uncertainty of C-arm’s geometry and pose, and 4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. We proposed a method that solves the 3D-2D registration and C-arm calibration simultaneously based on information from 3D and 2D images. The method was rigorously and objectively validated against a highly accurate reference or “gold standard” registration, performed on clinical image datasets acquired in the context of the endovascular intervention. Because the tested methods perform simultaneous C-arm calibration and 3D-2D registration based solely on anatomical information, they have a high potential for automation and thus for an immediate integration into current interventional workflow. In this way, the advanced techniques for 3D and 2D image fusion and visualization can enter into the clinical environment.
COBISS.SI-ID: 11181396
Several researches have established that the sensitivity of visual assessment of smaller intracranial aneurysms is not satisfactory. Computer–aided diagnosis based on volume rendering of the response of blob enhancement filters may shorten visual inspection and increase detection sensitivity by directing a diagnostician to suspicious locations in cerebral vasculature. We proposed a novel blob enhancement filter based on a modified volume ratio of Hessian eigenvalues that has a more uniform response inside the blob–like structures compared to state-of-the-art filters. Because the response of proposed filter is independent of the size and intensity of structures, it is especially sensitive for detecting small blob–like structures such as aneuryms. We proposed a novel volume rendering method, which is sensitive to signal energy along the viewing ray and which visually enhances the visualization of true positives and suppresses usually sharp false positive responses. The proposed and state-of-the-art methods were quantitatively evaluated on a synthetic dataset and 42 clinical datasets of patients with aneurysms. Because of the capability to accurately enhance the aneurysm’s boundary and due to a low number of visualized false positive responses, the combined use of the proposed filter and visualization method ensures a reliable detection of (small) intracranial aneurysms.
COBISS.SI-ID: 11077716